diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..65043e75cd162dcc698d793a1bb6f7ec9150e712 100644
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diff --git a/README.md b/README.md
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index 0000000000000000000000000000000000000000..994d5ab7f48d4cff61893b3db24f22820ebdcd72
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+---
+tags:
+  - image-segmentation
+  - semantic-segmentation
+  - coral
+  - underwater
+  - pytorch
+  - convnext
+  - mask2former
+license: mit
+language:
+  - zh
+---
+
+# 珊瑚语义分割（Coral Segmentation）
+
+水下珊瑚遥感图像语义分割项目，采用 **Mask2Former-Swin-B + ConvNeXt-L-FPN** 双模型融合方案，将 `256×256` RGB 水下图像分割为四类：
+
+| 类别编号 | 类别名称 | 说明 |
+|---------|---------|------|
+| 0 | 背景 | 非珊瑚区域 |
+| 1 | 活珊瑚 | 健康存活的珊瑚 |
+| 2 | 死珊瑚 | 已死亡的珊瑚 |
+| 3 | 白化珊瑚 | 白化状态的珊瑚 |
+
+## 项目结构
+
+```
+coral-segmentation/
+├── coral_seg/                  # 核心代码与模型
+│   ├── coral_seg/              # Python 包（数据集、模型、损失函数）
+│   ├── configs/                # 训练配置（final_dual.yaml）
+│   ├── tools/                  # 训练、推理、评测、可视化脚本
+│   ├── pretrained/             # 预训练权重
+│   ├── work_dirs/              # 训练好的模型 checkpoint
+│   ├── splits/                 # 训练/验证集划分
+│   ├── vis_results/            # 可视化结果示例
+│   ├── submissions/            # 推理输出（results.zip）
+│   ├── docs/                   # 技术报告与操作指南
+│   └── logs/                   # 训练日志
+├── data_fresh/                 # 原始数据
+│   ├── train_bsdtar/           # 训练数据
+│   └── test_raw/               # 测试数据
+└── README.md
+```
+
+## 模型方案
+
+### ConvNeXt-L-FPN
+
+- **骨干网络**：ConvNeXt-Large（ImageNet-1K 预训练）
+- **解码器**：FPN（Feature Pyramid Network），四尺度特征融合
+- **输入尺寸**：512×512
+- **训练**：120 epochs，AdamW，lr=6e-5，混合精度
+
+### Mask2Former-Swin-B
+
+- **骨干网络**：Swin Transformer Base（ADE20K 预训练）
+- **解码器**：Mask2Former mask classification
+- **输入尺寸**：384×384
+- **训练**：80 epochs，AdamW，lr=3e-5，梯度检查点
+
+### 融合推理
+
+```
+prob = 0.55 × prob_mask2former + 0.45 × prob_convnext
+pred = argmax(prob)
+```
+
+支持 TTA（原图 + 水平翻转）。
+
+## 环境安装
+
+```bash
+pip install -r coral_seg/requirements.txt
+```
+
+主要依赖：
+
+- PyTorch 2.3.0
+- Transformers 4.46.3
+- timm 1.0.27
+- albumentations 2.0.8
+
+## 快速开始
+
+### 1. 检查数据
+
+```bash
+python coral_seg/tools/check_data.py --config coral_seg/configs/final_dual.yaml
+```
+
+### 2. 训练 ConvNeXt-L-FPN
+
+```bash
+python coral_seg/tools/train_convnext.py --config coral_seg/configs/final_dual.yaml
+```
+
+### 3. 训练 Mask2Former-Swin-B
+
+```bash
+python coral_seg/tools/train_mask2former.py --config coral_seg/configs/final_dual.yaml
+```
+
+### 4. 融合推理
+
+```bash
+python coral_seg/tools/infer_ensemble.py \
+  --config coral_seg/configs/final_dual.yaml \
+  --convnext-checkpoint coral_seg/work_dirs/convnext_l_fpn/best.pth \
+  --mask2former-checkpoint coral_seg/work_dirs/mask2former_swin_b/best_hf
+```
+
+### 5. 验证集评测
+
+```bash
+python coral_seg/tools/eval_ensemble_val.py --config coral_seg/configs/final_dual.yaml
+```
+
+## 训练策略
+
+- **损失函数**：CE + Dice + 0.5×Lovasz（ConvNeXt）；Mask2Former 内置组合损失
+- **Label Smoothing**：0.05
+- **学习率调度**：warmup + cosine decay
+- **骨干网络低学习率**：ConvNeXt backbone 使用 0.25× 基础学习率
+- **数据增强**：翻转、旋转、仿射变换、亮度/对比度/饱和度扰动、CLAHE、高斯模糊、运动模糊
+
+## 复现条件
+
+- GPU：A30 24GB（或同等显存）
+- 训练+推理总时间 < 24 小时
+- 固定随机种子（seed: 2026）
+- 固定 train/val split
+
+## 文档
+
+- [操作指南](coral_seg/docs/operation-guide.md) — 完整的部署、训练、推理流程
+- [技术报告](coral_seg/docs/technical-report.md) — 算法设计、模型选择、损失函数等技术细节
diff --git a/coral_seg/README.md b/coral_seg/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..355a8d3cd5c862eb6177eef46efe2ab5363d9473
--- /dev/null
+++ b/coral_seg/README.md
@@ -0,0 +1,106 @@
+# 珊瑚语义分割项目说明
+
+本项目用于完成水下珊瑚遥感图像语义分割任务，目标是将每张 `256x256` RGB 图像分割为四类：
+
+```text
+0 背景
+1 活珊瑚
+2 死珊瑚
+3 白化珊瑚
+```
+
+当前实现采用双模型融合方案：
+
+```text
+Mask2Former-Swin-B + ConvNeXt-L-FPN
+```
+
+其中：
+
+```text
+Mask2Former-Swin-B 负责复杂区域和全局语义建模
+ConvNeXt-L-FPN 负责局部纹理、边界和卷积稳定性
+最终通过概率加权融合生成 results.zip
+```
+
+## 文档索引
+
+完整操作流程，包括环境部署、数据解压、模型下载、训练、推理、评测和压缩提交：
+
+```text
+docs/operation-guide.md
+```
+
+算法与技术说明，包括模型选择、训练策略、损失函数、数据增强、融合推理和 mIoU 计算原理：
+
+```text
+docs/technical-report.md
+```
+
+预训练模型部署详见操作指南第 5 节，当前工程使用的本地路径为：
+
+```text
+coral_seg/pretrained/mask2former-swin-base-ade-semantic/
+coral_seg/pretrained/convnext_large-ea097f82.pth
+```
+
+## 推荐运行目录
+
+当前配置文件中的数据路径、权重路径和输出路径默认指向：
+
+```text
+/root/autodl-tmp/coral/data_fresh
+/root/autodl-tmp/coral/coral_seg
+```
+
+因此推荐从以下目录执行命令：
+
+```bash
+cd /root/autodl-tmp/coral
+```
+
+## 快速训练与推理
+
+下载或检查预训练模型：
+
+```bash
+python coral_seg/tools/download_pretrained.py --config coral_seg/configs/final_dual.yaml
+```
+
+检查数据：
+
+```bash
+python coral_seg/tools/check_data.py --config coral_seg/configs/final_dual.yaml
+```
+
+训练 ConvNeXt-L-FPN：
+
+```bash
+mkdir -p coral_seg/logs
+python coral_seg/tools/train_convnext.py \
+  --config coral_seg/configs/final_dual.yaml \
+  2>&1 | tee coral_seg/logs/convnext_train.log
+```
+
+训练 Mask2Former-Swin-B：
+
+```bash
+python coral_seg/tools/train_mask2former.py \
+  --config coral_seg/configs/final_dual.yaml \
+  2>&1 | tee coral_seg/logs/mask2former_train.log
+```
+
+融合推理并生成提交文件：
+
+```bash
+python coral_seg/tools/infer_ensemble.py \
+  --config coral_seg/configs/final_dual.yaml \
+  --convnext-checkpoint coral_seg/work_dirs/convnext_l_fpn/best.pth \
+  --mask2former-checkpoint coral_seg/work_dirs/mask2former_swin_b/best_hf
+```
+
+最终提交文件：
+
+```text
+/root/autodl-tmp/coral/coral_seg/submissions/results.zip
+```
diff --git a/coral_seg/configs/final_dual.yaml b/coral_seg/configs/final_dual.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..26ef025bd97c73586a5d9a80378a4057c6865605
--- /dev/null
+++ b/coral_seg/configs/final_dual.yaml
@@ -0,0 +1,58 @@
+seed: 2026
+num_classes: 4
+class_names:
+  - background
+  - live_coral
+  - dead_coral
+  - bleached_coral
+
+data:
+  train_images: /root/autodl-tmp/coral/data_fresh/train_bsdtar/train/image
+  train_masks: /root/autodl-tmp/coral/data_fresh/train_bsdtar/train/label
+  test_images: /root/autodl-tmp/coral/data_fresh/test_raw/PrecisionLabel800/images
+  split_dir: /root/autodl-tmp/coral/coral_seg/splits
+  val_ratio: 0.15
+
+train:
+  num_workers: 8
+  amp: true
+  grad_clip_norm: 1.0
+  print_freq: 50
+
+convnext:
+  image_size: 512
+  batch_size: 4
+  epochs: 120
+  lr: 0.00006
+  backbone_lr_mult: 0.25
+  weight_decay: 0.01
+  warmup_epochs: 5
+  pretrained: true
+  pretrained_path: /root/autodl-tmp/coral/coral_seg/pretrained/convnext_large-ea097f82.pth
+  work_dir: /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn
+  loss:
+    ce: 1.0
+    dice: 1.0
+    lovasz: 0.5
+    label_smoothing: 0.05
+
+mask2former:
+  image_size: 384
+  batch_size: 2
+  epochs: 80
+  lr: 0.00003
+  weight_decay: 0.05
+  warmup_epochs: 5
+  pretrained_name: facebook/mask2former-swin-base-ade-semantic
+  pretrained_dir: /root/autodl-tmp/coral/coral_seg/pretrained/mask2former-swin-base-ade-semantic
+  gradient_checkpointing: true
+  work_dir: /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b
+
+infer:
+  convnext_weight: 0.45
+  mask2former_weight: 0.55
+  convnext_sizes: [512]
+  mask2former_sizes: [384]
+  tta_flips: [none, h]
+  out_dir: /root/autodl-tmp/coral/coral_seg/submissions/results
+  zip_path: /root/autodl-tmp/coral/coral_seg/submissions/results.zip
diff --git a/coral_seg/coral_seg/__init__.py b/coral_seg/coral_seg/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..13cb7dfb7e6a0379080069f2878e27a0bcd94b50
--- /dev/null
+++ b/coral_seg/coral_seg/__init__.py
@@ -0,0 +1,2 @@
+"""Coral reef semantic segmentation training package."""
+
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diff --git a/coral_seg/coral_seg/dataset.py b/coral_seg/coral_seg/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9ddfd4ee1fb95b80d9392e603a505c033b01e68
--- /dev/null
+++ b/coral_seg/coral_seg/dataset.py
@@ -0,0 +1,144 @@
+from pathlib import Path
+from typing import Dict, List, Optional
+
+import albumentations as A
+import cv2
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+from .splits import read_ids
+from .utils import IMAGENET_MEAN, IMAGENET_STD, list_pngs, read_image, read_mask
+
+
+def build_train_transform(size: int) -> A.Compose:
+    return A.Compose(
+        [
+            A.HorizontalFlip(p=0.5),
+            A.VerticalFlip(p=0.5),
+            A.RandomRotate90(p=0.5),
+            A.Affine(
+                scale=(0.75, 1.25),
+                translate_percent=(-0.06, 0.06),
+                rotate=(-25, 25),
+                shear=(-5, 5),
+                interpolation=cv2.INTER_LINEAR,
+                mask_interpolation=cv2.INTER_NEAREST,
+                border_mode=cv2.BORDER_REFLECT_101,
+                p=0.65,
+            ),
+            A.OneOf(
+                [
+                    A.RandomBrightnessContrast(brightness_limit=0.25, contrast_limit=0.25, p=1.0),
+                    A.HueSaturationValue(hue_shift_limit=8, sat_shift_limit=18, val_shift_limit=18, p=1.0),
+                    A.RandomGamma(gamma_limit=(75, 130), p=1.0),
+                    A.CLAHE(clip_limit=(1, 3), tile_grid_size=(8, 8), p=1.0),
+                ],
+                p=0.85,
+            ),
+            A.OneOf(
+                [
+                    A.GaussianBlur(blur_limit=(3, 5), p=1.0),
+                    A.MotionBlur(blur_limit=3, p=1.0),
+                ],
+                p=0.20,
+            ),
+            A.Resize(size, size, interpolation=cv2.INTER_LINEAR, mask_interpolation=cv2.INTER_NEAREST),
+            A.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
+        ]
+    )
+
+
+def build_val_transform(size: int) -> A.Compose:
+    return A.Compose(
+        [
+            A.Resize(size, size, interpolation=cv2.INTER_LINEAR, mask_interpolation=cv2.INTER_NEAREST),
+            A.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
+        ]
+    )
+
+
+class CoralSegDataset(Dataset):
+    def __init__(
+        self,
+        image_dir: str,
+        mask_dir: Optional[str] = None,
+        split_file: Optional[str] = None,
+        transform: Optional[A.Compose] = None,
+    ) -> None:
+        self.image_dir = Path(image_dir)
+        self.mask_dir = Path(mask_dir) if mask_dir else None
+        self.transform = transform
+
+        if split_file:
+            ids = read_ids(split_file)
+            self.image_paths = [self.image_dir / f"{sample_id}.png" for sample_id in ids]
+        else:
+            self.image_paths = list_pngs(self.image_dir)
+
+        if not self.image_paths:
+            raise RuntimeError(f"No PNG images found in {self.image_dir}")
+
+    def __len__(self) -> int:
+        return len(self.image_paths)
+
+    def __getitem__(self, idx: int) -> Dict[str, torch.Tensor | str]:
+        image_path = self.image_paths[idx]
+        image = read_image(image_path)
+        mask = None
+        if self.mask_dir is not None:
+            mask_path = self.mask_dir / image_path.name
+            mask = read_mask(mask_path)
+
+        if self.transform is not None:
+            if mask is None:
+                transformed = self.transform(image=image)
+                image = transformed["image"]
+            else:
+                transformed = self.transform(image=image, mask=mask)
+                image, mask = transformed["image"], transformed["mask"]
+
+        image_tensor = torch.from_numpy(image.transpose(2, 0, 1)).float()
+        sample: Dict[str, torch.Tensor | str] = {
+            "image": image_tensor,
+            "name": image_path.name,
+        }
+        if mask is not None:
+            sample["mask"] = torch.from_numpy(mask.astype(np.int64))
+        return sample
+
+
+def segmentation_collate(batch: List[Dict[str, torch.Tensor | str]]) -> Dict[str, torch.Tensor | List[str]]:
+    images = torch.stack([item["image"] for item in batch])  # type: ignore[arg-type]
+    names = [str(item["name"]) for item in batch]
+    result: Dict[str, torch.Tensor | List[str]] = {"image": images, "name": names}
+    if "mask" in batch[0]:
+        result["mask"] = torch.stack([item["mask"] for item in batch])  # type: ignore[arg-type]
+    return result
+
+
+def mask2former_collate(
+    batch: List[Dict[str, torch.Tensor | str]],
+    num_classes: int,
+) -> Dict[str, torch.Tensor | List[torch.Tensor] | List[str]]:
+    images = torch.stack([item["image"] for item in batch])  # type: ignore[arg-type]
+    names = [str(item["name"]) for item in batch]
+    result: Dict[str, torch.Tensor | List[torch.Tensor] | List[str]] = {"image": images, "name": names}
+    if "mask" not in batch[0]:
+        return result
+
+    masks = [item["mask"].long() for item in batch]  # type: ignore[union-attr]
+    mask_labels: List[torch.Tensor] = []
+    class_labels: List[torch.Tensor] = []
+    for mask in masks:
+        labels = torch.unique(mask)
+        labels = labels[(labels >= 0) & (labels < num_classes)]
+        labels = labels.sort()[0]
+        binary_masks = torch.stack([(mask == cls).float() for cls in labels], dim=0)
+        mask_labels.append(binary_masks)
+        class_labels.append(labels.long())
+
+    result["raw_mask"] = torch.stack(masks)
+    result["mask_labels"] = mask_labels
+    result["class_labels"] = class_labels
+    return result
diff --git a/coral_seg/coral_seg/losses.py b/coral_seg/coral_seg/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4ca02aa85521c6b60f0cc4abc72dd8e2a60c0e5
--- /dev/null
+++ b/coral_seg/coral_seg/losses.py
@@ -0,0 +1,101 @@
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class DiceLoss(nn.Module):
+    def __init__(self, num_classes: int, smooth: float = 1.0, ignore_index: int = 255) -> None:
+        super().__init__()
+        self.num_classes = num_classes
+        self.smooth = smooth
+        self.ignore_index = ignore_index
+
+    def forward(self, logits: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+        probs = torch.softmax(logits, dim=1)
+        valid = target != self.ignore_index
+        target_safe = target.clamp(0, self.num_classes - 1)
+        one_hot = F.one_hot(target_safe, self.num_classes).permute(0, 3, 1, 2).float()
+        valid = valid.unsqueeze(1).float()
+        probs = probs * valid
+        one_hot = one_hot * valid
+        dims = (0, 2, 3)
+        intersection = torch.sum(probs * one_hot, dims)
+        cardinality = torch.sum(probs + one_hot, dims)
+        dice = (2.0 * intersection + self.smooth) / (cardinality + self.smooth)
+        return 1.0 - dice.mean()
+
+
+def lovasz_grad(gt_sorted: torch.Tensor) -> torch.Tensor:
+    gts = gt_sorted.sum()
+    intersection = gts - gt_sorted.float().cumsum(0)
+    union = gts + (1 - gt_sorted).float().cumsum(0)
+    jaccard = 1.0 - intersection / union.clamp_min(1e-6)
+    if gt_sorted.numel() > 1:
+        jaccard[1:] = jaccard[1:] - jaccard[:-1]
+    return jaccard
+
+
+def lovasz_softmax_flat(probs: torch.Tensor, labels: torch.Tensor, num_classes: int) -> torch.Tensor:
+    losses = []
+    for cls in range(num_classes):
+        fg = (labels == cls).float()
+        if fg.sum() == 0:
+            continue
+        class_pred = probs[:, cls]
+        errors = (fg - class_pred).abs()
+        errors_sorted, perm = torch.sort(errors, descending=True)
+        fg_sorted = fg[perm]
+        losses.append(torch.dot(errors_sorted, lovasz_grad(fg_sorted)))
+    if not losses:
+        return probs.sum() * 0.0
+    return torch.stack(losses).mean()
+
+
+class LovaszSoftmaxLoss(nn.Module):
+    def __init__(self, num_classes: int, ignore_index: int = 255) -> None:
+        super().__init__()
+        self.num_classes = num_classes
+        self.ignore_index = ignore_index
+
+    def forward(self, logits: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
+        probs = torch.softmax(logits, dim=1).permute(0, 2, 3, 1).reshape(-1, self.num_classes)
+        labels = target.reshape(-1)
+        valid = labels != self.ignore_index
+        if valid.sum() == 0:
+            return logits.sum() * 0.0
+        return lovasz_softmax_flat(probs[valid], labels[valid], self.num_classes)
+
+
+class ComboSegLoss(nn.Module):
+    def __init__(
+        self,
+        num_classes: int,
+        ce_weight: float = 1.0,
+        dice_weight: float = 1.0,
+        lovasz_weight: float = 0.5,
+        label_smoothing: float = 0.05,
+        ignore_index: int = 255,
+    ) -> None:
+        super().__init__()
+        self.ce_weight = ce_weight
+        self.dice_weight = dice_weight
+        self.lovasz_weight = lovasz_weight
+        self.label_smoothing = label_smoothing
+        self.ignore_index = ignore_index
+        self.dice = DiceLoss(num_classes, ignore_index=ignore_index)
+        self.lovasz = LovaszSoftmaxLoss(num_classes, ignore_index=ignore_index)
+
+    def forward(self, logits: torch.Tensor, target: torch.Tensor, epoch: Optional[int] = None) -> torch.Tensor:
+        ce = F.cross_entropy(
+            logits,
+            target,
+            ignore_index=self.ignore_index,
+            label_smoothing=self.label_smoothing,
+        )
+        dice = self.dice(logits, target)
+        loss = self.ce_weight * ce + self.dice_weight * dice
+        if self.lovasz_weight > 0:
+            loss = loss + self.lovasz_weight * self.lovasz(logits, target)
+        return loss
diff --git a/coral_seg/coral_seg/model_convnext_fpn.py b/coral_seg/coral_seg/model_convnext_fpn.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa79c07db95948f2078e61cfb1d117d3f66e114d
--- /dev/null
+++ b/coral_seg/coral_seg/model_convnext_fpn.py
@@ -0,0 +1,82 @@
+from pathlib import Path
+from typing import List
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision.models import ConvNeXt_Large_Weights, convnext_large
+
+
+class ConvBNAct(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3, dropout: float = 0.0) -> None:
+        super().__init__()
+        padding = kernel_size // 2
+        layers: List[nn.Module] = [
+            nn.Conv2d(in_channels, out_channels, kernel_size, padding=padding, bias=False),
+            nn.BatchNorm2d(out_channels),
+            nn.GELU(),
+        ]
+        if dropout > 0:
+            layers.append(nn.Dropout2d(dropout))
+        self.block = nn.Sequential(*layers)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.block(x)
+
+
+class ConvNeXtFPN(nn.Module):
+    def __init__(
+        self,
+        num_classes: int = 4,
+        fpn_channels: int = 256,
+        pretrained: bool = True,
+        pretrained_path: str | None = None,
+        dropout: float = 0.1,
+    ) -> None:
+        super().__init__()
+        if pretrained and pretrained_path and Path(pretrained_path).is_file():
+            backbone = convnext_large(weights=None)
+            state = torch.load(pretrained_path, map_location="cpu")
+            if isinstance(state, dict) and "model" in state:
+                state = state["model"]
+            backbone.load_state_dict(state, strict=True)
+        else:
+            weights = ConvNeXt_Large_Weights.IMAGENET1K_V1 if pretrained else None
+            backbone = convnext_large(weights=weights)
+        self.features = backbone.features
+        in_channels = [192, 384, 768, 1536]
+        self.lateral = nn.ModuleList([nn.Conv2d(c, fpn_channels, 1) for c in in_channels])
+        self.smooth = nn.ModuleList([ConvBNAct(fpn_channels, fpn_channels, 3) for _ in in_channels])
+        self.decoder = nn.Sequential(
+            ConvBNAct(fpn_channels * 4, fpn_channels, 3, dropout=dropout),
+            ConvBNAct(fpn_channels, fpn_channels, 3, dropout=dropout),
+            nn.Conv2d(fpn_channels, num_classes, 1),
+        )
+
+    def forward_features(self, x: torch.Tensor) -> List[torch.Tensor]:
+        feats: List[torch.Tensor] = []
+        for idx, layer in enumerate(self.features):
+            x = layer(x)
+            if idx in (1, 3, 5, 7):
+                feats.append(x)
+        return feats
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        input_size = x.shape[-2:]
+        feats = self.forward_features(x)
+        pyramid = [lat(feat) for lat, feat in zip(self.lateral, feats)]
+        for i in range(len(pyramid) - 1, 0, -1):
+            pyramid[i - 1] = pyramid[i - 1] + F.interpolate(
+                pyramid[i],
+                size=pyramid[i - 1].shape[-2:],
+                mode="bilinear",
+                align_corners=False,
+            )
+        pyramid = [smooth(feat) for smooth, feat in zip(self.smooth, pyramid)]
+        target_size = pyramid[0].shape[-2:]
+        pyramid = [
+            feat if feat.shape[-2:] == target_size else F.interpolate(feat, size=target_size, mode="bilinear", align_corners=False)
+            for feat in pyramid
+        ]
+        logits = self.decoder(torch.cat(pyramid, dim=1))
+        return F.interpolate(logits, size=input_size, mode="bilinear", align_corners=False)
diff --git a/coral_seg/coral_seg/splits.py b/coral_seg/coral_seg/splits.py
new file mode 100644
index 0000000000000000000000000000000000000000..516b2d384454e14eb35109a5caa2031825250c67
--- /dev/null
+++ b/coral_seg/coral_seg/splits.py
@@ -0,0 +1,44 @@
+from pathlib import Path
+from typing import Iterable, List, Tuple
+
+import numpy as np
+
+from .utils import ensure_dir, list_pngs
+
+
+def create_split_files(
+    image_dir: str,
+    mask_dir: str,
+    split_dir: str,
+    val_ratio: float = 0.15,
+    seed: int = 2026,
+) -> Tuple[Path, Path, Path]:
+    image_paths = list_pngs(image_dir)
+    mask_dir_path = Path(mask_dir)
+    ids = []
+    for image_path in image_paths:
+        if not (mask_dir_path / image_path.name).exists():
+            raise FileNotFoundError(f"Missing mask for {image_path.name}")
+        ids.append(image_path.stem)
+
+    rng = np.random.default_rng(seed)
+    ids = np.array(ids)
+    order = rng.permutation(len(ids))
+    val_count = max(1, int(round(len(ids) * val_ratio)))
+    val_ids = sorted(ids[order[:val_count]].tolist())
+    train_ids = sorted(ids[order[val_count:]].tolist())
+    full_ids = sorted(ids.tolist())
+
+    split_path = ensure_dir(split_dir)
+    train_file = split_path / "train.txt"
+    val_file = split_path / "val.txt"
+    full_file = split_path / "train_full.txt"
+    train_file.write_text("\n".join(train_ids) + "\n", encoding="utf-8")
+    val_file.write_text("\n".join(val_ids) + "\n", encoding="utf-8")
+    full_file.write_text("\n".join(full_ids) + "\n", encoding="utf-8")
+    return train_file, val_file, full_file
+
+
+def read_ids(path: str | Path) -> List[str]:
+    p = Path(path)
+    return [line.strip() for line in p.read_text(encoding="utf-8").splitlines() if line.strip()]
diff --git a/coral_seg/coral_seg/utils.py b/coral_seg/coral_seg/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d035436c2c8e9e3876b9a00c9ae0702fdbb4e1b4
--- /dev/null
+++ b/coral_seg/coral_seg/utils.py
@@ -0,0 +1,140 @@
+import json
+import math
+import os
+import random
+import time
+import zipfile
+from pathlib import Path
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple
+
+import numpy as np
+import torch
+import yaml
+from PIL import Image
+
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+
+def load_config(path: str | os.PathLike[str]) -> Dict[str, Any]:
+    with open(path, "r", encoding="utf-8") as f:
+        return yaml.safe_load(f)
+
+
+def ensure_dir(path: str | os.PathLike[str]) -> Path:
+    p = Path(path)
+    p.mkdir(parents=True, exist_ok=True)
+    return p
+
+
+def set_seed(seed: int) -> None:
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.benchmark = True
+
+
+def read_image(path: str | os.PathLike[str]) -> np.ndarray:
+    return np.array(Image.open(path).convert("RGB"))
+
+
+def read_mask(path: str | os.PathLike[str]) -> np.ndarray:
+    return np.array(Image.open(path), dtype=np.uint8)
+
+
+def save_mask(path: str | os.PathLike[str], mask: np.ndarray) -> None:
+    mask = np.asarray(mask, dtype=np.uint8)
+    Image.fromarray(mask, mode="L").save(path)
+
+
+def normalize_tensor(image: np.ndarray) -> torch.Tensor:
+    image = image.astype(np.float32) / 255.0
+    mean = np.array(IMAGENET_MEAN, dtype=np.float32)
+    std = np.array(IMAGENET_STD, dtype=np.float32)
+    image = (image - mean) / std
+    return torch.from_numpy(image.transpose(2, 0, 1)).float()
+
+
+def denormalize_tensor(x: torch.Tensor) -> torch.Tensor:
+    mean = torch.tensor(IMAGENET_MEAN, device=x.device).view(1, 3, 1, 1)
+    std = torch.tensor(IMAGENET_STD, device=x.device).view(1, 3, 1, 1)
+    return x * std + mean
+
+
+class AverageMeter:
+    def __init__(self) -> None:
+        self.reset()
+
+    def reset(self) -> None:
+        self.sum = 0.0
+        self.count = 0
+
+    @property
+    def avg(self) -> float:
+        return self.sum / max(1, self.count)
+
+    def update(self, value: float, n: int = 1) -> None:
+        self.sum += float(value) * n
+        self.count += n
+
+
+def fast_hist(pred: np.ndarray, target: np.ndarray, num_classes: int, ignore_index: int = 255) -> np.ndarray:
+    pred = pred.reshape(-1)
+    target = target.reshape(-1)
+    valid = (target != ignore_index) & (target >= 0) & (target < num_classes)
+    hist = np.bincount(
+        num_classes * target[valid].astype(np.int64) + pred[valid].astype(np.int64),
+        minlength=num_classes**2,
+    )
+    return hist.reshape(num_classes, num_classes)
+
+
+def compute_iou(hist: np.ndarray) -> Tuple[np.ndarray, float]:
+    denom = hist.sum(1) + hist.sum(0) - np.diag(hist)
+    iou = np.divide(np.diag(hist), denom, out=np.full_like(denom, np.nan, dtype=np.float64), where=denom != 0)
+    return iou, float(np.nanmean(iou))
+
+
+def format_metrics(iou: np.ndarray, miou: float, class_names: Sequence[str]) -> str:
+    parts = [f"mIoU={miou:.5f}"]
+    for name, value in zip(class_names, iou):
+        parts.append(f"{name}={value:.5f}" if not np.isnan(value) else f"{name}=nan")
+    return " ".join(parts)
+
+
+def save_json(path: str | os.PathLike[str], obj: Dict[str, Any]) -> None:
+    with open(path, "w", encoding="utf-8") as f:
+        json.dump(obj, f, indent=2, ensure_ascii=False)
+
+
+def cosine_warmup_lambda(
+    current_step: int,
+    *,
+    total_steps: int,
+    warmup_steps: int,
+    min_factor: float = 0.01,
+) -> float:
+    if current_step < warmup_steps:
+        return float(current_step + 1) / float(max(1, warmup_steps))
+    progress = float(current_step - warmup_steps) / float(max(1, total_steps - warmup_steps))
+    cosine = 0.5 * (1.0 + math.cos(math.pi * progress))
+    return min_factor + (1.0 - min_factor) * cosine
+
+
+def make_results_zip(results_dir: str | os.PathLike[str], zip_path: str | os.PathLike[str]) -> None:
+    results_dir = Path(results_dir)
+    zip_path = Path(zip_path)
+    ensure_dir(zip_path.parent)
+    with zipfile.ZipFile(zip_path, "w", compression=zipfile.ZIP_DEFLATED) as zf:
+        for path in sorted(results_dir.glob("*.png")):
+            zf.write(path, arcname=f"results/{path.name}")
+
+
+def list_pngs(path: str | os.PathLike[str]) -> List[Path]:
+    return sorted(Path(path).glob("*.png"))
+
+
+def timestamp() -> str:
+    return time.strftime("%Y-%m-%d %H:%M:%S")
diff --git a/coral_seg/docs/operation-guide.md b/coral_seg/docs/operation-guide.md
new file mode 100644
index 0000000000000000000000000000000000000000..6f83bbfddf9966cd6770aad876dff3cde8c3e505
--- /dev/null
+++ b/coral_seg/docs/operation-guide.md
@@ -0,0 +1,575 @@
+# 完整操作指南
+
+本文档说明如何从环境部署开始，完成数据解压、预训练模型下载、模型训练、结果推理、可视化、mIoU 评估和 `results.zip` 压缩提交。
+
+## 1. 运行目录
+
+推荐从工作区根目录执行所有命令：
+
+```bash
+cd /root/autodl-tmp/coral
+```
+
+当前配置默认使用以下路径：
+
+```text
+训练图像：/root/autodl-tmp/coral/data_fresh/train_bsdtar/train/image
+训练标签：/root/autodl-tmp/coral/data_fresh/train_bsdtar/train/label
+测试图像：/root/autodl-tmp/coral/data_fresh/test_raw/PrecisionLabel800/images
+工程目录：/root/autodl-tmp/coral/coral_seg
+```
+
+## 2. 环境部署
+
+本项目没有单独新建 conda 环境，直接使用当前默认 Python 环境。
+
+检查当前环境：
+
+```bash
+which python
+python -V
+python -c "import torch; print(torch.__version__, torch.cuda.is_available())"
+```
+
+当前已验证版本：
+
+```text
+Python 3.12.3
+PyTorch 2.3.0+cu121
+torchvision 0.18.0+cu121
+transformers 4.46.3
+timm 1.0.27
+albumentations 2.0.8
+opencv-python-headless 4.13.0.92
+```
+
+如果需要重新安装依赖：
+
+```bash
+python -m pip install -U \
+  transformers==4.46.3 \
+  timm==1.0.27 \
+  albumentations==2.0.8 \
+  opencv-python-headless==4.13.0.92 \
+  safetensors \
+  accelerate \
+  PyYAML \
+  huggingface-hub
+```
+
+不要随意升级 `torch` 和 `torchvision`，当前训练代码已经按现有版本验证通过。
+
+## 3. 数据解压
+
+如果 `data_fresh/` 已经存在且检查正常，可以跳过本节。
+
+外层数据包：
+
+```text
+/root/autodl-tmp/比赛data.zip
+```
+
+解压外层 zip：
+
+```bash
+mkdir -p extracted_zip
+unzip -q -o '比赛data.zip' -d extracted_zip
+```
+
+外层解压后包含：
+
+```text
+比赛data/train.rar
+比赛data/test.zip
+```
+
+训练集建议使用 `bsdtar` 解压。旧版 `7z` 对该 RAR5 压缩方式可能报 `Unsupported Method`。
+
+```bash
+mkdir -p data_fresh/train_bsdtar
+bsdtar -xf 'extracted_zip/比赛data/train.rar' -C data_fresh/train_bsdtar
+```
+
+测试集解压：
+
+```bash
+mkdir -p data_fresh/test_raw
+unzip -q -o 'extracted_zip/比赛data/test.zip' -d data_fresh/test_raw
+```
+
+清理旧数据和临时目录：
+
+```bash
+rm -rf data data_fresh/train_raw extracted_zip
+```
+
+## 4. 检查数据
+
+执行：
+
+```bash
+python coral_seg/tools/check_data.py --config coral_seg/configs/final_dual.yaml
+```
+
+正常输出应包含：
+
+```text
+train_images 3000 0000.png 2999.png
+train_masks 3000 0000.png 2999.png
+test_images 800 image_0001.png image_0800.png
+mask values: [0, 1, 2, 3]
+bad masks: 0 []
+```
+
+如果 `bad masks` 不为 0，说明训练标签像素值异常，不应继续训练。
+
+## 5. 预训练模型部署和下载
+
+本方案需要两类预训练模型，均通过配置文件显式指定路径。
+
+```text
+Mask2Former-Swin-B：facebook/mask2former-swin-base-ade-semantic
+ConvNeXt-L：torchvision convnext_large ImageNet-1K 权重
+```
+
+配置位置：
+
+```text
+coral_seg/configs/final_dual.yaml
+```
+
+对应字段：
+
+```text
+mask2former.pretrained_name: facebook/mask2former-swin-base-ade-semantic
+mask2former.pretrained_dir:  /root/autodl-tmp/coral/coral_seg/pretrained/mask2former-swin-base-ade-semantic
+convnext.pretrained_path:    /root/autodl-tmp/coral/coral_seg/pretrained/convnext_large-ea097f82.pth
+```
+
+### 5.1 在线下载
+
+如果机器可以访问 HuggingFace 和 PyTorch 权重源，直接执行：
+
+```bash
+python coral_seg/tools/download_pretrained.py --config coral_seg/configs/final_dual.yaml
+```
+
+该脚本会完成两件事：
+
+```text
+1. 下载 Mask2Former-Swin-B 到 coral_seg/pretrained/mask2former-swin-base-ade-semantic/
+2. 下载 ConvNeXt-L 的 torchvision 权重，并复制到 coral_seg/pretrained/convnext_large-ea097f82.pth
+```
+
+下载完成后，工程内应存在：
+
+```text
+coral_seg/pretrained/
+├── convnext_large-ea097f82.pth
+└── mask2former-swin-base-ade-semantic/
+    ├── config.json
+    ├── preprocessor_config.json
+    ├── model.safetensors
+    └── pytorch_model.bin
+```
+
+`model.safetensors` 和 `pytorch_model.bin` 至少保留一个即可；当前目录同时保留两者，便于不同版本 `transformers` 加载。
+
+### 5.2 离线部署
+
+如果复现环境不能联网，需要提前把预训练模型放到以下位置。
+
+Mask2Former 目录：
+
+```text
+/root/autodl-tmp/coral/coral_seg/pretrained/mask2former-swin-base-ade-semantic/
+```
+
+该目录至少需要包含：
+
+```text
+config.json
+preprocessor_config.json
+model.safetensors 或 pytorch_model.bin
+```
+
+ConvNeXt-L 权重文件：
+
+```text
+/root/autodl-tmp/coral/coral_seg/pretrained/convnext_large-ea097f82.pth
+```
+
+如果当前机器已经有 torchvision 缓存，可以复制到工程目录：
+
+```bash
+mkdir -p coral_seg/pretrained
+cp /root/.cache/torch/hub/checkpoints/convnext_large-ea097f82.pth \
+  coral_seg/pretrained/convnext_large-ea097f82.pth
+```
+
+训练代码会优先读取 `convnext.pretrained_path`。只要该文件存在，就不会依赖 `/root/.cache/torch` 中的缓存。
+
+### 5.3 完整性检查
+
+执行以下命令检查预训练模型文件是否齐全：
+
+```bash
+python - <<'PY_CHECK'
+from pathlib import Path
+
+root = Path('coral_seg/pretrained')
+mask2former = root / 'mask2former-swin-base-ade-semantic'
+convnext = root / 'convnext_large-ea097f82.pth'
+
+required = [
+    mask2former / 'config.json',
+    mask2former / 'preprocessor_config.json',
+    convnext,
+]
+missing = [str(p) for p in required if not p.is_file()]
+has_weight = (mask2former / 'model.safetensors').is_file() or (mask2former / 'pytorch_model.bin').is_file()
+if not has_weight:
+    missing.append(str(mask2former / 'model.safetensors 或 pytorch_model.bin'))
+
+print('missing:', missing)
+print('mask2former_dir:', mask2former.resolve())
+print('convnext_weight:', convnext.resolve(), convnext.stat().st_size if convnext.is_file() else 0)
+PY_CHECK
+```
+
+正常情况下：
+
+```text
+missing: []
+```
+
+### 5.4 加载说明
+
+ConvNeXt 分支：
+
+```text
+coral_seg/model_convnext_fpn.py 会优先读取 convnext.pretrained_path
+如果该文件不存在，才会退回到 torchvision 自动下载或缓存加载
+```
+
+Mask2Former 分支：
+
+```text
+train_mask2former.py 会从 mask2former.pretrained_dir 加载本地目录
+分类头从 ADE20K 的 150 类改为本任务的 4 类，因此分类头重新初始化是正常现象
+```
+
+常见日志：
+
+```text
+class_predictor shape mismatch
+```
+
+这是正常现象，不是权重损坏。
+
+如果在线下载时出现网络超时，可以改用离线部署方式，只要上述文件清单完整即可继续训练。
+
+## 6. 生成训练和验证划分
+
+执行：
+
+```bash
+python coral_seg/tools/create_splits.py --config coral_seg/configs/final_dual.yaml
+```
+
+生成：
+
+```text
+coral_seg/splits/train.txt
+coral_seg/splits/val.txt
+coral_seg/splits/train_full.txt
+```
+
+默认划分：
+
+```text
+85% 训练集
+15% 验证集
+seed = 2026
+```
+
+## 7. 训练 ConvNeXt-L-FPN
+
+建议先训练 ConvNeXt 分支，因为它更稳定、速度更快。
+
+```bash
+mkdir -p coral_seg/logs
+
+python coral_seg/tools/train_convnext.py \
+  --config coral_seg/configs/final_dual.yaml \
+  2>&1 | tee coral_seg/logs/convnext_train.log
+```
+
+主要配置：
+
+```text
+输入尺寸：512
+batch size：4
+epoch：120
+优化器：AdamW
+学习率：6e-5
+损失函数：CE + Dice + Lovasz
+label smoothing：0.05
+```
+
+训练产物：
+
+```text
+coral_seg/work_dirs/convnext_l_fpn/best.pth
+coral_seg/work_dirs/convnext_l_fpn/latest.pth
+```
+
+查看日志：
+
+```bash
+tail -f coral_seg/logs/convnext_train.log
+```
+
+查看验证集 mIoU：
+
+```bash
+grep "convnext val" coral_seg/logs/convnext_train.log
+```
+
+## 8. 训练 Mask2Former-Swin-B
+
+ConvNeXt 训练完成后，训练 Mask2Former：
+
+```bash
+python coral_seg/tools/train_mask2former.py \
+  --config coral_seg/configs/final_dual.yaml \
+  2>&1 | tee coral_seg/logs/mask2former_train.log
+```
+
+主要配置：
+
+```text
+输入尺寸：384
+batch size：2
+epoch：80
+优化器：AdamW
+学习率：3e-5
+预训练权重：facebook/mask2former-swin-base-ade-semantic
+```
+
+训练产物：
+
+```text
+coral_seg/work_dirs/mask2former_swin_b/best.pth
+coral_seg/work_dirs/mask2former_swin_b/latest.pth
+coral_seg/work_dirs/mask2former_swin_b/best_hf/
+```
+
+查看日志：
+
+```bash
+tail -f coral_seg/logs/mask2former_train.log
+```
+
+查看验证集 mIoU：
+
+```bash
+grep "mask2former val" coral_seg/logs/mask2former_train.log
+```
+
+常见日志说明：
+
+```text
+class_predictor shape mismatch
+```
+
+这是正常现象。ADE20K 预训练模型是 150 类，本任务是 4 类，分类头需要重新初始化。
+
+```text
+Gradient checkpointing is not supported
+```
+
+这是正常现象。本地 transformers 的 Mask2Former 类不支持梯度检查点，代码会自动跳过。
+
+## 9. 查看 GPU 状态
+
+```bash
+nvidia-smi
+```
+
+持续刷新：
+
+```bash
+watch -n 2 nvidia-smi
+```
+
+## 10. 中断后继续训练
+
+ConvNeXt 继续训练：
+
+```bash
+python coral_seg/tools/train_convnext.py \
+  --config coral_seg/configs/final_dual.yaml \
+  --resume coral_seg/work_dirs/convnext_l_fpn/latest.pth
+```
+
+Mask2Former 继续训练：
+
+```bash
+python coral_seg/tools/train_mask2former.py \
+  --config coral_seg/configs/final_dual.yaml \
+  --resume coral_seg/work_dirs/mask2former_swin_b/latest.pth
+```
+
+## 11. 验证集融合 mIoU
+
+两个模型都训练完成后，计算验证集双模型融合 mIoU：
+
+```bash
+python coral_seg/tools/eval_ensemble_val.py \
+  --config coral_seg/configs/final_dual.yaml \
+  --convnext-checkpoint coral_seg/work_dirs/convnext_l_fpn/best.pth \
+  --mask2former-checkpoint coral_seg/work_dirs/mask2former_swin_b/best_hf
+```
+
+## 12. 测试集推理
+
+双模型融合推理：
+
+```bash
+python coral_seg/tools/infer_ensemble.py \
+  --config coral_seg/configs/final_dual.yaml \
+  --convnext-checkpoint coral_seg/work_dirs/convnext_l_fpn/best.pth \
+  --mask2former-checkpoint coral_seg/work_dirs/mask2former_swin_b/best_hf
+```
+
+推理输出：
+
+```text
+coral_seg/submissions/results/
+coral_seg/submissions/results.zip
+```
+
+`results/` 中应包含 800 张单通道 PNG：
+
+```text
+image_0001.png
+image_0002.png
+...
+image_0800.png
+```
+
+每张 PNG 的像素值只能是：
+
+```text
+0, 1, 2, 3
+```
+
+## 13. 检查并压缩 results
+
+推理脚本会自动压缩 `results.zip`。如果需要手动检查：
+
+```bash
+python - <<'PY_CHECK'
+import zipfile
+from pathlib import Path
+from PIL import Image
+import numpy as np
+
+zip_path = Path('coral_seg/submissions/results.zip')
+with zipfile.ZipFile(zip_path) as zf:
+    names = zf.namelist()
+    print('num files:', len(names))
+    print('first:', names[:3])
+    print('last:', names[-3:])
+
+values = set()
+for p in sorted(Path('coral_seg/submissions/results').glob('*.png')):
+    arr = np.array(Image.open(p))
+    values |= set(np.unique(arr).tolist())
+print('values:', sorted(values))
+PY_CHECK
+```
+
+正常结果：
+
+```text
+num files: 800
+first: ['results/image_0001.png', ...]
+values: [0, 1, 2, 3]
+```
+
+如果需要手动重新压缩：
+
+```bash
+cd coral_seg/submissions
+zip -r results.zip results
+cd /root/autodl-tmp/coral
+```
+
+压缩包内部结构必须是：
+
+```text
+results.zip
+└── results/
+    ├── image_0001.png
+    ├── image_0002.png
+    ├── ...
+    └── image_0800.png
+```
+
+## 14. 生成可视化结果
+
+```bash
+python coral_seg/tools/visualize_results.py \
+  --image-dir data_fresh/test_raw/PrecisionLabel800/images \
+  --pred-dir coral_seg/submissions/results \
+  --out-dir coral_seg/vis_results \
+  --num 24
+```
+
+输出目录：
+
+```text
+coral_seg/vis_results/
+```
+
+每张可视化图包含：
+
+```text
+原图
+预测彩色 mask
+预测叠加图
+```
+
+## 15. 本地 test mIoU
+
+只有当 `data_fresh/test_raw/PrecisionLabel800/mask` 确认为官方测试集真值时，下面的 test mIoU 才有意义。
+
+当前识别到该目录是 RGB 彩色 mask，颜色映射为：
+
+```text
+(0, 0, 0)       -> 0 背景
+(240, 110, 170) -> 1 活珊瑚
+(240, 171, 203) -> 2 死珊瑚
+(0, 162, 232)   -> 3 白化珊瑚
+```
+
+计算命令：
+
+```bash
+python coral_seg/tools/eval_predictions.py \
+  --pred-dir coral_seg/submissions/results \
+  --gt-dir data_fresh/test_raw/PrecisionLabel800/mask \
+  --color-map '0,0,0:0;240,110,170:1;240,171,203:2;0,162,232:3'
+```
+
+如果该 `mask` 目录不是官方真值，而只是参考或占位 mask，则最终测试集成绩以竞赛平台为准。
+
+## 16. 最终提交文件
+
+最终提交文件位置：
+
+```text
+/root/autodl-tmp/coral/coral_seg/submissions/results.zip
+```
diff --git a/coral_seg/docs/technical-report.md b/coral_seg/docs/technical-report.md
new file mode 100644
index 0000000000000000000000000000000000000000..e86cda59129a445f2717494aeacae083cbd3b421
--- /dev/null
+++ b/coral_seg/docs/technical-report.md
@@ -0,0 +1,566 @@
+# 方法与技术细节说明
+
+本文档说明本项目解决水下珊瑚语义分割任务的完整技术方案，包括任务分析、模型设计、训练策略、损失函数、数据增强、融合推理、mIoU 计算和复现设计。
+
+## 1. 任务定义
+
+输入为 `256x256` RGB 水下图像，输出为同尺寸单通道语义分割图。
+
+类别定义：
+
+```text
+0 背景
+1 活珊瑚
+2 死珊瑚
+3 白化珊瑚
+```
+
+评价指标为 mIoU。
+
+比赛限制：
+
+```text
+只允许使用官方训练集
+测试集只允许用于推理
+不得使用伪标签、自训练或测试集分布统计更新
+预训练模型必须是 2026-01-01 前公开发布的开源模型
+最终复现要求在 A30 单卡 24G 上训练和推理总时间不超过 24 小时
+```
+
+## 2. 数据难点
+
+水下珊瑚图像相比普通自然图像有明显差异：
+
+```text
+光照不均
+颜色偏移
+水体浑浊
+悬浮颗粒干扰
+珊瑚边界不规则
+活珊瑚、死珊瑚、白化珊瑚外观相似
+标签存在噪声
+```
+
+因此，模型需要同时利用：
+
+```text
+局部纹理
+边界形态
+多尺度上下文
+全局语义信息
+```
+
+单纯依赖颜色容易失败，尤其在水下颜色失真严重时。
+
+## 3. 总体方案
+
+最终采用双模型融合：
+
+```text
+Mask2Former-Swin-B + ConvNeXt-L-FPN
+```
+
+职责划分：
+
+```text
+Mask2Former-Swin-B：负责复杂区域、全局上下文和 mask 级建模
+ConvNeXt-L-FPN：负责局部纹理、边界细节和卷积稳定性
+```
+
+融合方式：
+
+```text
+final_prob = 0.55 * prob_mask2former + 0.45 * prob_convnext
+prediction = argmax(final_prob)
+```
+
+选择异构融合的原因：
+
+```text
+两类模型错误模式不同
+Transformer 更擅长全局关系
+卷积模型更擅长局部纹理和边界
+概率融合可以降低单模型偶然错误
+```
+
+## 4. ConvNeXt-L-FPN 分支
+
+### 4.1 ConvNeXt-L 骨干
+
+ConvNeXt 是现代卷积网络，吸收了 Transformer 时代的一些结构设计，例如：
+
+```text
+大卷积核深度卷积
+更合理的 stage 设计
+GELU 激活
+LayerNorm 风格结构
+倒瓶颈结构
+```
+
+本项目使用 torchvision 的 `convnext_large`，并加载 ImageNet-1K 预训练权重。
+
+优势：
+
+```text
+原生 torchvision 支持，环境稳定
+卷积归纳偏置强，适合小数据和纹理任务
+局部边界表达能力好
+训练速度可控
+```
+
+### 4.2 FPN 解码器
+
+ConvNeXt-L 输出四个尺度特征：
+
+```text
+192, 384, 768, 1536 channels
+```
+
+FPN 解码流程：
+
+```text
+1. 每层特征用 1x1 卷积投影到 256 通道
+2. 高层特征逐级上采样并与低层相加
+3. 每层用 3x3 卷积平滑
+4. 所有尺度上采样到同一分辨率
+5. 拼接后输出 4 类 logits
+```
+
+FPN 的作用：
+
+```text
+高层特征提供语义
+低层特征提供边界
+多尺度融合提升小目标和复杂区域分割效果
+```
+
+### 4.3 ConvNeXt 训练配置
+
+```text
+输入尺寸：512
+batch size：4
+epoch：120
+优化器：AdamW
+基础学习率：6e-5
+backbone 学习率：基础学习率的 0.25
+weight decay：0.01
+学习率策略：warmup + cosine decay
+混合精度：开启
+```
+
+骨干网络使用更低学习率，是为了保护预训练特征，降低小数据噪声标签导致的过拟合风险。
+
+## 5. Mask2Former-Swin-B 分支
+
+### 5.1 Mask2Former 思想
+
+Mask2Former 将分割任务建模为 mask classification。
+
+普通语义分割直接输出每个像素的类别；Mask2Former 则预测一组 query，每个 query 包含：
+
+```text
+类别概率
+对应的二值 mask
+```
+
+最终语义分割通过：
+
+```text
+类别概率 x mask 概率
+```
+
+组合成每个类别的像素概率图。
+
+这种方式对复杂、不规则区域更友好，适合珊瑚这类边界复杂的对象。
+
+### 5.2 Swin Transformer 骨干
+
+Swin Transformer 使用窗口注意力机制：
+
+```text
+局部窗口 self-attention 降低计算量
+shifted window 实现跨窗口信息交互
+分层结构输出多尺度特征
+```
+
+它同时具备 Transformer 的上下文建模能力和类似 CNN 的层级特征结构。
+
+### 5.3 预训练模型
+
+Mask2Former 分支使用：
+
+```text
+facebook/mask2former-swin-base-ade-semantic
+```
+
+该模型是 ADE20K 语义分割预训练模型。ADE20K 为 150 类，本任务为 4 类，因此加载时分类头会重新初始化，这是正常现象。
+
+ConvNeXt 分支使用：
+
+```text
+torchvision convnext_large ImageNet-1K 权重
+```
+
+本地部署路径由配置文件固定：
+
+```text
+mask2former.pretrained_dir = /root/autodl-tmp/coral/coral_seg/pretrained/mask2former-swin-base-ade-semantic
+convnext.pretrained_path = /root/autodl-tmp/coral/coral_seg/pretrained/convnext_large-ea097f82.pth
+```
+
+这样复现时不依赖临时下载缓存；联网环境可以运行 `tools/download_pretrained.py` 下载，离线环境只需要提前放入对应文件。
+
+### 5.4 标签格式转换
+
+原始标签格式：
+
+```text
+H x W，像素值为 0/1/2/3
+```
+
+Mask2Former 训练需要转换为：
+
+```text
+class_labels：当前图像中出现过的类别编号
+mask_labels：每个类别对应一个二值 mask
+```
+
+例如图像中出现类别 0、1、3，则：
+
+```text
+class_labels = [0, 1, 3]
+mask_labels.shape = [3, H, W]
+```
+
+## 6. 数据增强
+
+水下图像受光照、水体和成像条件影响明显，因此增强策略重点覆盖几何变化和颜色变化。
+
+使用增强：
+
+```text
+HorizontalFlip
+VerticalFlip
+RandomRotate90
+Affine scale/translate/rotate/shear
+RandomBrightnessContrast
+HueSaturationValue
+RandomGamma
+CLAHE
+GaussianBlur
+MotionBlur
+Resize
+Normalize
+```
+
+几何增强模拟：
+
+```text
+拍摄方向变化
+视角变化
+尺度变化
+轻微形变
+```
+
+颜色增强模拟：
+
+```text
+水下光照变化
+色偏
+对比度变化
+相机白平衡差异
+```
+
+模糊增强模拟：
+
+```text
+水体浑浊
+运动模糊
+散射导致的细节损失
+```
+
+## 7. 损失函数
+
+ConvNeXt 分支使用组合损失：
+
+```text
+Loss = CE + Dice + 0.5 * Lovasz
+```
+
+### 7.1 交叉熵损失
+
+交叉熵用于像素级分类监督：
+
+```text
+CE = -log P(correct_class)
+```
+
+优点是稳定、收敛快。
+
+### 7.2 Dice Loss
+
+Dice 衡量预测区域与真实区域的重叠程度：
+
+```text
+Dice = 2 * intersection / (prediction + target)
+```
+
+适合类别不均衡和小区域分割。
+
+### 7.3 Lovasz Loss
+
+Lovasz Softmax 是 IoU 的可优化近似，更贴近比赛 mIoU 指标。
+
+作用：
+
+```text
+优化目标更接近 mIoU
+提升区域交并比表现
+```
+
+### 7.4 Label Smoothing
+
+使用：
+
+```text
+label_smoothing = 0.05
+```
+
+作用：
+
+```text
+降低模型对噪声标签的过度自信
+提高泛化能力
+缓解标注误差
+```
+
+Mask2Former 分支使用模型内部的分类、mask 和 dice 损失组合。
+
+## 8. 学习率策略
+
+两个模型均使用：
+
+```text
+warmup + cosine decay
+```
+
+warmup 作用：
+
+```text
+避免训练初期学习率过大破坏预训练权重
+降低 loss 震荡
+提升训练稳定性
+```
+
+cosine decay 作用：
+
+```text
+后期平滑降低学习率
+帮助模型收敛到更稳定的局部最优
+```
+
+## 9. 噪声标签处理
+
+当前方案通过以下方式降低噪声标签影响：
+
+```text
+使用强预训练模型
+使用 label smoothing
+使用 Dice 和 Lovasz 关注区域级一致性
+强数据增强提升泛化
+保存验证集 best checkpoint
+双模型融合降低单模型噪声敏感性
+```
+
+没有使用测试集伪标签、自训练或测试集分布统计，符合比赛规则。
+
+## 10. 融合推理
+
+两个模型分别输出每个类别的概率图：
+
+```text
+prob_convnext: [4, H, W]
+prob_mask2former: [4, H, W]
+```
+
+融合：
+
+```text
+prob = 0.45 * prob_convnext + 0.55 * prob_mask2former
+pred = argmax(prob)
+```
+
+使用概率融合而不是硬投票的原因：
+
+```text
+可以保留模型置信度
+对边界和混淆区域更平滑
+高置信模型可以纠正低置信模型
+```
+
+## 11. TTA 推理
+
+当前使用：
+
+```text
+原图
+水平翻转
+```
+
+流程：
+
+```text
+1. 原图推理得到概率
+2. 水平翻转图像推理
+3. 将翻转预测翻回原方向
+4. 对概率求平均
+```
+
+TTA 可以降低方向相关误差，提升预测稳定性。
+
+## 12. mIoU 计算原理
+
+对每个类别 c：
+
+```text
+TP_c：预测为 c 且真值为 c 的像素数
+FP_c：预测为 c 但真值不是 c 的像素数
+FN_c：真值为 c 但预测不是 c 的像素数
+```
+
+类别 IoU：
+
+```text
+IoU_c = TP_c / (TP_c + FP_c + FN_c)
+```
+
+最终：
+
+```text
+mIoU = mean(IoU_0, IoU_1, IoU_2, IoU_3)
+```
+
+这里的“区域”是同一类别的像素集合，不是实例框、连通块或单独目标实例。
+
+## 13. 本地 test mask 说明
+
+`data_fresh/test_raw/PrecisionLabel800/mask` 是 RGB 彩色图，不是标准单通道标签。
+
+统计到的颜色：
+
+```text
+(0, 0, 0)
+(240, 110, 170)
+(240, 171, 203)
+(0, 162, 232)
+```
+
+当前最合理颜色映射：
+
+```text
+(0, 0, 0)       -> 0 背景
+(240, 110, 170) -> 1 活珊瑚
+(240, 171, 203) -> 2 死珊瑚
+(0, 162, 232)   -> 3 白化珊瑚
+```
+
+如果该目录不是官方真值，则该结果不能代表平台成绩。
+
+## 14. 复现性设计
+
+为满足 A30 单卡 24G、24 小时内复现，方案做了以下取舍：
+
+```text
+不使用 5-fold
+不使用第三个大模型
+不使用超大输入尺寸
+不使用测试集伪标签
+不使用测试集统计更新
+固定 seed
+保存固定 train/val split
+保存 best/latest checkpoint
+提供完整训练、推理和打包脚本
+```
+
+推荐复现流程：
+
+```text
+1. 检查数据
+2. 下载预训练权重
+3. 训练 ConvNeXt-L-FPN
+4. 训练 Mask2Former-Swin-B
+5. 验证集融合评估
+6. 测试集融合推理
+7. 生成 results.zip
+```
+
+## 15. 代码模块说明
+
+```text
+coral_seg/coral_seg/dataset.py
+```
+
+负责图像读取、标签读取、数据增强和 Mask2Former 标签格式转换。
+
+```text
+coral_seg/coral_seg/model_convnext_fpn.py
+```
+
+负责 ConvNeXt-L 骨干和 FPN 解码器。
+
+```text
+coral_seg/coral_seg/losses.py
+```
+
+负责 CE、Dice、Lovasz 和组合损失。
+
+```text
+coral_seg/tools/train_convnext.py
+```
+
+负责 ConvNeXt 分支训练、验证和 checkpoint 保存。
+
+```text
+coral_seg/tools/train_mask2former.py
+```
+
+负责 Mask2Former 分支训练、验证和 HuggingFace 格式权重保存。
+
+```text
+coral_seg/tools/infer_ensemble.py
+```
+
+负责双模型 TTA 推理、概率融合、PNG 输出和 `results.zip` 生成。
+
+```text
+coral_seg/tools/eval_ensemble_val.py
+```
+
+负责验证集双模型融合 mIoU 计算。
+
+```text
+coral_seg/tools/eval_predictions.py
+```
+
+负责已有预测和标签目录之间的 mIoU 计算，支持 RGB 标签颜色映射。
+
+```text
+coral_seg/tools/visualize_results.py
+```
+
+负责预测结果彩色化和叠加可视化。
+
+## 16. 可提升方向
+
+如果后续还有时间，可以尝试：
+
+```text
+调整融合权重，例如 0.50/0.50 或 0.60/0.40
+对白化珊瑚增加类别权重
+增加轻量多尺度 TTA
+人工清洗明显错误标签
+最终阶段使用 train_full.txt 全量复训
+后处理去除极小孤立区域
+```
+
+正式复现阶段应优先保持当前稳定配置，避免引入不可控变量。
diff --git a/coral_seg/logs/convnext_train.log b/coral_seg/logs/convnext_train.log
new file mode 100644
index 0000000000000000000000000000000000000000..470cca650017fc0c9760cf34b25956c64cb007f5
--- /dev/null
+++ b/coral_seg/logs/convnext_train.log
@@ -0,0 +1,1591 @@
+2026-06-27 16:44:34 epoch=1/120 step=50/637 loss=2.71662 lr=0.00000096
+2026-06-27 16:44:40 epoch=1/120 step=100/637 loss=2.68148 lr=0.00000190
+2026-06-27 16:44:45 epoch=1/120 step=150/637 loss=2.64554 lr=0.00000284
+2026-06-27 16:44:51 epoch=1/120 step=200/637 loss=2.61647 lr=0.00000379
+2026-06-27 16:44:56 epoch=1/120 step=250/637 loss=2.59650 lr=0.00000473
+2026-06-27 16:45:01 epoch=1/120 step=300/637 loss=2.56967 lr=0.00000567
+2026-06-27 16:45:07 epoch=1/120 step=350/637 loss=2.53091 lr=0.00000661
+2026-06-27 16:45:12 epoch=1/120 step=400/637 loss=2.49872 lr=0.00000755
+2026-06-27 16:45:17 epoch=1/120 step=450/637 loss=2.46940 lr=0.00000850
+2026-06-27 16:45:22 epoch=1/120 step=500/637 loss=2.44554 lr=0.00000944
+2026-06-27 16:45:28 epoch=1/120 step=550/637 loss=2.42753 lr=0.00001038
+2026-06-27 16:45:33 epoch=1/120 step=600/637 loss=2.40862 lr=0.00001132
+2026-06-27 16:45:47 convnext val mIoU=0.37042 background=0.49276 live_coral=0.47697 dead_coral=0.19581 bleached_coral=0.31616
+2026-06-27 16:45:49 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 16:45:55 epoch=2/120 step=50/637 loss=2.14355 lr=0.00001296
+2026-06-27 16:46:00 epoch=2/120 step=100/637 loss=2.14720 lr=0.00001390
+2026-06-27 16:46:06 epoch=2/120 step=150/637 loss=2.15805 lr=0.00001484
+2026-06-27 16:46:11 epoch=2/120 step=200/637 loss=2.14964 lr=0.00001579
+2026-06-27 16:46:16 epoch=2/120 step=250/637 loss=2.13892 lr=0.00001673
+2026-06-27 16:46:22 epoch=2/120 step=300/637 loss=2.13156 lr=0.00001767
+2026-06-27 16:46:27 epoch=2/120 step=350/637 loss=2.12658 lr=0.00001861
+2026-06-27 16:46:33 epoch=2/120 step=400/637 loss=2.11425 lr=0.00001955
+2026-06-27 16:46:38 epoch=2/120 step=450/637 loss=2.11727 lr=0.00002050
+2026-06-27 16:46:44 epoch=2/120 step=500/637 loss=2.11255 lr=0.00002144
+2026-06-27 16:46:49 epoch=2/120 step=550/637 loss=2.10467 lr=0.00002238
+2026-06-27 16:46:54 epoch=2/120 step=600/637 loss=2.10238 lr=0.00002332
+2026-06-27 16:47:07 convnext val mIoU=0.40426 background=0.51797 live_coral=0.52660 dead_coral=0.21367 bleached_coral=0.35880
+2026-06-27 16:47:09 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 16:47:14 epoch=3/120 step=50/637 loss=2.02098 lr=0.00002496
+2026-06-27 16:47:20 epoch=3/120 step=100/637 loss=2.01418 lr=0.00002590
+2026-06-27 16:47:25 epoch=3/120 step=150/637 loss=2.00984 lr=0.00002684
+2026-06-27 16:47:31 epoch=3/120 step=200/637 loss=2.01109 lr=0.00002779
+2026-06-27 16:47:36 epoch=3/120 step=250/637 loss=2.00624 lr=0.00002873
+2026-06-27 16:47:41 epoch=3/120 step=300/637 loss=2.00283 lr=0.00002967
+2026-06-27 16:47:47 epoch=3/120 step=350/637 loss=2.00322 lr=0.00003061
+2026-06-27 16:47:52 epoch=3/120 step=400/637 loss=2.00153 lr=0.00003155
+2026-06-27 16:47:58 epoch=3/120 step=450/637 loss=2.00058 lr=0.00003250
+2026-06-27 16:48:03 epoch=3/120 step=500/637 loss=1.99440 lr=0.00003344
+2026-06-27 16:48:08 epoch=3/120 step=550/637 loss=1.99133 lr=0.00003438
+2026-06-27 16:48:14 epoch=3/120 step=600/637 loss=1.98274 lr=0.00003532
+2026-06-27 16:48:26 convnext val mIoU=0.43012 background=0.51080 live_coral=0.54845 dead_coral=0.27704 bleached_coral=0.38419
+2026-06-27 16:48:29 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 16:48:34 epoch=4/120 step=50/637 loss=1.92177 lr=0.00003696
+2026-06-27 16:48:40 epoch=4/120 step=100/637 loss=1.89495 lr=0.00003790
+2026-06-27 16:48:45 epoch=4/120 step=150/637 loss=1.91347 lr=0.00003884
+2026-06-27 16:48:51 epoch=4/120 step=200/637 loss=1.91374 lr=0.00003979
+2026-06-27 16:48:56 epoch=4/120 step=250/637 loss=1.91600 lr=0.00004073
+2026-06-27 16:49:01 epoch=4/120 step=300/637 loss=1.91080 lr=0.00004167
+2026-06-27 16:49:07 epoch=4/120 step=350/637 loss=1.90855 lr=0.00004261
+2026-06-27 16:49:12 epoch=4/120 step=400/637 loss=1.91528 lr=0.00004355
+2026-06-27 16:49:18 epoch=4/120 step=450/637 loss=1.91437 lr=0.00004450
+2026-06-27 16:49:23 epoch=4/120 step=500/637 loss=1.91588 lr=0.00004544
+2026-06-27 16:49:28 epoch=4/120 step=550/637 loss=1.91216 lr=0.00004638
+2026-06-27 16:49:34 epoch=4/120 step=600/637 loss=1.90823 lr=0.00004732
+2026-06-27 16:49:46 convnext val mIoU=0.43905 background=0.54191 live_coral=0.58298 dead_coral=0.29495 bleached_coral=0.33635
+2026-06-27 16:49:49 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 16:49:54 epoch=5/120 step=50/637 loss=1.84385 lr=0.00004896
+2026-06-27 16:50:00 epoch=5/120 step=100/637 loss=1.86171 lr=0.00004990
+2026-06-27 16:50:05 epoch=5/120 step=150/637 loss=1.87017 lr=0.00005084
+2026-06-27 16:50:11 epoch=5/120 step=200/637 loss=1.87724 lr=0.00005179
+2026-06-27 16:50:16 epoch=5/120 step=250/637 loss=1.87485 lr=0.00005273
+2026-06-27 16:50:21 epoch=5/120 step=300/637 loss=1.88565 lr=0.00005367
+2026-06-27 16:50:27 epoch=5/120 step=350/637 loss=1.88786 lr=0.00005461
+2026-06-27 16:50:32 epoch=5/120 step=400/637 loss=1.87439 lr=0.00005555
+2026-06-27 16:50:38 epoch=5/120 step=450/637 loss=1.87368 lr=0.00005650
+2026-06-27 16:50:43 epoch=5/120 step=500/637 loss=1.86782 lr=0.00005744
+2026-06-27 16:50:49 epoch=5/120 step=550/637 loss=1.86573 lr=0.00005838
+2026-06-27 16:50:54 epoch=5/120 step=600/637 loss=1.86336 lr=0.00005932
+2026-06-27 16:51:06 convnext val mIoU=0.46877 background=0.54790 live_coral=0.59464 dead_coral=0.29498 bleached_coral=0.43757
+2026-06-27 16:51:09 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 16:51:15 epoch=6/120 step=50/637 loss=1.94563 lr=0.00006000
+2026-06-27 16:51:20 epoch=6/120 step=100/637 loss=1.92297 lr=0.00006000
+2026-06-27 16:51:26 epoch=6/120 step=150/637 loss=1.89061 lr=0.00006000
+2026-06-27 16:51:31 epoch=6/120 step=200/637 loss=1.85606 lr=0.00006000
+2026-06-27 16:51:37 epoch=6/120 step=250/637 loss=1.84139 lr=0.00006000
+2026-06-27 16:51:42 epoch=6/120 step=300/637 loss=1.84655 lr=0.00006000
+2026-06-27 16:51:47 epoch=6/120 step=350/637 loss=1.84182 lr=0.00006000
+2026-06-27 16:51:53 epoch=6/120 step=400/637 loss=1.84078 lr=0.00006000
+2026-06-27 16:51:58 epoch=6/120 step=450/637 loss=1.83504 lr=0.00005999
+2026-06-27 16:52:04 epoch=6/120 step=500/637 loss=1.83844 lr=0.00005999
+2026-06-27 16:52:09 epoch=6/120 step=550/637 loss=1.83368 lr=0.00005999
+2026-06-27 16:52:15 epoch=6/120 step=600/637 loss=1.83786 lr=0.00005999
+2026-06-27 16:52:27 convnext val mIoU=0.45916 background=0.56144 live_coral=0.60738 dead_coral=0.23863 bleached_coral=0.42921
+2026-06-27 16:52:34 epoch=7/120 step=50/637 loss=1.73025 lr=0.00005999
+2026-06-27 16:52:39 epoch=7/120 step=100/637 loss=1.76065 lr=0.00005999
+2026-06-27 16:52:44 epoch=7/120 step=150/637 loss=1.79232 lr=0.00005998
+2026-06-27 16:52:50 epoch=7/120 step=200/637 loss=1.79499 lr=0.00005998
+2026-06-27 16:52:55 epoch=7/120 step=250/637 loss=1.79368 lr=0.00005998
+2026-06-27 16:53:00 epoch=7/120 step=300/637 loss=1.78265 lr=0.00005998
+2026-06-27 16:53:06 epoch=7/120 step=350/637 loss=1.79653 lr=0.00005997
+2026-06-27 16:53:11 epoch=7/120 step=400/637 loss=1.79807 lr=0.00005997
+2026-06-27 16:53:17 epoch=7/120 step=450/637 loss=1.80186 lr=0.00005997
+2026-06-27 16:53:22 epoch=7/120 step=500/637 loss=1.79815 lr=0.00005996
+2026-06-27 16:53:28 epoch=7/120 step=550/637 loss=1.79059 lr=0.00005996
+2026-06-27 16:53:33 epoch=7/120 step=600/637 loss=1.79220 lr=0.00005996
+2026-06-27 16:53:46 convnext val mIoU=0.47624 background=0.54786 live_coral=0.60585 dead_coral=0.32217 bleached_coral=0.42908
+2026-06-27 16:53:48 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 16:53:54 epoch=8/120 step=50/637 loss=1.82925 lr=0.00005995
+2026-06-27 16:53:59 epoch=8/120 step=100/637 loss=1.81056 lr=0.00005995
+2026-06-27 16:54:05 epoch=8/120 step=150/637 loss=1.80306 lr=0.00005994
+2026-06-27 16:54:10 epoch=8/120 step=200/637 loss=1.77735 lr=0.00005994
+2026-06-27 16:54:15 epoch=8/120 step=250/637 loss=1.76513 lr=0.00005994
+2026-06-27 16:54:21 epoch=8/120 step=300/637 loss=1.76161 lr=0.00005993
+2026-06-27 16:54:26 epoch=8/120 step=350/637 loss=1.76466 lr=0.00005993
+2026-06-27 16:54:32 epoch=8/120 step=400/637 loss=1.77804 lr=0.00005992
+2026-06-27 16:54:37 epoch=8/120 step=450/637 loss=1.77819 lr=0.00005992
+2026-06-27 16:54:42 epoch=8/120 step=500/637 loss=1.78232 lr=0.00005991
+2026-06-27 16:54:48 epoch=8/120 step=550/637 loss=1.77187 lr=0.00005991
+2026-06-27 16:54:53 epoch=8/120 step=600/637 loss=1.77085 lr=0.00005990
+2026-06-27 16:55:06 convnext val mIoU=0.48928 background=0.55380 live_coral=0.60595 dead_coral=0.35693 bleached_coral=0.44044
+2026-06-27 16:55:08 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 16:55:13 epoch=9/120 step=50/637 loss=1.70067 lr=0.00005990
+2026-06-27 16:55:19 epoch=9/120 step=100/637 loss=1.73390 lr=0.00005989
+2026-06-27 16:55:24 epoch=9/120 step=150/637 loss=1.76872 lr=0.00005988
+2026-06-27 16:55:30 epoch=9/120 step=200/637 loss=1.76799 lr=0.00005988
+2026-06-27 16:55:35 epoch=9/120 step=250/637 loss=1.75212 lr=0.00005987
+2026-06-27 16:55:41 epoch=9/120 step=300/637 loss=1.73264 lr=0.00005987
+2026-06-27 16:55:46 epoch=9/120 step=350/637 loss=1.72588 lr=0.00005986
+2026-06-27 16:55:51 epoch=9/120 step=400/637 loss=1.72870 lr=0.00005985
+2026-06-27 16:55:57 epoch=9/120 step=450/637 loss=1.73157 lr=0.00005985
+2026-06-27 16:56:02 epoch=9/120 step=500/637 loss=1.73356 lr=0.00005984
+2026-06-27 16:56:08 epoch=9/120 step=550/637 loss=1.73117 lr=0.00005983
+2026-06-27 16:56:13 epoch=9/120 step=600/637 loss=1.73628 lr=0.00005983
+2026-06-27 16:56:26 convnext val mIoU=0.49439 background=0.56433 live_coral=0.61264 dead_coral=0.35806 bleached_coral=0.44252
+2026-06-27 16:56:28 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 16:56:34 epoch=10/120 step=50/637 loss=1.74424 lr=0.00005982
+2026-06-27 16:56:39 epoch=10/120 step=100/637 loss=1.74460 lr=0.00005981
+2026-06-27 16:56:44 epoch=10/120 step=150/637 loss=1.74402 lr=0.00005980
+2026-06-27 16:56:50 epoch=10/120 step=200/637 loss=1.72740 lr=0.00005979
+2026-06-27 16:56:55 epoch=10/120 step=250/637 loss=1.70748 lr=0.00005979
+2026-06-27 16:57:01 epoch=10/120 step=300/637 loss=1.70936 lr=0.00005978
+2026-06-27 16:57:06 epoch=10/120 step=350/637 loss=1.71476 lr=0.00005977
+2026-06-27 16:57:11 epoch=10/120 step=400/637 loss=1.71070 lr=0.00005976
+2026-06-27 16:57:17 epoch=10/120 step=450/637 loss=1.71248 lr=0.00005975
+2026-06-27 16:57:22 epoch=10/120 step=500/637 loss=1.71069 lr=0.00005975
+2026-06-27 16:57:28 epoch=10/120 step=550/637 loss=1.70641 lr=0.00005974
+2026-06-27 16:57:33 epoch=10/120 step=600/637 loss=1.70167 lr=0.00005973
+2026-06-27 16:57:46 convnext val mIoU=0.50432 background=0.59762 live_coral=0.63441 dead_coral=0.32787 bleached_coral=0.45737
+2026-06-27 16:57:49 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 16:57:54 epoch=11/120 step=50/637 loss=1.62350 lr=0.00005971
+2026-06-27 16:58:00 epoch=11/120 step=100/637 loss=1.66251 lr=0.00005971
+2026-06-27 16:58:05 epoch=11/120 step=150/637 loss=1.65280 lr=0.00005970
+2026-06-27 16:58:11 epoch=11/120 step=200/637 loss=1.66847 lr=0.00005969
+2026-06-27 16:58:16 epoch=11/120 step=250/637 loss=1.67060 lr=0.00005968
+2026-06-27 16:58:22 epoch=11/120 step=300/637 loss=1.66973 lr=0.00005967
+2026-06-27 16:58:27 epoch=11/120 step=350/637 loss=1.66622 lr=0.00005966
+2026-06-27 16:58:33 epoch=11/120 step=400/637 loss=1.67080 lr=0.00005965
+2026-06-27 16:58:38 epoch=11/120 step=450/637 loss=1.66732 lr=0.00005964
+2026-06-27 16:58:43 epoch=11/120 step=500/637 loss=1.67433 lr=0.00005963
+2026-06-27 16:58:49 epoch=11/120 step=550/637 loss=1.67492 lr=0.00005962
+2026-06-27 16:58:54 epoch=11/120 step=600/637 loss=1.67642 lr=0.00005961
+2026-06-27 16:59:07 convnext val mIoU=0.50316 background=0.59832 live_coral=0.63323 dead_coral=0.32778 bleached_coral=0.45330
+2026-06-27 16:59:14 epoch=12/120 step=50/637 loss=1.73225 lr=0.00005959
+2026-06-27 16:59:19 epoch=12/120 step=100/637 loss=1.70270 lr=0.00005958
+2026-06-27 16:59:24 epoch=12/120 step=150/637 loss=1.69895 lr=0.00005957
+2026-06-27 16:59:30 epoch=12/120 step=200/637 loss=1.68420 lr=0.00005956
+2026-06-27 16:59:35 epoch=12/120 step=250/637 loss=1.68662 lr=0.00005955
+2026-06-27 16:59:41 epoch=12/120 step=300/637 loss=1.67512 lr=0.00005954
+2026-06-27 16:59:46 epoch=12/120 step=350/637 loss=1.66760 lr=0.00005953
+2026-06-27 16:59:51 epoch=12/120 step=400/637 loss=1.65904 lr=0.00005951
+2026-06-27 16:59:57 epoch=12/120 step=450/637 loss=1.65636 lr=0.00005950
+2026-06-27 17:00:02 epoch=12/120 step=500/637 loss=1.66746 lr=0.00005949
+2026-06-27 17:00:08 epoch=12/120 step=550/637 loss=1.66373 lr=0.00005948
+2026-06-27 17:00:13 epoch=12/120 step=600/637 loss=1.66616 lr=0.00005947
+2026-06-27 17:00:26 convnext val mIoU=0.51114 background=0.59538 live_coral=0.62210 dead_coral=0.35926 bleached_coral=0.46782
+2026-06-27 17:00:29 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 17:00:34 epoch=13/120 step=50/637 loss=1.64110 lr=0.00005945
+2026-06-27 17:00:40 epoch=13/120 step=100/637 loss=1.65301 lr=0.00005943
+2026-06-27 17:00:45 epoch=13/120 step=150/637 loss=1.67143 lr=0.00005942
+2026-06-27 17:00:51 epoch=13/120 step=200/637 loss=1.64450 lr=0.00005941
+2026-06-27 17:00:56 epoch=13/120 step=250/637 loss=1.64939 lr=0.00005940
+2026-06-27 17:01:02 epoch=13/120 step=300/637 loss=1.65600 lr=0.00005938
+2026-06-27 17:01:07 epoch=13/120 step=350/637 loss=1.65503 lr=0.00005937
+2026-06-27 17:01:13 epoch=13/120 step=400/637 loss=1.64946 lr=0.00005936
+2026-06-27 17:01:18 epoch=13/120 step=450/637 loss=1.65004 lr=0.00005934
+2026-06-27 17:01:23 epoch=13/120 step=500/637 loss=1.65306 lr=0.00005933
+2026-06-27 17:01:29 epoch=13/120 step=550/637 loss=1.65212 lr=0.00005932
+2026-06-27 17:01:34 epoch=13/120 step=600/637 loss=1.66047 lr=0.00005930
+2026-06-27 17:01:47 convnext val mIoU=0.51252 background=0.54866 live_coral=0.64104 dead_coral=0.38832 bleached_coral=0.47205
+2026-06-27 17:01:50 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 17:01:55 epoch=14/120 step=50/637 loss=1.62041 lr=0.00005928
+2026-06-27 17:02:01 epoch=14/120 step=100/637 loss=1.61067 lr=0.00005927
+2026-06-27 17:02:06 epoch=14/120 step=150/637 loss=1.59621 lr=0.00005925
+2026-06-27 17:02:11 epoch=14/120 step=200/637 loss=1.58711 lr=0.00005924
+2026-06-27 17:02:17 epoch=14/120 step=250/637 loss=1.60710 lr=0.00005922
+2026-06-27 17:02:22 epoch=14/120 step=300/637 loss=1.61092 lr=0.00005921
+2026-06-27 17:02:28 epoch=14/120 step=350/637 loss=1.60323 lr=0.00005919
+2026-06-27 17:02:33 epoch=14/120 step=400/637 loss=1.61289 lr=0.00005918
+2026-06-27 17:02:39 epoch=14/120 step=450/637 loss=1.60835 lr=0.00005916
+2026-06-27 17:02:44 epoch=14/120 step=500/637 loss=1.60288 lr=0.00005915
+2026-06-27 17:02:49 epoch=14/120 step=550/637 loss=1.61208 lr=0.00005913
+2026-06-27 17:02:55 epoch=14/120 step=600/637 loss=1.61196 lr=0.00005912
+2026-06-27 17:03:07 convnext val mIoU=0.52261 background=0.59098 live_coral=0.64045 dead_coral=0.38630 bleached_coral=0.47270
+2026-06-27 17:03:10 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 17:03:16 epoch=15/120 step=50/637 loss=1.56531 lr=0.00005909
+2026-06-27 17:03:21 epoch=15/120 step=100/637 loss=1.57571 lr=0.00005908
+2026-06-27 17:03:26 epoch=15/120 step=150/637 loss=1.59090 lr=0.00005906
+2026-06-27 17:03:32 epoch=15/120 step=200/637 loss=1.60853 lr=0.00005904
+2026-06-27 17:03:37 epoch=15/120 step=250/637 loss=1.59899 lr=0.00005903
+2026-06-27 17:03:43 epoch=15/120 step=300/637 loss=1.60405 lr=0.00005901
+2026-06-27 17:03:48 epoch=15/120 step=350/637 loss=1.60679 lr=0.00005900
+2026-06-27 17:03:54 epoch=15/120 step=400/637 loss=1.60722 lr=0.00005898
+2026-06-27 17:03:59 epoch=15/120 step=450/637 loss=1.60667 lr=0.00005896
+2026-06-27 17:04:05 epoch=15/120 step=500/637 loss=1.60800 lr=0.00005895
+2026-06-27 17:04:10 epoch=15/120 step=550/637 loss=1.60813 lr=0.00005893
+2026-06-27 17:04:15 epoch=15/120 step=600/637 loss=1.60588 lr=0.00005891
+2026-06-27 17:04:28 convnext val mIoU=0.52226 background=0.61015 live_coral=0.64759 dead_coral=0.35898 bleached_coral=0.47230
+2026-06-27 17:04:35 epoch=16/120 step=50/637 loss=1.52667 lr=0.00005888
+2026-06-27 17:04:40 epoch=16/120 step=100/637 loss=1.56974 lr=0.00005886
+2026-06-27 17:04:46 epoch=16/120 step=150/637 loss=1.58300 lr=0.00005885
+2026-06-27 17:04:51 epoch=16/120 step=200/637 loss=1.56853 lr=0.00005883
+2026-06-27 17:04:57 epoch=16/120 step=250/637 loss=1.58424 lr=0.00005881
+2026-06-27 17:05:02 epoch=16/120 step=300/637 loss=1.59103 lr=0.00005879
+2026-06-27 17:05:07 epoch=16/120 step=350/637 loss=1.58680 lr=0.00005878
+2026-06-27 17:05:13 epoch=16/120 step=400/637 loss=1.58776 lr=0.00005876
+2026-06-27 17:05:18 epoch=16/120 step=450/637 loss=1.59114 lr=0.00005874
+2026-06-27 17:05:24 epoch=16/120 step=500/637 loss=1.59523 lr=0.00005872
+2026-06-27 17:05:29 epoch=16/120 step=550/637 loss=1.59818 lr=0.00005870
+2026-06-27 17:05:35 epoch=16/120 step=600/637 loss=1.59315 lr=0.00005868
+2026-06-27 17:05:47 convnext val mIoU=0.52871 background=0.60869 live_coral=0.64227 dead_coral=0.38199 bleached_coral=0.48188
+2026-06-27 17:05:49 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 17:05:55 epoch=17/120 step=50/637 loss=1.49518 lr=0.00005865
+2026-06-27 17:06:00 epoch=17/120 step=100/637 loss=1.53556 lr=0.00005863
+2026-06-27 17:06:06 epoch=17/120 step=150/637 loss=1.53052 lr=0.00005861
+2026-06-27 17:06:11 epoch=17/120 step=200/637 loss=1.52527 lr=0.00005859
+2026-06-27 17:06:17 epoch=17/120 step=250/637 loss=1.53132 lr=0.00005857
+2026-06-27 17:06:22 epoch=17/120 step=300/637 loss=1.53153 lr=0.00005855
+2026-06-27 17:06:28 epoch=17/120 step=350/637 loss=1.53985 lr=0.00005853
+2026-06-27 17:06:33 epoch=17/120 step=400/637 loss=1.54232 lr=0.00005851
+2026-06-27 17:06:39 epoch=17/120 step=450/637 loss=1.55783 lr=0.00005849
+2026-06-27 17:06:44 epoch=17/120 step=500/637 loss=1.56125 lr=0.00005847
+2026-06-27 17:06:50 epoch=17/120 step=550/637 loss=1.56919 lr=0.00005845
+2026-06-27 17:06:55 epoch=17/120 step=600/637 loss=1.57732 lr=0.00005843
+2026-06-27 17:07:08 convnext val mIoU=0.51369 background=0.62189 live_coral=0.64013 dead_coral=0.33644 bleached_coral=0.45629
+2026-06-27 17:07:14 epoch=18/120 step=50/637 loss=1.54042 lr=0.00005840
+2026-06-27 17:07:20 epoch=18/120 step=100/637 loss=1.55881 lr=0.00005838
+2026-06-27 17:07:25 epoch=18/120 step=150/637 loss=1.57016 lr=0.00005836
+2026-06-27 17:07:31 epoch=18/120 step=200/637 loss=1.56660 lr=0.00005834
+2026-06-27 17:07:36 epoch=18/120 step=250/637 loss=1.57070 lr=0.00005831
+2026-06-27 17:07:41 epoch=18/120 step=300/637 loss=1.56162 lr=0.00005829
+2026-06-27 17:07:47 epoch=18/120 step=350/637 loss=1.56114 lr=0.00005827
+2026-06-27 17:07:52 epoch=18/120 step=400/637 loss=1.56066 lr=0.00005825
+2026-06-27 17:07:58 epoch=18/120 step=450/637 loss=1.55983 lr=0.00005823
+2026-06-27 17:08:03 epoch=18/120 step=500/637 loss=1.56063 lr=0.00005821
+2026-06-27 17:08:08 epoch=18/120 step=550/637 loss=1.56608 lr=0.00005819
+2026-06-27 17:08:14 epoch=18/120 step=600/637 loss=1.56039 lr=0.00005816
+2026-06-27 17:08:26 convnext val mIoU=0.52540 background=0.60777 live_coral=0.65731 dead_coral=0.36699 bleached_coral=0.46953
+2026-06-27 17:08:33 epoch=19/120 step=50/637 loss=1.54405 lr=0.00005812
+2026-06-27 17:08:38 epoch=19/120 step=100/637 loss=1.55228 lr=0.00005810
+2026-06-27 17:08:44 epoch=19/120 step=150/637 loss=1.55401 lr=0.00005808
+2026-06-27 17:08:49 epoch=19/120 step=200/637 loss=1.54318 lr=0.00005806
+2026-06-27 17:08:55 epoch=19/120 step=250/637 loss=1.54947 lr=0.00005803
+2026-06-27 17:09:00 epoch=19/120 step=300/637 loss=1.53280 lr=0.00005801
+2026-06-27 17:09:06 epoch=19/120 step=350/637 loss=1.54269 lr=0.00005799
+2026-06-27 17:09:11 epoch=19/120 step=400/637 loss=1.53947 lr=0.00005797
+2026-06-27 17:09:17 epoch=19/120 step=450/637 loss=1.53800 lr=0.00005794
+2026-06-27 17:09:22 epoch=19/120 step=500/637 loss=1.54258 lr=0.00005792
+2026-06-27 17:09:28 epoch=19/120 step=550/637 loss=1.53908 lr=0.00005790
+2026-06-27 17:09:33 epoch=19/120 step=600/637 loss=1.54149 lr=0.00005787
+2026-06-27 17:09:45 convnext val mIoU=0.52951 background=0.61443 live_coral=0.66059 dead_coral=0.35627 bleached_coral=0.48674
+2026-06-27 17:09:48 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 17:09:53 epoch=20/120 step=50/637 loss=1.58873 lr=0.00005783
+2026-06-27 17:09:59 epoch=20/120 step=100/637 loss=1.55422 lr=0.00005781
+2026-06-27 17:10:04 epoch=20/120 step=150/637 loss=1.54930 lr=0.00005778
+2026-06-27 17:10:10 epoch=20/120 step=200/637 loss=1.57520 lr=0.00005776
+2026-06-27 17:10:15 epoch=20/120 step=250/637 loss=1.55383 lr=0.00005773
+2026-06-27 17:10:20 epoch=20/120 step=300/637 loss=1.54085 lr=0.00005771
+2026-06-27 17:10:26 epoch=20/120 step=350/637 loss=1.52557 lr=0.00005768
+2026-06-27 17:10:31 epoch=20/120 step=400/637 loss=1.52698 lr=0.00005766
+2026-06-27 17:10:37 epoch=20/120 step=450/637 loss=1.52552 lr=0.00005764
+2026-06-27 17:10:42 epoch=20/120 step=500/637 loss=1.52640 lr=0.00005761
+2026-06-27 17:10:48 epoch=20/120 step=550/637 loss=1.52638 lr=0.00005759
+2026-06-27 17:10:53 epoch=20/120 step=600/637 loss=1.52162 lr=0.00005756
+2026-06-27 17:11:05 convnext val mIoU=0.54391 background=0.61017 live_coral=0.65486 dead_coral=0.41029 bleached_coral=0.50032
+2026-06-27 17:11:08 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 17:11:14 epoch=21/120 step=50/637 loss=1.46143 lr=0.00005752
+2026-06-27 17:11:19 epoch=21/120 step=100/637 loss=1.45457 lr=0.00005749
+2026-06-27 17:11:25 epoch=21/120 step=150/637 loss=1.45926 lr=0.00005746
+2026-06-27 17:11:30 epoch=21/120 step=200/637 loss=1.47063 lr=0.00005744
+2026-06-27 17:11:36 epoch=21/120 step=250/637 loss=1.46811 lr=0.00005741
+2026-06-27 17:11:41 epoch=21/120 step=300/637 loss=1.46136 lr=0.00005739
+2026-06-27 17:11:47 epoch=21/120 step=350/637 loss=1.46851 lr=0.00005736
+2026-06-27 17:11:52 epoch=21/120 step=400/637 loss=1.48709 lr=0.00005733
+2026-06-27 17:11:58 epoch=21/120 step=450/637 loss=1.49066 lr=0.00005731
+2026-06-27 17:12:03 epoch=21/120 step=500/637 loss=1.49467 lr=0.00005728
+2026-06-27 17:12:08 epoch=21/120 step=550/637 loss=1.50147 lr=0.00005725
+2026-06-27 17:12:14 epoch=21/120 step=600/637 loss=1.49920 lr=0.00005723
+2026-06-27 17:12:26 convnext val mIoU=0.54488 background=0.58405 live_coral=0.65995 dead_coral=0.43378 bleached_coral=0.50173
+2026-06-27 17:12:29 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 17:12:34 epoch=22/120 step=50/637 loss=1.46779 lr=0.00005718
+2026-06-27 17:12:40 epoch=22/120 step=100/637 loss=1.50638 lr=0.00005715
+2026-06-27 17:12:45 epoch=22/120 step=150/637 loss=1.49467 lr=0.00005713
+2026-06-27 17:12:51 epoch=22/120 step=200/637 loss=1.51106 lr=0.00005710
+2026-06-27 17:12:56 epoch=22/120 step=250/637 loss=1.50526 lr=0.00005707
+2026-06-27 17:13:02 epoch=22/120 step=300/637 loss=1.49841 lr=0.00005704
+2026-06-27 17:13:07 epoch=22/120 step=350/637 loss=1.49432 lr=0.00005702
+2026-06-27 17:13:12 epoch=22/120 step=400/637 loss=1.49621 lr=0.00005699
+2026-06-27 17:13:18 epoch=22/120 step=450/637 loss=1.49277 lr=0.00005696
+2026-06-27 17:13:23 epoch=22/120 step=500/637 loss=1.49720 lr=0.00005693
+2026-06-27 17:13:29 epoch=22/120 step=550/637 loss=1.49536 lr=0.00005690
+2026-06-27 17:13:34 epoch=22/120 step=600/637 loss=1.49691 lr=0.00005688
+2026-06-27 17:13:46 convnext val mIoU=0.54213 background=0.62342 live_coral=0.67430 dead_coral=0.38709 bleached_coral=0.48372
+2026-06-27 17:13:54 epoch=23/120 step=50/637 loss=1.48626 lr=0.00005683
+2026-06-27 17:13:59 epoch=23/120 step=100/637 loss=1.48466 lr=0.00005680
+2026-06-27 17:14:05 epoch=23/120 step=150/637 loss=1.49928 lr=0.00005677
+2026-06-27 17:14:10 epoch=23/120 step=200/637 loss=1.47336 lr=0.00005674
+2026-06-27 17:14:16 epoch=23/120 step=250/637 loss=1.47668 lr=0.00005671
+2026-06-27 17:14:21 epoch=23/120 step=300/637 loss=1.47768 lr=0.00005668
+2026-06-27 17:14:26 epoch=23/120 step=350/637 loss=1.46518 lr=0.00005665
+2026-06-27 17:14:32 epoch=23/120 step=400/637 loss=1.46397 lr=0.00005662
+2026-06-27 17:14:37 epoch=23/120 step=450/637 loss=1.46726 lr=0.00005659
+2026-06-27 17:14:43 epoch=23/120 step=500/637 loss=1.46920 lr=0.00005656
+2026-06-27 17:14:48 epoch=23/120 step=550/637 loss=1.46937 lr=0.00005653
+2026-06-27 17:14:54 epoch=23/120 step=600/637 loss=1.46994 lr=0.00005650
+2026-06-27 17:15:06 convnext val mIoU=0.55126 background=0.62064 live_coral=0.66634 dead_coral=0.41884 bleached_coral=0.49921
+2026-06-27 17:15:08 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 17:15:14 epoch=24/120 step=50/637 loss=1.44043 lr=0.00005645
+2026-06-27 17:15:19 epoch=24/120 step=100/637 loss=1.44815 lr=0.00005642
+2026-06-27 17:15:25 epoch=24/120 step=150/637 loss=1.45350 lr=0.00005639
+2026-06-27 17:15:30 epoch=24/120 step=200/637 loss=1.45293 lr=0.00005636
+2026-06-27 17:15:35 epoch=24/120 step=250/637 loss=1.46072 lr=0.00005633
+2026-06-27 17:15:41 epoch=24/120 step=300/637 loss=1.45134 lr=0.00005630
+2026-06-27 17:15:46 epoch=24/120 step=350/637 loss=1.45783 lr=0.00005627
+2026-06-27 17:15:52 epoch=24/120 step=400/637 loss=1.46019 lr=0.00005624
+2026-06-27 17:15:57 epoch=24/120 step=450/637 loss=1.46036 lr=0.00005621
+2026-06-27 17:16:03 epoch=24/120 step=500/637 loss=1.46260 lr=0.00005617
+2026-06-27 17:16:08 epoch=24/120 step=550/637 loss=1.46178 lr=0.00005614
+2026-06-27 17:16:13 epoch=24/120 step=600/637 loss=1.46537 lr=0.00005611
+2026-06-27 17:16:26 convnext val mIoU=0.55777 background=0.62227 live_coral=0.67535 dead_coral=0.41681 bleached_coral=0.51666
+2026-06-27 17:16:28 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 17:16:34 epoch=25/120 step=50/637 loss=1.37337 lr=0.00005606
+2026-06-27 17:16:39 epoch=25/120 step=100/637 loss=1.37468 lr=0.00005602
+2026-06-27 17:16:45 epoch=25/120 step=150/637 loss=1.38385 lr=0.00005599
+2026-06-27 17:16:50 epoch=25/120 step=200/637 loss=1.40118 lr=0.00005596
+2026-06-27 17:16:56 epoch=25/120 step=250/637 loss=1.42727 lr=0.00005593
+2026-06-27 17:17:01 epoch=25/120 step=300/637 loss=1.44464 lr=0.00005590
+2026-06-27 17:17:07 epoch=25/120 step=350/637 loss=1.44553 lr=0.00005586
+2026-06-27 17:17:12 epoch=25/120 step=400/637 loss=1.45617 lr=0.00005583
+2026-06-27 17:17:18 epoch=25/120 step=450/637 loss=1.46131 lr=0.00005580
+2026-06-27 17:17:23 epoch=25/120 step=500/637 loss=1.46286 lr=0.00005577
+2026-06-27 17:17:28 epoch=25/120 step=550/637 loss=1.46263 lr=0.00005573
+2026-06-27 17:17:34 epoch=25/120 step=600/637 loss=1.46482 lr=0.00005570
+2026-06-27 17:17:46 convnext val mIoU=0.55593 background=0.61300 live_coral=0.67009 dead_coral=0.43019 bleached_coral=0.51045
+2026-06-27 17:17:53 epoch=26/120 step=50/637 loss=1.42888 lr=0.00005564
+2026-06-27 17:17:58 epoch=26/120 step=100/637 loss=1.41785 lr=0.00005561
+2026-06-27 17:18:04 epoch=26/120 step=150/637 loss=1.42521 lr=0.00005558
+2026-06-27 17:18:09 epoch=26/120 step=200/637 loss=1.42554 lr=0.00005554
+2026-06-27 17:18:15 epoch=26/120 step=250/637 loss=1.44401 lr=0.00005551
+2026-06-27 17:18:20 epoch=26/120 step=300/637 loss=1.44758 lr=0.00005548
+2026-06-27 17:18:25 epoch=26/120 step=350/637 loss=1.44931 lr=0.00005544
+2026-06-27 17:18:31 epoch=26/120 step=400/637 loss=1.45623 lr=0.00005541
+2026-06-27 17:18:36 epoch=26/120 step=450/637 loss=1.45612 lr=0.00005537
+2026-06-27 17:18:42 epoch=26/120 step=500/637 loss=1.45168 lr=0.00005534
+2026-06-27 17:18:47 epoch=26/120 step=550/637 loss=1.45283 lr=0.00005531
+2026-06-27 17:18:53 epoch=26/120 step=600/637 loss=1.44617 lr=0.00005527
+2026-06-27 17:19:05 convnext val mIoU=0.55148 background=0.62378 live_coral=0.67011 dead_coral=0.41714 bleached_coral=0.49489
+2026-06-27 17:19:12 epoch=27/120 step=50/637 loss=1.50984 lr=0.00005521
+2026-06-27 17:19:17 epoch=27/120 step=100/637 loss=1.48761 lr=0.00005518
+2026-06-27 17:19:23 epoch=27/120 step=150/637 loss=1.43005 lr=0.00005514
+2026-06-27 17:19:28 epoch=27/120 step=200/637 loss=1.44560 lr=0.00005511
+2026-06-27 17:19:34 epoch=27/120 step=250/637 loss=1.45504 lr=0.00005507
+2026-06-27 17:19:39 epoch=27/120 step=300/637 loss=1.43330 lr=0.00005504
+2026-06-27 17:19:44 epoch=27/120 step=350/637 loss=1.41859 lr=0.00005500
+2026-06-27 17:19:50 epoch=27/120 step=400/637 loss=1.42413 lr=0.00005497
+2026-06-27 17:19:55 epoch=27/120 step=450/637 loss=1.41872 lr=0.00005493
+2026-06-27 17:20:01 epoch=27/120 step=500/637 loss=1.41215 lr=0.00005489
+2026-06-27 17:20:06 epoch=27/120 step=550/637 loss=1.41908 lr=0.00005486
+2026-06-27 17:20:12 epoch=27/120 step=600/637 loss=1.42007 lr=0.00005482
+2026-06-27 17:20:24 convnext val mIoU=0.55687 background=0.61848 live_coral=0.67695 dead_coral=0.42486 bleached_coral=0.50719
+2026-06-27 17:20:31 epoch=28/120 step=50/637 loss=1.40786 lr=0.00005476
+2026-06-27 17:20:37 epoch=28/120 step=100/637 loss=1.46938 lr=0.00005472
+2026-06-27 17:20:42 epoch=28/120 step=150/637 loss=1.43269 lr=0.00005469
+2026-06-27 17:20:47 epoch=28/120 step=200/637 loss=1.43088 lr=0.00005465
+2026-06-27 17:20:53 epoch=28/120 step=250/637 loss=1.43325 lr=0.00005461
+2026-06-27 17:20:58 epoch=28/120 step=300/637 loss=1.43535 lr=0.00005458
+2026-06-27 17:21:04 epoch=28/120 step=350/637 loss=1.42885 lr=0.00005454
+2026-06-27 17:21:09 epoch=28/120 step=400/637 loss=1.42118 lr=0.00005450
+2026-06-27 17:21:14 epoch=28/120 step=450/637 loss=1.41354 lr=0.00005447
+2026-06-27 17:21:20 epoch=28/120 step=500/637 loss=1.41672 lr=0.00005443
+2026-06-27 17:21:25 epoch=28/120 step=550/637 loss=1.42154 lr=0.00005439
+2026-06-27 17:21:31 epoch=28/120 step=600/637 loss=1.41865 lr=0.00005436
+2026-06-27 17:21:43 convnext val mIoU=0.56122 background=0.60991 live_coral=0.67602 dead_coral=0.44281 bleached_coral=0.51614
+2026-06-27 17:21:46 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 17:21:51 epoch=29/120 step=50/637 loss=1.36301 lr=0.00005429
+2026-06-27 17:21:57 epoch=29/120 step=100/637 loss=1.40392 lr=0.00005425
+2026-06-27 17:22:02 epoch=29/120 step=150/637 loss=1.39958 lr=0.00005422
+2026-06-27 17:22:07 epoch=29/120 step=200/637 loss=1.41191 lr=0.00005418
+2026-06-27 17:22:13 epoch=29/120 step=250/637 loss=1.42695 lr=0.00005414
+2026-06-27 17:22:18 epoch=29/120 step=300/637 loss=1.42783 lr=0.00005410
+2026-06-27 17:22:24 epoch=29/120 step=350/637 loss=1.41827 lr=0.00005406
+2026-06-27 17:22:29 epoch=29/120 step=400/637 loss=1.41982 lr=0.00005402
+2026-06-27 17:22:35 epoch=29/120 step=450/637 loss=1.42098 lr=0.00005399
+2026-06-27 17:22:40 epoch=29/120 step=500/637 loss=1.41141 lr=0.00005395
+2026-06-27 17:22:46 epoch=29/120 step=550/637 loss=1.41793 lr=0.00005391
+2026-06-27 17:22:51 epoch=29/120 step=600/637 loss=1.41303 lr=0.00005387
+2026-06-27 17:23:03 convnext val mIoU=0.55985 background=0.60934 live_coral=0.67947 dead_coral=0.43734 bleached_coral=0.51324
+2026-06-27 17:23:10 epoch=30/120 step=50/637 loss=1.31146 lr=0.00005380
+2026-06-27 17:23:15 epoch=30/120 step=100/637 loss=1.37230 lr=0.00005376
+2026-06-27 17:23:21 epoch=30/120 step=150/637 loss=1.35606 lr=0.00005373
+2026-06-27 17:23:26 epoch=30/120 step=200/637 loss=1.36234 lr=0.00005369
+2026-06-27 17:23:32 epoch=30/120 step=250/637 loss=1.37019 lr=0.00005365
+2026-06-27 17:23:37 epoch=30/120 step=300/637 loss=1.38200 lr=0.00005361
+2026-06-27 17:23:42 epoch=30/120 step=350/637 loss=1.38426 lr=0.00005357
+2026-06-27 17:23:48 epoch=30/120 step=400/637 loss=1.38223 lr=0.00005353
+2026-06-27 17:23:53 epoch=30/120 step=450/637 loss=1.38648 lr=0.00005349
+2026-06-27 17:23:59 epoch=30/120 step=500/637 loss=1.37771 lr=0.00005345
+2026-06-27 17:24:04 epoch=30/120 step=550/637 loss=1.37341 lr=0.00005341
+2026-06-27 17:24:10 epoch=30/120 step=600/637 loss=1.37099 lr=0.00005337
+2026-06-27 17:24:22 convnext val mIoU=0.56043 background=0.61940 live_coral=0.68410 dead_coral=0.42094 bleached_coral=0.51729
+2026-06-27 17:24:29 epoch=31/120 step=50/637 loss=1.38406 lr=0.00005330
+2026-06-27 17:24:34 epoch=31/120 step=100/637 loss=1.37743 lr=0.00005326
+2026-06-27 17:24:40 epoch=31/120 step=150/637 loss=1.39364 lr=0.00005322
+2026-06-27 17:24:45 epoch=31/120 step=200/637 loss=1.39162 lr=0.00005318
+2026-06-27 17:24:51 epoch=31/120 step=250/637 loss=1.39222 lr=0.00005314
+2026-06-27 17:24:56 epoch=31/120 step=300/637 loss=1.38927 lr=0.00005310
+2026-06-27 17:25:01 epoch=31/120 step=350/637 loss=1.38595 lr=0.00005305
+2026-06-27 17:25:07 epoch=31/120 step=400/637 loss=1.37102 lr=0.00005301
+2026-06-27 17:25:12 epoch=31/120 step=450/637 loss=1.36794 lr=0.00005297
+2026-06-27 17:25:18 epoch=31/120 step=500/637 loss=1.36231 lr=0.00005293
+2026-06-27 17:25:23 epoch=31/120 step=550/637 loss=1.36066 lr=0.00005289
+2026-06-27 17:25:29 epoch=31/120 step=600/637 loss=1.36199 lr=0.00005285
+2026-06-27 17:25:41 convnext val mIoU=0.53928 background=0.61628 live_coral=0.66041 dead_coral=0.39149 bleached_coral=0.48894
+2026-06-27 17:25:48 epoch=32/120 step=50/637 loss=1.39381 lr=0.00005278
+2026-06-27 17:25:53 epoch=32/120 step=100/637 loss=1.38669 lr=0.00005273
+2026-06-27 17:25:59 epoch=32/120 step=150/637 loss=1.39170 lr=0.00005269
+2026-06-27 17:26:04 epoch=32/120 step=200/637 loss=1.40000 lr=0.00005265
+2026-06-27 17:26:09 epoch=32/120 step=250/637 loss=1.37346 lr=0.00005261
+2026-06-27 17:26:15 epoch=32/120 step=300/637 loss=1.36992 lr=0.00005257
+2026-06-27 17:26:20 epoch=32/120 step=350/637 loss=1.36183 lr=0.00005252
+2026-06-27 17:26:26 epoch=32/120 step=400/637 loss=1.35995 lr=0.00005248
+2026-06-27 17:26:31 epoch=32/120 step=450/637 loss=1.36580 lr=0.00005244
+2026-06-27 17:26:37 epoch=32/120 step=500/637 loss=1.36549 lr=0.00005240
+2026-06-27 17:26:42 epoch=32/120 step=550/637 loss=1.37155 lr=0.00005236
+2026-06-27 17:26:48 epoch=32/120 step=600/637 loss=1.37230 lr=0.00005231
+2026-06-27 17:27:00 convnext val mIoU=0.56100 background=0.61751 live_coral=0.67216 dead_coral=0.44180 bleached_coral=0.51254
+2026-06-27 17:27:07 epoch=33/120 step=50/637 loss=1.35650 lr=0.00005224
+2026-06-27 17:27:12 epoch=33/120 step=100/637 loss=1.36610 lr=0.00005219
+2026-06-27 17:27:18 epoch=33/120 step=150/637 loss=1.34711 lr=0.00005215
+2026-06-27 17:27:23 epoch=33/120 step=200/637 loss=1.35296 lr=0.00005211
+2026-06-27 17:27:29 epoch=33/120 step=250/637 loss=1.34765 lr=0.00005207
+2026-06-27 17:27:34 epoch=33/120 step=300/637 loss=1.34501 lr=0.00005202
+2026-06-27 17:27:39 epoch=33/120 step=350/637 loss=1.34012 lr=0.00005198
+2026-06-27 17:27:45 epoch=33/120 step=400/637 loss=1.33088 lr=0.00005193
+2026-06-27 17:27:50 epoch=33/120 step=450/637 loss=1.34255 lr=0.00005189
+2026-06-27 17:27:56 epoch=33/120 step=500/637 loss=1.34609 lr=0.00005185
+2026-06-27 17:28:01 epoch=33/120 step=550/637 loss=1.35288 lr=0.00005180
+2026-06-27 17:28:07 epoch=33/120 step=600/637 loss=1.35001 lr=0.00005176
+2026-06-27 17:28:19 convnext val mIoU=0.55825 background=0.61631 live_coral=0.68301 dead_coral=0.41029 bleached_coral=0.52337
+2026-06-27 17:28:26 epoch=34/120 step=50/637 loss=1.31671 lr=0.00005168
+2026-06-27 17:28:31 epoch=34/120 step=100/637 loss=1.32547 lr=0.00005164
+2026-06-27 17:28:37 epoch=34/120 step=150/637 loss=1.33880 lr=0.00005159
+2026-06-27 17:28:42 epoch=34/120 step=200/637 loss=1.33614 lr=0.00005155
+2026-06-27 17:28:48 epoch=34/120 step=250/637 loss=1.32749 lr=0.00005151
+2026-06-27 17:28:53 epoch=34/120 step=300/637 loss=1.31718 lr=0.00005146
+2026-06-27 17:28:58 epoch=34/120 step=350/637 loss=1.32815 lr=0.00005142
+2026-06-27 17:29:04 epoch=34/120 step=400/637 loss=1.32320 lr=0.00005137
+2026-06-27 17:29:09 epoch=34/120 step=450/637 loss=1.33147 lr=0.00005133
+2026-06-27 17:29:15 epoch=34/120 step=500/637 loss=1.32692 lr=0.00005128
+2026-06-27 17:29:20 epoch=34/120 step=550/637 loss=1.32429 lr=0.00005124
+2026-06-27 17:29:26 epoch=34/120 step=600/637 loss=1.32165 lr=0.00005119
+2026-06-27 17:29:38 convnext val mIoU=0.54695 background=0.61248 live_coral=0.67423 dead_coral=0.39842 bleached_coral=0.50266
+2026-06-27 17:29:45 epoch=35/120 step=50/637 loss=1.41653 lr=0.00005111
+2026-06-27 17:29:50 epoch=35/120 step=100/637 loss=1.35350 lr=0.00005107
+2026-06-27 17:29:56 epoch=35/120 step=150/637 loss=1.31216 lr=0.00005102
+2026-06-27 17:30:01 epoch=35/120 step=200/637 loss=1.31864 lr=0.00005098
+2026-06-27 17:30:07 epoch=35/120 step=250/637 loss=1.31405 lr=0.00005093
+2026-06-27 17:30:12 epoch=35/120 step=300/637 loss=1.32536 lr=0.00005088
+2026-06-27 17:30:18 epoch=35/120 step=350/637 loss=1.32250 lr=0.00005084
+2026-06-27 17:30:23 epoch=35/120 step=400/637 loss=1.31314 lr=0.00005079
+2026-06-27 17:30:28 epoch=35/120 step=450/637 loss=1.31349 lr=0.00005075
+2026-06-27 17:30:34 epoch=35/120 step=500/637 loss=1.31696 lr=0.00005070
+2026-06-27 17:30:39 epoch=35/120 step=550/637 loss=1.31015 lr=0.00005065
+2026-06-27 17:30:45 epoch=35/120 step=600/637 loss=1.30380 lr=0.00005061
+2026-06-27 17:30:57 convnext val mIoU=0.55992 background=0.62595 live_coral=0.68246 dead_coral=0.42089 bleached_coral=0.51040
+2026-06-27 17:31:04 epoch=36/120 step=50/637 loss=1.35306 lr=0.00005053
+2026-06-27 17:31:09 epoch=36/120 step=100/637 loss=1.32435 lr=0.00005048
+2026-06-27 17:31:15 epoch=36/120 step=150/637 loss=1.31814 lr=0.00005043
+2026-06-27 17:31:20 epoch=36/120 step=200/637 loss=1.31016 lr=0.00005038
+2026-06-27 17:31:25 epoch=36/120 step=250/637 loss=1.31473 lr=0.00005034
+2026-06-27 17:31:31 epoch=36/120 step=300/637 loss=1.31130 lr=0.00005029
+2026-06-27 17:31:36 epoch=36/120 step=350/637 loss=1.30161 lr=0.00005024
+2026-06-27 17:31:42 epoch=36/120 step=400/637 loss=1.30631 lr=0.00005020
+2026-06-27 17:31:47 epoch=36/120 step=450/637 loss=1.30809 lr=0.00005015
+2026-06-27 17:31:53 epoch=36/120 step=500/637 loss=1.31888 lr=0.00005010
+2026-06-27 17:31:58 epoch=36/120 step=550/637 loss=1.31266 lr=0.00005005
+2026-06-27 17:32:03 epoch=36/120 step=600/637 loss=1.30896 lr=0.00005001
+2026-06-27 17:32:16 convnext val mIoU=0.57094 background=0.62017 live_coral=0.68442 dead_coral=0.45039 bleached_coral=0.52878
+2026-06-27 17:32:18 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 17:32:24 epoch=37/120 step=50/637 loss=1.30166 lr=0.00004992
+2026-06-27 17:32:29 epoch=37/120 step=100/637 loss=1.26420 lr=0.00004988
+2026-06-27 17:32:35 epoch=37/120 step=150/637 loss=1.26262 lr=0.00004983
+2026-06-27 17:32:40 epoch=37/120 step=200/637 loss=1.26957 lr=0.00004978
+2026-06-27 17:32:45 epoch=37/120 step=250/637 loss=1.26686 lr=0.00004973
+2026-06-27 17:32:51 epoch=37/120 step=300/637 loss=1.27601 lr=0.00004968
+2026-06-27 17:32:56 epoch=37/120 step=350/637 loss=1.29568 lr=0.00004964
+2026-06-27 17:33:02 epoch=37/120 step=400/637 loss=1.30204 lr=0.00004959
+2026-06-27 17:33:07 epoch=37/120 step=450/637 loss=1.30153 lr=0.00004954
+2026-06-27 17:33:13 epoch=37/120 step=500/637 loss=1.30636 lr=0.00004949
+2026-06-27 17:33:18 epoch=37/120 step=550/637 loss=1.30353 lr=0.00004944
+2026-06-27 17:33:23 epoch=37/120 step=600/637 loss=1.30245 lr=0.00004939
+2026-06-27 17:33:36 convnext val mIoU=0.56347 background=0.61226 live_coral=0.67260 dead_coral=0.45622 bleached_coral=0.51280
+2026-06-27 17:33:42 epoch=38/120 step=50/637 loss=1.31431 lr=0.00004931
+2026-06-27 17:33:48 epoch=38/120 step=100/637 loss=1.30447 lr=0.00004926
+2026-06-27 17:33:53 epoch=38/120 step=150/637 loss=1.31042 lr=0.00004921
+2026-06-27 17:33:59 epoch=38/120 step=200/637 loss=1.31310 lr=0.00004916
+2026-06-27 17:34:04 epoch=38/120 step=250/637 loss=1.31472 lr=0.00004911
+2026-06-27 17:34:09 epoch=38/120 step=300/637 loss=1.30869 lr=0.00004906
+2026-06-27 17:34:15 epoch=38/120 step=350/637 loss=1.29875 lr=0.00004901
+2026-06-27 17:34:20 epoch=38/120 step=400/637 loss=1.29295 lr=0.00004896
+2026-06-27 17:34:26 epoch=38/120 step=450/637 loss=1.30343 lr=0.00004891
+2026-06-27 17:34:31 epoch=38/120 step=500/637 loss=1.29749 lr=0.00004886
+2026-06-27 17:34:37 epoch=38/120 step=550/637 loss=1.29547 lr=0.00004881
+2026-06-27 17:34:42 epoch=38/120 step=600/637 loss=1.28754 lr=0.00004876
+2026-06-27 17:34:55 convnext val mIoU=0.55803 background=0.59709 live_coral=0.69111 dead_coral=0.45263 bleached_coral=0.49127
+2026-06-27 17:35:02 epoch=39/120 step=50/637 loss=1.28541 lr=0.00004868
+2026-06-27 17:35:07 epoch=39/120 step=100/637 loss=1.26102 lr=0.00004863
+2026-06-27 17:35:12 epoch=39/120 step=150/637 loss=1.28139 lr=0.00004858
+2026-06-27 17:35:18 epoch=39/120 step=200/637 loss=1.26857 lr=0.00004853
+2026-06-27 17:35:23 epoch=39/120 step=250/637 loss=1.27309 lr=0.00004848
+2026-06-27 17:35:29 epoch=39/120 step=300/637 loss=1.26747 lr=0.00004843
+2026-06-27 17:35:34 epoch=39/120 step=350/637 loss=1.27709 lr=0.00004838
+2026-06-27 17:35:40 epoch=39/120 step=400/637 loss=1.26655 lr=0.00004832
+2026-06-27 17:35:45 epoch=39/120 step=450/637 loss=1.26518 lr=0.00004827
+2026-06-27 17:35:50 epoch=39/120 step=500/637 loss=1.27930 lr=0.00004822
+2026-06-27 17:35:56 epoch=39/120 step=550/637 loss=1.28787 lr=0.00004817
+2026-06-27 17:36:01 epoch=39/120 step=600/637 loss=1.28928 lr=0.00004812
+2026-06-27 17:36:14 convnext val mIoU=0.56339 background=0.62715 live_coral=0.68356 dead_coral=0.42922 bleached_coral=0.51363
+2026-06-27 17:36:20 epoch=40/120 step=50/637 loss=1.25336 lr=0.00004803
+2026-06-27 17:36:26 epoch=40/120 step=100/637 loss=1.26943 lr=0.00004798
+2026-06-27 17:36:31 epoch=40/120 step=150/637 loss=1.25735 lr=0.00004793
+2026-06-27 17:36:36 epoch=40/120 step=200/637 loss=1.25970 lr=0.00004788
+2026-06-27 17:36:42 epoch=40/120 step=250/637 loss=1.24892 lr=0.00004783
+2026-06-27 17:36:47 epoch=40/120 step=300/637 loss=1.25027 lr=0.00004778
+2026-06-27 17:36:53 epoch=40/120 step=350/637 loss=1.25633 lr=0.00004772
+2026-06-27 17:36:58 epoch=40/120 step=400/637 loss=1.26029 lr=0.00004767
+2026-06-27 17:37:04 epoch=40/120 step=450/637 loss=1.25906 lr=0.00004762
+2026-06-27 17:37:09 epoch=40/120 step=500/637 loss=1.26374 lr=0.00004757
+2026-06-27 17:37:15 epoch=40/120 step=550/637 loss=1.25806 lr=0.00004752
+2026-06-27 17:37:20 epoch=40/120 step=600/637 loss=1.25788 lr=0.00004747
+2026-06-27 17:37:32 convnext val mIoU=0.56303 background=0.61156 live_coral=0.67637 dead_coral=0.46491 bleached_coral=0.49928
+2026-06-27 17:37:39 epoch=41/120 step=50/637 loss=1.23226 lr=0.00004738
+2026-06-27 17:37:44 epoch=41/120 step=100/637 loss=1.23397 lr=0.00004732
+2026-06-27 17:37:50 epoch=41/120 step=150/637 loss=1.22633 lr=0.00004727
+2026-06-27 17:37:55 epoch=41/120 step=200/637 loss=1.21380 lr=0.00004722
+2026-06-27 17:38:01 epoch=41/120 step=250/637 loss=1.22141 lr=0.00004717
+2026-06-27 17:38:06 epoch=41/120 step=300/637 loss=1.20986 lr=0.00004711
+2026-06-27 17:38:11 epoch=41/120 step=350/637 loss=1.20978 lr=0.00004706
+2026-06-27 17:38:17 epoch=41/120 step=400/637 loss=1.21574 lr=0.00004701
+2026-06-27 17:38:22 epoch=41/120 step=450/637 loss=1.21594 lr=0.00004696
+2026-06-27 17:38:28 epoch=41/120 step=500/637 loss=1.23090 lr=0.00004690
+2026-06-27 17:38:33 epoch=41/120 step=550/637 loss=1.23171 lr=0.00004685
+2026-06-27 17:38:38 epoch=41/120 step=600/637 loss=1.23468 lr=0.00004680
+2026-06-27 17:38:51 convnext val mIoU=0.56611 background=0.61919 live_coral=0.68865 dead_coral=0.44966 bleached_coral=0.50692
+2026-06-27 17:38:57 epoch=42/120 step=50/637 loss=1.23582 lr=0.00004671
+2026-06-27 17:39:03 epoch=42/120 step=100/637 loss=1.28004 lr=0.00004665
+2026-06-27 17:39:08 epoch=42/120 step=150/637 loss=1.29159 lr=0.00004660
+2026-06-27 17:39:14 epoch=42/120 step=200/637 loss=1.27104 lr=0.00004655
+2026-06-27 17:39:19 epoch=42/120 step=250/637 loss=1.26715 lr=0.00004649
+2026-06-27 17:39:25 epoch=42/120 step=300/637 loss=1.27195 lr=0.00004644
+2026-06-27 17:39:30 epoch=42/120 step=350/637 loss=1.26517 lr=0.00004639
+2026-06-27 17:39:36 epoch=42/120 step=400/637 loss=1.26647 lr=0.00004633
+2026-06-27 17:39:41 epoch=42/120 step=450/637 loss=1.26846 lr=0.00004628
+2026-06-27 17:39:46 epoch=42/120 step=500/637 loss=1.26785 lr=0.00004622
+2026-06-27 17:39:52 epoch=42/120 step=550/637 loss=1.27091 lr=0.00004617
+2026-06-27 17:39:57 epoch=42/120 step=600/637 loss=1.26706 lr=0.00004612
+2026-06-27 17:40:10 convnext val mIoU=0.57196 background=0.61129 live_coral=0.68749 dead_coral=0.46496 bleached_coral=0.52410
+2026-06-27 17:40:12 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 17:40:17 epoch=43/120 step=50/637 loss=1.20965 lr=0.00004602
+2026-06-27 17:40:23 epoch=43/120 step=100/637 loss=1.21083 lr=0.00004597
+2026-06-27 17:40:28 epoch=43/120 step=150/637 loss=1.20950 lr=0.00004591
+2026-06-27 17:40:34 epoch=43/120 step=200/637 loss=1.23489 lr=0.00004586
+2026-06-27 17:40:39 epoch=43/120 step=250/637 loss=1.22952 lr=0.00004581
+2026-06-27 17:40:45 epoch=43/120 step=300/637 loss=1.23561 lr=0.00004575
+2026-06-27 17:40:50 epoch=43/120 step=350/637 loss=1.23030 lr=0.00004570
+2026-06-27 17:40:55 epoch=43/120 step=400/637 loss=1.23109 lr=0.00004564
+2026-06-27 17:41:01 epoch=43/120 step=450/637 loss=1.22815 lr=0.00004559
+2026-06-27 17:41:06 epoch=43/120 step=500/637 loss=1.22835 lr=0.00004553
+2026-06-27 17:41:12 epoch=43/120 step=550/637 loss=1.22223 lr=0.00004548
+2026-06-27 17:41:17 epoch=43/120 step=600/637 loss=1.23137 lr=0.00004542
+2026-06-27 17:41:29 convnext val mIoU=0.55872 background=0.63308 live_coral=0.67417 dead_coral=0.43271 bleached_coral=0.49490
+2026-06-27 17:41:36 epoch=44/120 step=50/637 loss=1.16085 lr=0.00004533
+2026-06-27 17:41:42 epoch=44/120 step=100/637 loss=1.18381 lr=0.00004527
+2026-06-27 17:41:47 epoch=44/120 step=150/637 loss=1.20392 lr=0.00004522
+2026-06-27 17:41:52 epoch=44/120 step=200/637 loss=1.20017 lr=0.00004516
+2026-06-27 17:41:58 epoch=44/120 step=250/637 loss=1.20368 lr=0.00004511
+2026-06-27 17:42:03 epoch=44/120 step=300/637 loss=1.20248 lr=0.00004505
+2026-06-27 17:42:09 epoch=44/120 step=350/637 loss=1.21866 lr=0.00004500
+2026-06-27 17:42:14 epoch=44/120 step=400/637 loss=1.21449 lr=0.00004494
+2026-06-27 17:42:19 epoch=44/120 step=450/637 loss=1.21452 lr=0.00004489
+2026-06-27 17:42:25 epoch=44/120 step=500/637 loss=1.21019 lr=0.00004483
+2026-06-27 17:42:30 epoch=44/120 step=550/637 loss=1.21705 lr=0.00004478
+2026-06-27 17:42:36 epoch=44/120 step=600/637 loss=1.22221 lr=0.00004472
+2026-06-27 17:42:48 convnext val mIoU=0.56435 background=0.62028 live_coral=0.67714 dead_coral=0.45983 bleached_coral=0.50014
+2026-06-27 17:42:55 epoch=45/120 step=50/637 loss=1.14820 lr=0.00004462
+2026-06-27 17:43:00 epoch=45/120 step=100/637 loss=1.17135 lr=0.00004457
+2026-06-27 17:43:06 epoch=45/120 step=150/637 loss=1.19954 lr=0.00004451
+2026-06-27 17:43:11 epoch=45/120 step=200/637 loss=1.19287 lr=0.00004446
+2026-06-27 17:43:17 epoch=45/120 step=250/637 loss=1.18915 lr=0.00004440
+2026-06-27 17:43:22 epoch=45/120 step=300/637 loss=1.20040 lr=0.00004434
+2026-06-27 17:43:27 epoch=45/120 step=350/637 loss=1.21071 lr=0.00004429
+2026-06-27 17:43:33 epoch=45/120 step=400/637 loss=1.20944 lr=0.00004423
+2026-06-27 17:43:38 epoch=45/120 step=450/637 loss=1.20407 lr=0.00004417
+2026-06-27 17:43:44 epoch=45/120 step=500/637 loss=1.20613 lr=0.00004412
+2026-06-27 17:43:49 epoch=45/120 step=550/637 loss=1.20417 lr=0.00004406
+2026-06-27 17:43:55 epoch=45/120 step=600/637 loss=1.20843 lr=0.00004401
+2026-06-27 17:44:07 convnext val mIoU=0.57236 background=0.62476 live_coral=0.69110 dead_coral=0.45522 bleached_coral=0.51837
+2026-06-27 17:44:10 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 17:44:16 epoch=46/120 step=50/637 loss=1.19030 lr=0.00004391
+2026-06-27 17:44:21 epoch=46/120 step=100/637 loss=1.18459 lr=0.00004385
+2026-06-27 17:44:27 epoch=46/120 step=150/637 loss=1.17921 lr=0.00004379
+2026-06-27 17:44:32 epoch=46/120 step=200/637 loss=1.18265 lr=0.00004374
+2026-06-27 17:44:37 epoch=46/120 step=250/637 loss=1.17906 lr=0.00004368
+2026-06-27 17:44:43 epoch=46/120 step=300/637 loss=1.17185 lr=0.00004362
+2026-06-27 17:44:48 epoch=46/120 step=350/637 loss=1.17796 lr=0.00004357
+2026-06-27 17:44:54 epoch=46/120 step=400/637 loss=1.17968 lr=0.00004351
+2026-06-27 17:44:59 epoch=46/120 step=450/637 loss=1.18138 lr=0.00004345
+2026-06-27 17:45:05 epoch=46/120 step=500/637 loss=1.18150 lr=0.00004340
+2026-06-27 17:45:10 epoch=46/120 step=550/637 loss=1.17728 lr=0.00004334
+2026-06-27 17:45:15 epoch=46/120 step=600/637 loss=1.18181 lr=0.00004328
+2026-06-27 17:45:28 convnext val mIoU=0.57790 background=0.63559 live_coral=0.69796 dead_coral=0.45134 bleached_coral=0.52670
+2026-06-27 17:45:30 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 17:45:36 epoch=47/120 step=50/637 loss=1.27406 lr=0.00004318
+2026-06-27 17:45:41 epoch=47/120 step=100/637 loss=1.22981 lr=0.00004312
+2026-06-27 17:45:47 epoch=47/120 step=150/637 loss=1.21333 lr=0.00004307
+2026-06-27 17:45:52 epoch=47/120 step=200/637 loss=1.19757 lr=0.00004301
+2026-06-27 17:45:58 epoch=47/120 step=250/637 loss=1.18818 lr=0.00004295
+2026-06-27 17:46:03 epoch=47/120 step=300/637 loss=1.18968 lr=0.00004289
+2026-06-27 17:46:08 epoch=47/120 step=350/637 loss=1.18057 lr=0.00004284
+2026-06-27 17:46:14 epoch=47/120 step=400/637 loss=1.17897 lr=0.00004278
+2026-06-27 17:46:19 epoch=47/120 step=450/637 loss=1.17188 lr=0.00004272
+2026-06-27 17:46:25 epoch=47/120 step=500/637 loss=1.17234 lr=0.00004266
+2026-06-27 17:46:30 epoch=47/120 step=550/637 loss=1.17330 lr=0.00004260
+2026-06-27 17:46:36 epoch=47/120 step=600/637 loss=1.18131 lr=0.00004255
+2026-06-27 17:46:48 convnext val mIoU=0.56389 background=0.63935 live_coral=0.69165 dead_coral=0.41875 bleached_coral=0.50581
+2026-06-27 17:46:55 epoch=48/120 step=50/637 loss=1.13485 lr=0.00004245
+2026-06-27 17:47:00 epoch=48/120 step=100/637 loss=1.17123 lr=0.00004239
+2026-06-27 17:47:06 epoch=48/120 step=150/637 loss=1.16201 lr=0.00004233
+2026-06-27 17:47:11 epoch=48/120 step=200/637 loss=1.14420 lr=0.00004227
+2026-06-27 17:47:17 epoch=48/120 step=250/637 loss=1.14525 lr=0.00004221
+2026-06-27 17:47:22 epoch=48/120 step=300/637 loss=1.16544 lr=0.00004215
+2026-06-27 17:47:27 epoch=48/120 step=350/637 loss=1.16743 lr=0.00004210
+2026-06-27 17:47:33 epoch=48/120 step=400/637 loss=1.17093 lr=0.00004204
+2026-06-27 17:47:38 epoch=48/120 step=450/637 loss=1.16721 lr=0.00004198
+2026-06-27 17:47:44 epoch=48/120 step=500/637 loss=1.15932 lr=0.00004192
+2026-06-27 17:47:49 epoch=48/120 step=550/637 loss=1.16376 lr=0.00004186
+2026-06-27 17:47:55 epoch=48/120 step=600/637 loss=1.16272 lr=0.00004180
+2026-06-27 17:48:07 convnext val mIoU=0.56271 background=0.62404 live_coral=0.67249 dead_coral=0.44175 bleached_coral=0.51258
+2026-06-27 17:48:14 epoch=49/120 step=50/637 loss=1.17917 lr=0.00004170
+2026-06-27 17:48:20 epoch=49/120 step=100/637 loss=1.21565 lr=0.00004164
+2026-06-27 17:48:25 epoch=49/120 step=150/637 loss=1.18686 lr=0.00004158
+2026-06-27 17:48:31 epoch=49/120 step=200/637 loss=1.16384 lr=0.00004152
+2026-06-27 17:48:36 epoch=49/120 step=250/637 loss=1.16942 lr=0.00004146
+2026-06-27 17:48:41 epoch=49/120 step=300/637 loss=1.16594 lr=0.00004141
+2026-06-27 17:48:47 epoch=49/120 step=350/637 loss=1.17918 lr=0.00004135
+2026-06-27 17:48:52 epoch=49/120 step=400/637 loss=1.17836 lr=0.00004129
+2026-06-27 17:48:58 epoch=49/120 step=450/637 loss=1.18071 lr=0.00004123
+2026-06-27 17:49:03 epoch=49/120 step=500/637 loss=1.17940 lr=0.00004117
+2026-06-27 17:49:08 epoch=49/120 step=550/637 loss=1.17744 lr=0.00004111
+2026-06-27 17:49:14 epoch=49/120 step=600/637 loss=1.18752 lr=0.00004105
+2026-06-27 17:49:26 convnext val mIoU=0.56191 background=0.62684 live_coral=0.67934 dead_coral=0.43335 bleached_coral=0.50810
+2026-06-27 17:49:33 epoch=50/120 step=50/637 loss=1.16680 lr=0.00004095
+2026-06-27 17:49:39 epoch=50/120 step=100/637 loss=1.17525 lr=0.00004089
+2026-06-27 17:49:44 epoch=50/120 step=150/637 loss=1.18415 lr=0.00004083
+2026-06-27 17:49:49 epoch=50/120 step=200/637 loss=1.17886 lr=0.00004077
+2026-06-27 17:49:55 epoch=50/120 step=250/637 loss=1.18503 lr=0.00004071
+2026-06-27 17:50:00 epoch=50/120 step=300/637 loss=1.16967 lr=0.00004065
+2026-06-27 17:50:06 epoch=50/120 step=350/637 loss=1.15577 lr=0.00004059
+2026-06-27 17:50:11 epoch=50/120 step=400/637 loss=1.16193 lr=0.00004053
+2026-06-27 17:50:16 epoch=50/120 step=450/637 loss=1.15975 lr=0.00004047
+2026-06-27 17:50:22 epoch=50/120 step=500/637 loss=1.16558 lr=0.00004041
+2026-06-27 17:50:27 epoch=50/120 step=550/637 loss=1.16702 lr=0.00004035
+2026-06-27 17:50:33 epoch=50/120 step=600/637 loss=1.16742 lr=0.00004029
+2026-06-27 17:50:45 convnext val mIoU=0.57251 background=0.62830 live_coral=0.69019 dead_coral=0.44931 bleached_coral=0.52225
+2026-06-27 17:50:52 epoch=51/120 step=50/637 loss=1.13928 lr=0.00004019
+2026-06-27 17:50:57 epoch=51/120 step=100/637 loss=1.10121 lr=0.00004013
+2026-06-27 17:51:03 epoch=51/120 step=150/637 loss=1.11757 lr=0.00004007
+2026-06-27 17:51:08 epoch=51/120 step=200/637 loss=1.12413 lr=0.00004001
+2026-06-27 17:51:13 epoch=51/120 step=250/637 loss=1.13931 lr=0.00003995
+2026-06-27 17:51:19 epoch=51/120 step=300/637 loss=1.14535 lr=0.00003989
+2026-06-27 17:51:24 epoch=51/120 step=350/637 loss=1.14168 lr=0.00003982
+2026-06-27 17:51:30 epoch=51/120 step=400/637 loss=1.13519 lr=0.00003976
+2026-06-27 17:51:35 epoch=51/120 step=450/637 loss=1.13519 lr=0.00003970
+2026-06-27 17:51:40 epoch=51/120 step=500/637 loss=1.13064 lr=0.00003964
+2026-06-27 17:51:46 epoch=51/120 step=550/637 loss=1.12839 lr=0.00003958
+2026-06-27 17:51:51 epoch=51/120 step=600/637 loss=1.13513 lr=0.00003952
+2026-06-27 17:52:04 convnext val mIoU=0.57040 background=0.63297 live_coral=0.68536 dead_coral=0.44237 bleached_coral=0.52093
+2026-06-27 17:52:11 epoch=52/120 step=50/637 loss=1.02547 lr=0.00003942
+2026-06-27 17:52:16 epoch=52/120 step=100/637 loss=1.06478 lr=0.00003936
+2026-06-27 17:52:22 epoch=52/120 step=150/637 loss=1.07221 lr=0.00003930
+2026-06-27 17:52:27 epoch=52/120 step=200/637 loss=1.10996 lr=0.00003924
+2026-06-27 17:52:32 epoch=52/120 step=250/637 loss=1.12261 lr=0.00003917
+2026-06-27 17:52:38 epoch=52/120 step=300/637 loss=1.12550 lr=0.00003911
+2026-06-27 17:52:43 epoch=52/120 step=350/637 loss=1.11364 lr=0.00003905
+2026-06-27 17:52:49 epoch=52/120 step=400/637 loss=1.10827 lr=0.00003899
+2026-06-27 17:52:54 epoch=52/120 step=450/637 loss=1.11722 lr=0.00003893
+2026-06-27 17:53:00 epoch=52/120 step=500/637 loss=1.11621 lr=0.00003887
+2026-06-27 17:53:05 epoch=52/120 step=550/637 loss=1.11449 lr=0.00003881
+2026-06-27 17:53:10 epoch=52/120 step=600/637 loss=1.11555 lr=0.00003875
+2026-06-27 17:53:23 convnext val mIoU=0.56781 background=0.62810 live_coral=0.68875 dead_coral=0.44599 bleached_coral=0.50838
+2026-06-27 17:53:30 epoch=53/120 step=50/637 loss=1.06110 lr=0.00003864
+2026-06-27 17:53:35 epoch=53/120 step=100/637 loss=1.07836 lr=0.00003858
+2026-06-27 17:53:41 epoch=53/120 step=150/637 loss=1.07550 lr=0.00003852
+2026-06-27 17:53:46 epoch=53/120 step=200/637 loss=1.10418 lr=0.00003846
+2026-06-27 17:53:51 epoch=53/120 step=250/637 loss=1.08826 lr=0.00003840
+2026-06-27 17:53:57 epoch=53/120 step=300/637 loss=1.08749 lr=0.00003834
+2026-06-27 17:54:02 epoch=53/120 step=350/637 loss=1.09346 lr=0.00003827
+2026-06-27 17:54:08 epoch=53/120 step=400/637 loss=1.09975 lr=0.00003821
+2026-06-27 17:54:13 epoch=53/120 step=450/637 loss=1.10000 lr=0.00003815
+2026-06-27 17:54:19 epoch=53/120 step=500/637 loss=1.09966 lr=0.00003809
+2026-06-27 17:54:24 epoch=53/120 step=550/637 loss=1.10491 lr=0.00003803
+2026-06-27 17:54:30 epoch=53/120 step=600/637 loss=1.11448 lr=0.00003797
+2026-06-27 17:54:42 convnext val mIoU=0.56323 background=0.63447 live_coral=0.67371 dead_coral=0.44102 bleached_coral=0.50372
+2026-06-27 17:54:50 epoch=54/120 step=50/637 loss=1.13361 lr=0.00003786
+2026-06-27 17:54:55 epoch=54/120 step=100/637 loss=1.14682 lr=0.00003780
+2026-06-27 17:55:00 epoch=54/120 step=150/637 loss=1.12349 lr=0.00003774
+2026-06-27 17:55:06 epoch=54/120 step=200/637 loss=1.11568 lr=0.00003768
+2026-06-27 17:55:11 epoch=54/120 step=250/637 loss=1.11959 lr=0.00003761
+2026-06-27 17:55:17 epoch=54/120 step=300/637 loss=1.11247 lr=0.00003755
+2026-06-27 17:55:22 epoch=54/120 step=350/637 loss=1.12045 lr=0.00003749
+2026-06-27 17:55:28 epoch=54/120 step=400/637 loss=1.12119 lr=0.00003743
+2026-06-27 17:55:33 epoch=54/120 step=450/637 loss=1.12252 lr=0.00003737
+2026-06-27 17:55:38 epoch=54/120 step=500/637 loss=1.11956 lr=0.00003730
+2026-06-27 17:55:44 epoch=54/120 step=550/637 loss=1.12278 lr=0.00003724
+2026-06-27 17:55:49 epoch=54/120 step=600/637 loss=1.12936 lr=0.00003718
+2026-06-27 17:56:02 convnext val mIoU=0.57703 background=0.63220 live_coral=0.68878 dead_coral=0.45591 bleached_coral=0.53124
+2026-06-27 17:56:08 epoch=55/120 step=50/637 loss=1.11279 lr=0.00003707
+2026-06-27 17:56:14 epoch=55/120 step=100/637 loss=1.10735 lr=0.00003701
+2026-06-27 17:56:19 epoch=55/120 step=150/637 loss=1.09487 lr=0.00003695
+2026-06-27 17:56:25 epoch=55/120 step=200/637 loss=1.11280 lr=0.00003689
+2026-06-27 17:56:30 epoch=55/120 step=250/637 loss=1.11141 lr=0.00003682
+2026-06-27 17:56:36 epoch=55/120 step=300/637 loss=1.12616 lr=0.00003676
+2026-06-27 17:56:41 epoch=55/120 step=350/637 loss=1.13586 lr=0.00003670
+2026-06-27 17:56:46 epoch=55/120 step=400/637 loss=1.12962 lr=0.00003664
+2026-06-27 17:56:52 epoch=55/120 step=450/637 loss=1.12142 lr=0.00003658
+2026-06-27 17:56:57 epoch=55/120 step=500/637 loss=1.11939 lr=0.00003651
+2026-06-27 17:57:03 epoch=55/120 step=550/637 loss=1.12158 lr=0.00003645
+2026-06-27 17:57:08 epoch=55/120 step=600/637 loss=1.11835 lr=0.00003639
+2026-06-27 17:57:20 convnext val mIoU=0.57366 background=0.64049 live_coral=0.68586 dead_coral=0.45184 bleached_coral=0.51646
+2026-06-27 17:57:27 epoch=56/120 step=50/637 loss=1.08612 lr=0.00003628
+2026-06-27 17:57:33 epoch=56/120 step=100/637 loss=1.10515 lr=0.00003622
+2026-06-27 17:57:38 epoch=56/120 step=150/637 loss=1.08384 lr=0.00003616
+2026-06-27 17:57:43 epoch=56/120 step=200/637 loss=1.08161 lr=0.00003609
+2026-06-27 17:57:49 epoch=56/120 step=250/637 loss=1.09932 lr=0.00003603
+2026-06-27 17:57:54 epoch=56/120 step=300/637 loss=1.10958 lr=0.00003597
+2026-06-27 17:58:00 epoch=56/120 step=350/637 loss=1.11304 lr=0.00003591
+2026-06-27 17:58:05 epoch=56/120 step=400/637 loss=1.11673 lr=0.00003584
+2026-06-27 17:58:10 epoch=56/120 step=450/637 loss=1.10468 lr=0.00003578
+2026-06-27 17:58:16 epoch=56/120 step=500/637 loss=1.10813 lr=0.00003572
+2026-06-27 17:58:21 epoch=56/120 step=550/637 loss=1.10864 lr=0.00003566
+2026-06-27 17:58:27 epoch=56/120 step=600/637 loss=1.11349 lr=0.00003559
+2026-06-27 17:58:39 convnext val mIoU=0.56797 background=0.63163 live_coral=0.67527 dead_coral=0.45540 bleached_coral=0.50956
+2026-06-27 17:58:46 epoch=57/120 step=50/637 loss=1.17252 lr=0.00003548
+2026-06-27 17:58:51 epoch=57/120 step=100/637 loss=1.13580 lr=0.00003542
+2026-06-27 17:58:57 epoch=57/120 step=150/637 loss=1.13842 lr=0.00003536
+2026-06-27 17:59:02 epoch=57/120 step=200/637 loss=1.13309 lr=0.00003530
+2026-06-27 17:59:07 epoch=57/120 step=250/637 loss=1.12287 lr=0.00003523
+2026-06-27 17:59:13 epoch=57/120 step=300/637 loss=1.11816 lr=0.00003517
+2026-06-27 17:59:18 epoch=57/120 step=350/637 loss=1.11158 lr=0.00003511
+2026-06-27 17:59:24 epoch=57/120 step=400/637 loss=1.11057 lr=0.00003504
+2026-06-27 17:59:29 epoch=57/120 step=450/637 loss=1.10588 lr=0.00003498
+2026-06-27 17:59:35 epoch=57/120 step=500/637 loss=1.11023 lr=0.00003492
+2026-06-27 17:59:40 epoch=57/120 step=550/637 loss=1.10216 lr=0.00003486
+2026-06-27 17:59:46 epoch=57/120 step=600/637 loss=1.10494 lr=0.00003479
+2026-06-27 17:59:58 convnext val mIoU=0.56711 background=0.62515 live_coral=0.67037 dead_coral=0.46778 bleached_coral=0.50515
+2026-06-27 18:00:05 epoch=58/120 step=50/637 loss=1.08716 lr=0.00003468
+2026-06-27 18:00:10 epoch=58/120 step=100/637 loss=1.07773 lr=0.00003462
+2026-06-27 18:00:16 epoch=58/120 step=150/637 loss=1.04298 lr=0.00003456
+2026-06-27 18:00:21 epoch=58/120 step=200/637 loss=1.05360 lr=0.00003449
+2026-06-27 18:00:27 epoch=58/120 step=250/637 loss=1.05906 lr=0.00003443
+2026-06-27 18:00:32 epoch=58/120 step=300/637 loss=1.06181 lr=0.00003437
+2026-06-27 18:00:37 epoch=58/120 step=350/637 loss=1.06183 lr=0.00003430
+2026-06-27 18:00:43 epoch=58/120 step=400/637 loss=1.07510 lr=0.00003424
+2026-06-27 18:00:48 epoch=58/120 step=450/637 loss=1.07728 lr=0.00003418
+2026-06-27 18:00:54 epoch=58/120 step=500/637 loss=1.08033 lr=0.00003412
+2026-06-27 18:00:59 epoch=58/120 step=550/637 loss=1.08297 lr=0.00003405
+2026-06-27 18:01:04 epoch=58/120 step=600/637 loss=1.08733 lr=0.00003399
+2026-06-27 18:01:17 convnext val mIoU=0.57477 background=0.63218 live_coral=0.68527 dead_coral=0.45975 bleached_coral=0.52189
+2026-06-27 18:01:24 epoch=59/120 step=50/637 loss=0.99931 lr=0.00003388
+2026-06-27 18:01:29 epoch=59/120 step=100/637 loss=1.06193 lr=0.00003382
+2026-06-27 18:01:34 epoch=59/120 step=150/637 loss=1.06574 lr=0.00003375
+2026-06-27 18:01:40 epoch=59/120 step=200/637 loss=1.07049 lr=0.00003369
+2026-06-27 18:01:45 epoch=59/120 step=250/637 loss=1.07321 lr=0.00003363
+2026-06-27 18:01:51 epoch=59/120 step=300/637 loss=1.07634 lr=0.00003356
+2026-06-27 18:01:56 epoch=59/120 step=350/637 loss=1.08126 lr=0.00003350
+2026-06-27 18:02:02 epoch=59/120 step=400/637 loss=1.08509 lr=0.00003344
+2026-06-27 18:02:07 epoch=59/120 step=450/637 loss=1.08573 lr=0.00003337
+2026-06-27 18:02:13 epoch=59/120 step=500/637 loss=1.09324 lr=0.00003331
+2026-06-27 18:02:18 epoch=59/120 step=550/637 loss=1.09042 lr=0.00003325
+2026-06-27 18:02:23 epoch=59/120 step=600/637 loss=1.09108 lr=0.00003318
+2026-06-27 18:02:36 convnext val mIoU=0.57650 background=0.62460 live_coral=0.69278 dead_coral=0.45987 bleached_coral=0.52877
+2026-06-27 18:02:42 epoch=60/120 step=50/637 loss=1.11383 lr=0.00003307
+2026-06-27 18:02:48 epoch=60/120 step=100/637 loss=1.07998 lr=0.00003301
+2026-06-27 18:02:53 epoch=60/120 step=150/637 loss=1.08880 lr=0.00003295
+2026-06-27 18:02:59 epoch=60/120 step=200/637 loss=1.10399 lr=0.00003288
+2026-06-27 18:03:04 epoch=60/120 step=250/637 loss=1.10403 lr=0.00003282
+2026-06-27 18:03:10 epoch=60/120 step=300/637 loss=1.09626 lr=0.00003275
+2026-06-27 18:03:15 epoch=60/120 step=350/637 loss=1.08733 lr=0.00003269
+2026-06-27 18:03:20 epoch=60/120 step=400/637 loss=1.08198 lr=0.00003263
+2026-06-27 18:03:26 epoch=60/120 step=450/637 loss=1.08061 lr=0.00003256
+2026-06-27 18:03:31 epoch=60/120 step=500/637 loss=1.08248 lr=0.00003250
+2026-06-27 18:03:37 epoch=60/120 step=550/637 loss=1.08809 lr=0.00003244
+2026-06-27 18:03:42 epoch=60/120 step=600/637 loss=1.08652 lr=0.00003237
+2026-06-27 18:03:54 convnext val mIoU=0.56681 background=0.63080 live_coral=0.67430 dead_coral=0.45059 bleached_coral=0.51154
+2026-06-27 18:04:02 epoch=61/120 step=50/637 loss=1.04822 lr=0.00003226
+2026-06-27 18:04:08 epoch=61/120 step=100/637 loss=1.05482 lr=0.00003220
+2026-06-27 18:04:13 epoch=61/120 step=150/637 loss=1.07088 lr=0.00003214
+2026-06-27 18:04:19 epoch=61/120 step=200/637 loss=1.07338 lr=0.00003207
+2026-06-27 18:04:24 epoch=61/120 step=250/637 loss=1.07569 lr=0.00003201
+2026-06-27 18:04:30 epoch=61/120 step=300/637 loss=1.05916 lr=0.00003195
+2026-06-27 18:04:35 epoch=61/120 step=350/637 loss=1.05714 lr=0.00003188
+2026-06-27 18:04:41 epoch=61/120 step=400/637 loss=1.06201 lr=0.00003182
+2026-06-27 18:04:46 epoch=61/120 step=450/637 loss=1.06557 lr=0.00003175
+2026-06-27 18:04:52 epoch=61/120 step=500/637 loss=1.07383 lr=0.00003169
+2026-06-27 18:04:57 epoch=61/120 step=550/637 loss=1.07934 lr=0.00003163
+2026-06-27 18:05:03 epoch=61/120 step=600/637 loss=1.07601 lr=0.00003156
+2026-06-27 18:05:16 convnext val mIoU=0.57318 background=0.62663 live_coral=0.68599 dead_coral=0.45645 bleached_coral=0.52365
+2026-06-27 18:05:22 epoch=62/120 step=50/637 loss=1.08090 lr=0.00003145
+2026-06-27 18:05:28 epoch=62/120 step=100/637 loss=1.05906 lr=0.00003139
+2026-06-27 18:05:33 epoch=62/120 step=150/637 loss=1.06556 lr=0.00003133
+2026-06-27 18:05:39 epoch=62/120 step=200/637 loss=1.05957 lr=0.00003126
+2026-06-27 18:05:44 epoch=62/120 step=250/637 loss=1.06310 lr=0.00003120
+2026-06-27 18:05:50 epoch=62/120 step=300/637 loss=1.06544 lr=0.00003113
+2026-06-27 18:05:55 epoch=62/120 step=350/637 loss=1.06491 lr=0.00003107
+2026-06-27 18:06:01 epoch=62/120 step=400/637 loss=1.06744 lr=0.00003101
+2026-06-27 18:06:06 epoch=62/120 step=450/637 loss=1.06710 lr=0.00003094
+2026-06-27 18:06:12 epoch=62/120 step=500/637 loss=1.07365 lr=0.00003088
+2026-06-27 18:06:17 epoch=62/120 step=550/637 loss=1.07171 lr=0.00003082
+2026-06-27 18:06:23 epoch=62/120 step=600/637 loss=1.07590 lr=0.00003075
+2026-06-27 18:06:35 convnext val mIoU=0.56632 background=0.62358 live_coral=0.67402 dead_coral=0.45767 bleached_coral=0.51001
+2026-06-27 18:06:42 epoch=63/120 step=50/637 loss=1.18560 lr=0.00003064
+2026-06-27 18:06:47 epoch=63/120 step=100/637 loss=1.12582 lr=0.00003058
+2026-06-27 18:06:53 epoch=63/120 step=150/637 loss=1.10077 lr=0.00003051
+2026-06-27 18:06:58 epoch=63/120 step=200/637 loss=1.08905 lr=0.00003045
+2026-06-27 18:07:04 epoch=63/120 step=250/637 loss=1.06977 lr=0.00003039
+2026-06-27 18:07:09 epoch=63/120 step=300/637 loss=1.06197 lr=0.00003032
+2026-06-27 18:07:15 epoch=63/120 step=350/637 loss=1.05898 lr=0.00003026
+2026-06-27 18:07:20 epoch=63/120 step=400/637 loss=1.05567 lr=0.00003020
+2026-06-27 18:07:26 epoch=63/120 step=450/637 loss=1.05678 lr=0.00003013
+2026-06-27 18:07:31 epoch=63/120 step=500/637 loss=1.05636 lr=0.00003007
+2026-06-27 18:07:37 epoch=63/120 step=550/637 loss=1.06015 lr=0.00003001
+2026-06-27 18:07:42 epoch=63/120 step=600/637 loss=1.05881 lr=0.00002994
+2026-06-27 18:07:55 convnext val mIoU=0.57135 background=0.62033 live_coral=0.68406 dead_coral=0.46410 bleached_coral=0.51690
+2026-06-27 18:08:02 epoch=64/120 step=50/637 loss=1.01091 lr=0.00002983
+2026-06-27 18:08:08 epoch=64/120 step=100/637 loss=1.00015 lr=0.00002977
+2026-06-27 18:08:13 epoch=64/120 step=150/637 loss=1.03089 lr=0.00002970
+2026-06-27 18:08:19 epoch=64/120 step=200/637 loss=1.00812 lr=0.00002964
+2026-06-27 18:08:24 epoch=64/120 step=250/637 loss=1.03463 lr=0.00002958
+2026-06-27 18:08:29 epoch=64/120 step=300/637 loss=1.04246 lr=0.00002951
+2026-06-27 18:08:35 epoch=64/120 step=350/637 loss=1.04831 lr=0.00002945
+2026-06-27 18:08:41 epoch=64/120 step=400/637 loss=1.04755 lr=0.00002938
+2026-06-27 18:08:46 epoch=64/120 step=450/637 loss=1.04784 lr=0.00002932
+2026-06-27 18:08:52 epoch=64/120 step=500/637 loss=1.05149 lr=0.00002926
+2026-06-27 18:08:57 epoch=64/120 step=550/637 loss=1.04831 lr=0.00002919
+2026-06-27 18:09:03 epoch=64/120 step=600/637 loss=1.04957 lr=0.00002913
+2026-06-27 18:09:16 convnext val mIoU=0.57615 background=0.62836 live_coral=0.69351 dead_coral=0.45984 bleached_coral=0.52288
+2026-06-27 18:09:23 epoch=65/120 step=50/637 loss=0.98976 lr=0.00002902
+2026-06-27 18:09:28 epoch=65/120 step=100/637 loss=1.03987 lr=0.00002896
+2026-06-27 18:09:34 epoch=65/120 step=150/637 loss=1.05057 lr=0.00002889
+2026-06-27 18:09:39 epoch=65/120 step=200/637 loss=1.02800 lr=0.00002883
+2026-06-27 18:09:45 epoch=65/120 step=250/637 loss=1.01775 lr=0.00002877
+2026-06-27 18:09:52 epoch=65/120 step=300/637 loss=1.02392 lr=0.00002870
+2026-06-27 18:09:57 epoch=65/120 step=350/637 loss=1.03607 lr=0.00002864
+2026-06-27 18:10:03 epoch=65/120 step=400/637 loss=1.04077 lr=0.00002857
+2026-06-27 18:10:08 epoch=65/120 step=450/637 loss=1.04045 lr=0.00002851
+2026-06-27 18:10:14 epoch=65/120 step=500/637 loss=1.04336 lr=0.00002845
+2026-06-27 18:10:19 epoch=65/120 step=550/637 loss=1.04044 lr=0.00002838
+2026-06-27 18:10:25 epoch=65/120 step=600/637 loss=1.03709 lr=0.00002832
+2026-06-27 18:10:38 convnext val mIoU=0.57442 background=0.62830 live_coral=0.69435 dead_coral=0.45242 bleached_coral=0.52260
+2026-06-27 18:10:44 epoch=66/120 step=50/637 loss=1.07879 lr=0.00002821
+2026-06-27 18:10:50 epoch=66/120 step=100/637 loss=1.06091 lr=0.00002815
+2026-06-27 18:10:55 epoch=66/120 step=150/637 loss=1.04590 lr=0.00002808
+2026-06-27 18:11:01 epoch=66/120 step=200/637 loss=1.04553 lr=0.00002802
+2026-06-27 18:11:07 epoch=66/120 step=250/637 loss=1.03874 lr=0.00002796
+2026-06-27 18:11:12 epoch=66/120 step=300/637 loss=1.04435 lr=0.00002789
+2026-06-27 18:11:18 epoch=66/120 step=350/637 loss=1.05400 lr=0.00002783
+2026-06-27 18:11:23 epoch=66/120 step=400/637 loss=1.05344 lr=0.00002777
+2026-06-27 18:11:29 epoch=66/120 step=450/637 loss=1.04416 lr=0.00002770
+2026-06-27 18:11:34 epoch=66/120 step=500/637 loss=1.04203 lr=0.00002764
+2026-06-27 18:11:40 epoch=66/120 step=550/637 loss=1.04691 lr=0.00002757
+2026-06-27 18:11:45 epoch=66/120 step=600/637 loss=1.04108 lr=0.00002751
+2026-06-27 18:11:58 convnext val mIoU=0.58008 background=0.62581 live_coral=0.69509 dead_coral=0.46982 bleached_coral=0.52961
+2026-06-27 18:12:00 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 18:12:06 epoch=67/120 step=50/637 loss=1.00604 lr=0.00002740
+2026-06-27 18:12:12 epoch=67/120 step=100/637 loss=1.03266 lr=0.00002734
+2026-06-27 18:12:17 epoch=67/120 step=150/637 loss=1.04125 lr=0.00002727
+2026-06-27 18:12:23 epoch=67/120 step=200/637 loss=1.03428 lr=0.00002721
+2026-06-27 18:12:28 epoch=67/120 step=250/637 loss=1.03333 lr=0.00002715
+2026-06-27 18:12:34 epoch=67/120 step=300/637 loss=1.03154 lr=0.00002708
+2026-06-27 18:12:39 epoch=67/120 step=350/637 loss=1.03240 lr=0.00002702
+2026-06-27 18:12:45 epoch=67/120 step=400/637 loss=1.04108 lr=0.00002696
+2026-06-27 18:12:50 epoch=67/120 step=450/637 loss=1.04642 lr=0.00002689
+2026-06-27 18:12:56 epoch=67/120 step=500/637 loss=1.04418 lr=0.00002683
+2026-06-27 18:13:01 epoch=67/120 step=550/637 loss=1.04614 lr=0.00002677
+2026-06-27 18:13:07 epoch=67/120 step=600/637 loss=1.04145 lr=0.00002670
+2026-06-27 18:13:19 convnext val mIoU=0.57558 background=0.63136 live_coral=0.69422 dead_coral=0.46131 bleached_coral=0.51545
+2026-06-27 18:13:26 epoch=68/120 step=50/637 loss=1.04819 lr=0.00002659
+2026-06-27 18:13:32 epoch=68/120 step=100/637 loss=1.07614 lr=0.00002653
+2026-06-27 18:13:37 epoch=68/120 step=150/637 loss=1.05985 lr=0.00002647
+2026-06-27 18:13:43 epoch=68/120 step=200/637 loss=1.06310 lr=0.00002641
+2026-06-27 18:13:48 epoch=68/120 step=250/637 loss=1.05013 lr=0.00002634
+2026-06-27 18:13:54 epoch=68/120 step=300/637 loss=1.03741 lr=0.00002628
+2026-06-27 18:13:59 epoch=68/120 step=350/637 loss=1.02979 lr=0.00002622
+2026-06-27 18:14:05 epoch=68/120 step=400/637 loss=1.02823 lr=0.00002615
+2026-06-27 18:14:10 epoch=68/120 step=450/637 loss=1.02841 lr=0.00002609
+2026-06-27 18:14:15 epoch=68/120 step=500/637 loss=1.02678 lr=0.00002603
+2026-06-27 18:14:21 epoch=68/120 step=550/637 loss=1.02135 lr=0.00002596
+2026-06-27 18:14:27 epoch=68/120 step=600/637 loss=1.02513 lr=0.00002590
+2026-06-27 18:14:40 convnext val mIoU=0.57328 background=0.63714 live_coral=0.68473 dead_coral=0.46007 bleached_coral=0.51120
+2026-06-27 18:14:47 epoch=69/120 step=50/637 loss=1.05653 lr=0.00002579
+2026-06-27 18:14:52 epoch=69/120 step=100/637 loss=1.04088 lr=0.00002573
+2026-06-27 18:14:58 epoch=69/120 step=150/637 loss=1.01852 lr=0.00002567
+2026-06-27 18:15:03 epoch=69/120 step=200/637 loss=1.02651 lr=0.00002560
+2026-06-27 18:15:09 epoch=69/120 step=250/637 loss=1.02038 lr=0.00002554
+2026-06-27 18:15:14 epoch=69/120 step=300/637 loss=1.01712 lr=0.00002548
+2026-06-27 18:15:20 epoch=69/120 step=350/637 loss=1.01164 lr=0.00002541
+2026-06-27 18:15:25 epoch=69/120 step=400/637 loss=1.02219 lr=0.00002535
+2026-06-27 18:15:31 epoch=69/120 step=450/637 loss=1.02383 lr=0.00002529
+2026-06-27 18:15:36 epoch=69/120 step=500/637 loss=1.02756 lr=0.00002523
+2026-06-27 18:15:42 epoch=69/120 step=550/637 loss=1.03016 lr=0.00002516
+2026-06-27 18:15:47 epoch=69/120 step=600/637 loss=1.02914 lr=0.00002510
+2026-06-27 18:16:00 convnext val mIoU=0.56808 background=0.62943 live_coral=0.67713 dead_coral=0.45795 bleached_coral=0.50779
+2026-06-27 18:16:07 epoch=70/120 step=50/637 loss=1.11672 lr=0.00002499
+2026-06-27 18:16:12 epoch=70/120 step=100/637 loss=1.06868 lr=0.00002493
+2026-06-27 18:16:18 epoch=70/120 step=150/637 loss=1.03775 lr=0.00002487
+2026-06-27 18:16:23 epoch=70/120 step=200/637 loss=1.02233 lr=0.00002480
+2026-06-27 18:16:29 epoch=70/120 step=250/637 loss=1.02373 lr=0.00002474
+2026-06-27 18:16:35 epoch=70/120 step=300/637 loss=1.02230 lr=0.00002468
+2026-06-27 18:16:40 epoch=70/120 step=350/637 loss=1.02217 lr=0.00002462
+2026-06-27 18:16:46 epoch=70/120 step=400/637 loss=1.03002 lr=0.00002455
+2026-06-27 18:16:52 epoch=70/120 step=450/637 loss=1.02529 lr=0.00002449
+2026-06-27 18:16:57 epoch=70/120 step=500/637 loss=1.02379 lr=0.00002443
+2026-06-27 18:17:03 epoch=70/120 step=550/637 loss=1.02446 lr=0.00002437
+2026-06-27 18:17:08 epoch=70/120 step=600/637 loss=1.02477 lr=0.00002430
+2026-06-27 18:17:21 convnext val mIoU=0.57482 background=0.62920 live_coral=0.69170 dead_coral=0.45857 bleached_coral=0.51980
+2026-06-27 18:17:28 epoch=71/120 step=50/637 loss=1.02948 lr=0.00002420
+2026-06-27 18:17:34 epoch=71/120 step=100/637 loss=1.03514 lr=0.00002413
+2026-06-27 18:17:39 epoch=71/120 step=150/637 loss=1.00592 lr=0.00002407
+2026-06-27 18:17:45 epoch=71/120 step=200/637 loss=0.99653 lr=0.00002401
+2026-06-27 18:17:50 epoch=71/120 step=250/637 loss=1.00706 lr=0.00002395
+2026-06-27 18:17:56 epoch=71/120 step=300/637 loss=1.00545 lr=0.00002388
+2026-06-27 18:18:01 epoch=71/120 step=350/637 loss=1.01715 lr=0.00002382
+2026-06-27 18:18:07 epoch=71/120 step=400/637 loss=1.00643 lr=0.00002376
+2026-06-27 18:18:13 epoch=71/120 step=450/637 loss=1.00127 lr=0.00002370
+2026-06-27 18:18:18 epoch=71/120 step=500/637 loss=1.00478 lr=0.00002364
+2026-06-27 18:18:24 epoch=71/120 step=550/637 loss=1.00152 lr=0.00002357
+2026-06-27 18:18:29 epoch=71/120 step=600/637 loss=1.00074 lr=0.00002351
+2026-06-27 18:18:42 convnext val mIoU=0.57591 background=0.63521 live_coral=0.69296 dead_coral=0.45611 bleached_coral=0.51935
+2026-06-27 18:18:50 epoch=72/120 step=50/637 loss=1.01546 lr=0.00002340
+2026-06-27 18:18:55 epoch=72/120 step=100/637 loss=0.98279 lr=0.00002334
+2026-06-27 18:19:01 epoch=72/120 step=150/637 loss=0.99124 lr=0.00002328
+2026-06-27 18:19:07 epoch=72/120 step=200/637 loss=1.00877 lr=0.00002322
+2026-06-27 18:19:12 epoch=72/120 step=250/637 loss=1.00424 lr=0.00002316
+2026-06-27 18:19:18 epoch=72/120 step=300/637 loss=1.00395 lr=0.00002309
+2026-06-27 18:19:24 epoch=72/120 step=350/637 loss=1.00070 lr=0.00002303
+2026-06-27 18:19:30 epoch=72/120 step=400/637 loss=1.00275 lr=0.00002297
+2026-06-27 18:19:36 epoch=72/120 step=450/637 loss=1.00414 lr=0.00002291
+2026-06-27 18:19:42 epoch=72/120 step=500/637 loss=1.00863 lr=0.00002285
+2026-06-27 18:19:48 epoch=72/120 step=550/637 loss=1.00471 lr=0.00002279
+2026-06-27 18:19:54 epoch=72/120 step=600/637 loss=1.00596 lr=0.00002272
+2026-06-27 18:20:07 convnext val mIoU=0.57933 background=0.63118 live_coral=0.69452 dead_coral=0.47107 bleached_coral=0.52055
+2026-06-27 18:20:15 epoch=73/120 step=50/637 loss=1.01682 lr=0.00002262
+2026-06-27 18:20:21 epoch=73/120 step=100/637 loss=0.97569 lr=0.00002256
+2026-06-27 18:20:26 epoch=73/120 step=150/637 loss=0.99068 lr=0.00002249
+2026-06-27 18:20:32 epoch=73/120 step=200/637 loss=1.00077 lr=0.00002243
+2026-06-27 18:20:38 epoch=73/120 step=250/637 loss=1.00321 lr=0.00002237
+2026-06-27 18:20:44 epoch=73/120 step=300/637 loss=0.99918 lr=0.00002231
+2026-06-27 18:20:50 epoch=73/120 step=350/637 loss=1.00531 lr=0.00002225
+2026-06-27 18:20:56 epoch=73/120 step=400/637 loss=1.00398 lr=0.00002219
+2026-06-27 18:21:01 epoch=73/120 step=450/637 loss=1.00618 lr=0.00002213
+2026-06-27 18:21:07 epoch=73/120 step=500/637 loss=0.99988 lr=0.00002206
+2026-06-27 18:21:13 epoch=73/120 step=550/637 loss=0.99655 lr=0.00002200
+2026-06-27 18:21:18 epoch=73/120 step=600/637 loss=0.99382 lr=0.00002194
+2026-06-27 18:21:31 convnext val mIoU=0.57895 background=0.63195 live_coral=0.69167 dead_coral=0.46938 bleached_coral=0.52280
+2026-06-27 18:21:37 epoch=74/120 step=50/637 loss=0.94525 lr=0.00002184
+2026-06-27 18:21:43 epoch=74/120 step=100/637 loss=0.96908 lr=0.00002178
+2026-06-27 18:21:49 epoch=74/120 step=150/637 loss=0.97585 lr=0.00002171
+2026-06-27 18:21:54 epoch=74/120 step=200/637 loss=0.97532 lr=0.00002165
+2026-06-27 18:22:00 epoch=74/120 step=250/637 loss=0.97941 lr=0.00002159
+2026-06-27 18:22:05 epoch=74/120 step=300/637 loss=0.98212 lr=0.00002153
+2026-06-27 18:22:11 epoch=74/120 step=350/637 loss=0.99446 lr=0.00002147
+2026-06-27 18:22:16 epoch=74/120 step=400/637 loss=1.00494 lr=0.00002141
+2026-06-27 18:22:22 epoch=74/120 step=450/637 loss=1.00227 lr=0.00002135
+2026-06-27 18:22:27 epoch=74/120 step=500/637 loss=1.00632 lr=0.00002129
+2026-06-27 18:22:33 epoch=74/120 step=550/637 loss=1.00499 lr=0.00002123
+2026-06-27 18:22:38 epoch=74/120 step=600/637 loss=1.00432 lr=0.00002117
+2026-06-27 18:22:51 convnext val mIoU=0.57788 background=0.63724 live_coral=0.69134 dead_coral=0.45749 bleached_coral=0.52545
+2026-06-27 18:22:59 epoch=75/120 step=50/637 loss=0.99132 lr=0.00002106
+2026-06-27 18:23:05 epoch=75/120 step=100/637 loss=0.97806 lr=0.00002100
+2026-06-27 18:23:10 epoch=75/120 step=150/637 loss=0.97041 lr=0.00002094
+2026-06-27 18:23:16 epoch=75/120 step=200/637 loss=0.97626 lr=0.00002088
+2026-06-27 18:23:21 epoch=75/120 step=250/637 loss=0.97437 lr=0.00002082
+2026-06-27 18:23:27 epoch=75/120 step=300/637 loss=0.98692 lr=0.00002076
+2026-06-27 18:23:32 epoch=75/120 step=350/637 loss=0.98322 lr=0.00002070
+2026-06-27 18:23:38 epoch=75/120 step=400/637 loss=0.99353 lr=0.00002064
+2026-06-27 18:23:43 epoch=75/120 step=450/637 loss=0.98802 lr=0.00002058
+2026-06-27 18:23:49 epoch=75/120 step=500/637 loss=0.99219 lr=0.00002052
+2026-06-27 18:23:54 epoch=75/120 step=550/637 loss=0.99090 lr=0.00002046
+2026-06-27 18:24:00 epoch=75/120 step=600/637 loss=0.99693 lr=0.00002040
+2026-06-27 18:24:13 convnext val mIoU=0.57838 background=0.62286 live_coral=0.68420 dead_coral=0.48952 bleached_coral=0.51696
+2026-06-27 18:24:20 epoch=76/120 step=50/637 loss=0.96973 lr=0.00002029
+2026-06-27 18:24:26 epoch=76/120 step=100/637 loss=0.97262 lr=0.00002023
+2026-06-27 18:24:31 epoch=76/120 step=150/637 loss=0.97507 lr=0.00002017
+2026-06-27 18:24:37 epoch=76/120 step=200/637 loss=0.98848 lr=0.00002011
+2026-06-27 18:24:42 epoch=76/120 step=250/637 loss=0.98165 lr=0.00002005
+2026-06-27 18:24:48 epoch=76/120 step=300/637 loss=0.97867 lr=0.00001999
+2026-06-27 18:24:53 epoch=76/120 step=350/637 loss=0.98114 lr=0.00001994
+2026-06-27 18:24:59 epoch=76/120 step=400/637 loss=0.99458 lr=0.00001988
+2026-06-27 18:25:04 epoch=76/120 step=450/637 loss=0.99344 lr=0.00001982
+2026-06-27 18:25:10 epoch=76/120 step=500/637 loss=0.98854 lr=0.00001976
+2026-06-27 18:25:16 epoch=76/120 step=550/637 loss=0.98630 lr=0.00001970
+2026-06-27 18:25:21 epoch=76/120 step=600/637 loss=0.99132 lr=0.00001964
+2026-06-27 18:25:34 convnext val mIoU=0.57801 background=0.63223 live_coral=0.68567 dead_coral=0.47275 bleached_coral=0.52137
+2026-06-27 18:25:41 epoch=77/120 step=50/637 loss=0.96931 lr=0.00001953
+2026-06-27 18:25:47 epoch=77/120 step=100/637 loss=0.97165 lr=0.00001947
+2026-06-27 18:25:52 epoch=77/120 step=150/637 loss=0.94827 lr=0.00001942
+2026-06-27 18:25:58 epoch=77/120 step=200/637 loss=0.95647 lr=0.00001936
+2026-06-27 18:26:03 epoch=77/120 step=250/637 loss=0.96697 lr=0.00001930
+2026-06-27 18:26:09 epoch=77/120 step=300/637 loss=0.96121 lr=0.00001924
+2026-06-27 18:26:14 epoch=77/120 step=350/637 loss=0.95711 lr=0.00001918
+2026-06-27 18:26:20 epoch=77/120 step=400/637 loss=0.95235 lr=0.00001912
+2026-06-27 18:26:25 epoch=77/120 step=450/637 loss=0.95645 lr=0.00001906
+2026-06-27 18:26:31 epoch=77/120 step=500/637 loss=0.96555 lr=0.00001900
+2026-06-27 18:26:36 epoch=77/120 step=550/637 loss=0.97278 lr=0.00001894
+2026-06-27 18:26:42 epoch=77/120 step=600/637 loss=0.97847 lr=0.00001888
+2026-06-27 18:26:54 convnext val mIoU=0.58033 background=0.63729 live_coral=0.69409 dead_coral=0.46455 bleached_coral=0.52540
+2026-06-27 18:26:58 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 18:27:03 epoch=78/120 step=50/637 loss=0.99050 lr=0.00001878
+2026-06-27 18:27:09 epoch=78/120 step=100/637 loss=0.99176 lr=0.00001872
+2026-06-27 18:27:14 epoch=78/120 step=150/637 loss=0.97719 lr=0.00001866
+2026-06-27 18:27:20 epoch=78/120 step=200/637 loss=0.95866 lr=0.00001861
+2026-06-27 18:27:25 epoch=78/120 step=250/637 loss=0.95859 lr=0.00001855
+2026-06-27 18:27:31 epoch=78/120 step=300/637 loss=0.94732 lr=0.00001849
+2026-06-27 18:27:37 epoch=78/120 step=350/637 loss=0.94820 lr=0.00001843
+2026-06-27 18:27:42 epoch=78/120 step=400/637 loss=0.95266 lr=0.00001837
+2026-06-27 18:27:48 epoch=78/120 step=450/637 loss=0.95955 lr=0.00001831
+2026-06-27 18:27:53 epoch=78/120 step=500/637 loss=0.95764 lr=0.00001826
+2026-06-27 18:27:59 epoch=78/120 step=550/637 loss=0.96346 lr=0.00001820
+2026-06-27 18:28:05 epoch=78/120 step=600/637 loss=0.96350 lr=0.00001814
+2026-06-27 18:28:17 convnext val mIoU=0.57826 background=0.63383 live_coral=0.69339 dead_coral=0.46721 bleached_coral=0.51861
+2026-06-27 18:28:24 epoch=79/120 step=50/637 loss=0.93638 lr=0.00001804
+2026-06-27 18:28:30 epoch=79/120 step=100/637 loss=0.93714 lr=0.00001798
+2026-06-27 18:28:35 epoch=79/120 step=150/637 loss=0.94479 lr=0.00001792
+2026-06-27 18:28:41 epoch=79/120 step=200/637 loss=0.95222 lr=0.00001786
+2026-06-27 18:28:47 epoch=79/120 step=250/637 loss=0.95780 lr=0.00001781
+2026-06-27 18:28:52 epoch=79/120 step=300/637 loss=0.97477 lr=0.00001775
+2026-06-27 18:28:58 epoch=79/120 step=350/637 loss=0.97566 lr=0.00001769
+2026-06-27 18:29:03 epoch=79/120 step=400/637 loss=0.97469 lr=0.00001763
+2026-06-27 18:29:09 epoch=79/120 step=450/637 loss=0.97438 lr=0.00001758
+2026-06-27 18:29:14 epoch=79/120 step=500/637 loss=0.97442 lr=0.00001752
+2026-06-27 18:29:20 epoch=79/120 step=550/637 loss=0.97386 lr=0.00001746
+2026-06-27 18:29:25 epoch=79/120 step=600/637 loss=0.97236 lr=0.00001740
+2026-06-27 18:29:38 convnext val mIoU=0.57933 background=0.64001 live_coral=0.69602 dead_coral=0.46149 bleached_coral=0.51982
+2026-06-27 18:29:45 epoch=80/120 step=50/637 loss=0.92285 lr=0.00001730
+2026-06-27 18:29:51 epoch=80/120 step=100/637 loss=0.94858 lr=0.00001725
+2026-06-27 18:29:56 epoch=80/120 step=150/637 loss=0.96203 lr=0.00001719
+2026-06-27 18:30:02 epoch=80/120 step=200/637 loss=0.96918 lr=0.00001713
+2026-06-27 18:30:08 epoch=80/120 step=250/637 loss=0.96867 lr=0.00001708
+2026-06-27 18:30:13 epoch=80/120 step=300/637 loss=0.95959 lr=0.00001702
+2026-06-27 18:30:19 epoch=80/120 step=350/637 loss=0.96297 lr=0.00001696
+2026-06-27 18:30:24 epoch=80/120 step=400/637 loss=0.96081 lr=0.00001690
+2026-06-27 18:30:30 epoch=80/120 step=450/637 loss=0.96292 lr=0.00001685
+2026-06-27 18:30:35 epoch=80/120 step=500/637 loss=0.97168 lr=0.00001679
+2026-06-27 18:30:41 epoch=80/120 step=550/637 loss=0.96985 lr=0.00001673
+2026-06-27 18:30:46 epoch=80/120 step=600/637 loss=0.96758 lr=0.00001668
+2026-06-27 18:30:59 convnext val mIoU=0.57787 background=0.63690 live_coral=0.69705 dead_coral=0.46370 bleached_coral=0.51382
+2026-06-27 18:31:06 epoch=81/120 step=50/637 loss=0.96362 lr=0.00001658
+2026-06-27 18:31:12 epoch=81/120 step=100/637 loss=0.96885 lr=0.00001652
+2026-06-27 18:31:17 epoch=81/120 step=150/637 loss=0.97127 lr=0.00001647
+2026-06-27 18:31:23 epoch=81/120 step=200/637 loss=0.98563 lr=0.00001641
+2026-06-27 18:31:28 epoch=81/120 step=250/637 loss=0.98629 lr=0.00001635
+2026-06-27 18:31:34 epoch=81/120 step=300/637 loss=0.98796 lr=0.00001630
+2026-06-27 18:31:39 epoch=81/120 step=350/637 loss=0.97462 lr=0.00001624
+2026-06-27 18:31:45 epoch=81/120 step=400/637 loss=0.96835 lr=0.00001619
+2026-06-27 18:31:51 epoch=81/120 step=450/637 loss=0.97153 lr=0.00001613
+2026-06-27 18:31:56 epoch=81/120 step=500/637 loss=0.96324 lr=0.00001607
+2026-06-27 18:32:02 epoch=81/120 step=550/637 loss=0.96763 lr=0.00001602
+2026-06-27 18:32:07 epoch=81/120 step=600/637 loss=0.96728 lr=0.00001596
+2026-06-27 18:32:20 convnext val mIoU=0.57593 background=0.62479 live_coral=0.69071 dead_coral=0.46932 bleached_coral=0.51891
+2026-06-27 18:32:27 epoch=82/120 step=50/637 loss=0.83317 lr=0.00001587
+2026-06-27 18:32:33 epoch=82/120 step=100/637 loss=0.89736 lr=0.00001581
+2026-06-27 18:32:38 epoch=82/120 step=150/637 loss=0.91639 lr=0.00001575
+2026-06-27 18:32:44 epoch=82/120 step=200/637 loss=0.93361 lr=0.00001570
+2026-06-27 18:32:49 epoch=82/120 step=250/637 loss=0.94486 lr=0.00001564
+2026-06-27 18:32:55 epoch=82/120 step=300/637 loss=0.94906 lr=0.00001559
+2026-06-27 18:33:00 epoch=82/120 step=350/637 loss=0.94037 lr=0.00001553
+2026-06-27 18:33:06 epoch=82/120 step=400/637 loss=0.93740 lr=0.00001548
+2026-06-27 18:33:12 epoch=82/120 step=450/637 loss=0.94732 lr=0.00001542
+2026-06-27 18:33:17 epoch=82/120 step=500/637 loss=0.94615 lr=0.00001537
+2026-06-27 18:33:23 epoch=82/120 step=550/637 loss=0.94144 lr=0.00001531
+2026-06-27 18:33:28 epoch=82/120 step=600/637 loss=0.94576 lr=0.00001526
+2026-06-27 18:33:41 convnext val mIoU=0.57746 background=0.62749 live_coral=0.69078 dead_coral=0.47333 bleached_coral=0.51823
+2026-06-27 18:33:48 epoch=83/120 step=50/637 loss=0.88750 lr=0.00001516
+2026-06-27 18:33:53 epoch=83/120 step=100/637 loss=0.91891 lr=0.00001511
+2026-06-27 18:33:59 epoch=83/120 step=150/637 loss=0.94764 lr=0.00001505
+2026-06-27 18:34:04 epoch=83/120 step=200/637 loss=0.95597 lr=0.00001500
+2026-06-27 18:34:10 epoch=83/120 step=250/637 loss=0.95691 lr=0.00001494
+2026-06-27 18:34:15 epoch=83/120 step=300/637 loss=0.94726 lr=0.00001489
+2026-06-27 18:34:21 epoch=83/120 step=350/637 loss=0.95234 lr=0.00001483
+2026-06-27 18:34:26 epoch=83/120 step=400/637 loss=0.95201 lr=0.00001478
+2026-06-27 18:34:32 epoch=83/120 step=450/637 loss=0.95438 lr=0.00001473
+2026-06-27 18:34:37 epoch=83/120 step=500/637 loss=0.95586 lr=0.00001467
+2026-06-27 18:34:43 epoch=83/120 step=550/637 loss=0.96210 lr=0.00001462
+2026-06-27 18:34:49 epoch=83/120 step=600/637 loss=0.96042 lr=0.00001456
+2026-06-27 18:35:02 convnext val mIoU=0.57323 background=0.63180 live_coral=0.68928 dead_coral=0.45266 bleached_coral=0.51916
+2026-06-27 18:35:09 epoch=84/120 step=50/637 loss=0.98635 lr=0.00001447
+2026-06-27 18:35:14 epoch=84/120 step=100/637 loss=0.96406 lr=0.00001442
+2026-06-27 18:35:20 epoch=84/120 step=150/637 loss=0.96981 lr=0.00001436
+2026-06-27 18:35:25 epoch=84/120 step=200/637 loss=0.97890 lr=0.00001431
+2026-06-27 18:35:31 epoch=84/120 step=250/637 loss=0.97764 lr=0.00001425
+2026-06-27 18:35:36 epoch=84/120 step=300/637 loss=0.97487 lr=0.00001420
+2026-06-27 18:35:42 epoch=84/120 step=350/637 loss=0.97451 lr=0.00001415
+2026-06-27 18:35:47 epoch=84/120 step=400/637 loss=0.96859 lr=0.00001409
+2026-06-27 18:35:53 epoch=84/120 step=450/637 loss=0.96573 lr=0.00001404
+2026-06-27 18:35:59 epoch=84/120 step=500/637 loss=0.96267 lr=0.00001399
+2026-06-27 18:36:04 epoch=84/120 step=550/637 loss=0.95704 lr=0.00001393
+2026-06-27 18:36:10 epoch=84/120 step=600/637 loss=0.95817 lr=0.00001388
+2026-06-27 18:36:22 convnext val mIoU=0.57714 background=0.63333 live_coral=0.68921 dead_coral=0.46484 bleached_coral=0.52117
+2026-06-27 18:36:30 epoch=85/120 step=50/637 loss=0.91701 lr=0.00001379
+2026-06-27 18:36:36 epoch=85/120 step=100/637 loss=0.90249 lr=0.00001374
+2026-06-27 18:36:41 epoch=85/120 step=150/637 loss=0.92728 lr=0.00001368
+2026-06-27 18:36:47 epoch=85/120 step=200/637 loss=0.93940 lr=0.00001363
+2026-06-27 18:36:52 epoch=85/120 step=250/637 loss=0.93378 lr=0.00001358
+2026-06-27 18:36:58 epoch=85/120 step=300/637 loss=0.93204 lr=0.00001353
+2026-06-27 18:37:03 epoch=85/120 step=350/637 loss=0.94661 lr=0.00001347
+2026-06-27 18:37:09 epoch=85/120 step=400/637 loss=0.95479 lr=0.00001342
+2026-06-27 18:37:14 epoch=85/120 step=450/637 loss=0.95705 lr=0.00001337
+2026-06-27 18:37:20 epoch=85/120 step=500/637 loss=0.95318 lr=0.00001332
+2026-06-27 18:37:25 epoch=85/120 step=550/637 loss=0.95642 lr=0.00001326
+2026-06-27 18:37:31 epoch=85/120 step=600/637 loss=0.95725 lr=0.00001321
+2026-06-27 18:37:44 convnext val mIoU=0.57288 background=0.63307 live_coral=0.67703 dead_coral=0.47161 bleached_coral=0.50980
+2026-06-27 18:37:51 epoch=86/120 step=50/637 loss=0.99026 lr=0.00001312
+2026-06-27 18:37:56 epoch=86/120 step=100/637 loss=0.99899 lr=0.00001307
+2026-06-27 18:38:02 epoch=86/120 step=150/637 loss=0.97571 lr=0.00001302
+2026-06-27 18:38:07 epoch=86/120 step=200/637 loss=0.98116 lr=0.00001297
+2026-06-27 18:38:13 epoch=86/120 step=250/637 loss=0.98913 lr=0.00001291
+2026-06-27 18:38:19 epoch=86/120 step=300/637 loss=0.98384 lr=0.00001286
+2026-06-27 18:38:24 epoch=86/120 step=350/637 loss=0.97371 lr=0.00001281
+2026-06-27 18:38:30 epoch=86/120 step=400/637 loss=0.95924 lr=0.00001276
+2026-06-27 18:38:35 epoch=86/120 step=450/637 loss=0.95783 lr=0.00001271
+2026-06-27 18:38:40 epoch=86/120 step=500/637 loss=0.95696 lr=0.00001266
+2026-06-27 18:38:46 epoch=86/120 step=550/637 loss=0.95686 lr=0.00001261
+2026-06-27 18:38:52 epoch=86/120 step=600/637 loss=0.95080 lr=0.00001255
+2026-06-27 18:39:04 convnext val mIoU=0.57947 background=0.63219 live_coral=0.69360 dead_coral=0.47242 bleached_coral=0.51969
+2026-06-27 18:39:11 epoch=87/120 step=50/637 loss=0.98453 lr=0.00001247
+2026-06-27 18:39:17 epoch=87/120 step=100/637 loss=0.98342 lr=0.00001241
+2026-06-27 18:39:22 epoch=87/120 step=150/637 loss=0.95374 lr=0.00001236
+2026-06-27 18:39:28 epoch=87/120 step=200/637 loss=0.94312 lr=0.00001231
+2026-06-27 18:39:33 epoch=87/120 step=250/637 loss=0.94377 lr=0.00001226
+2026-06-27 18:39:39 epoch=87/120 step=300/637 loss=0.94390 lr=0.00001221
+2026-06-27 18:39:44 epoch=87/120 step=350/637 loss=0.94641 lr=0.00001216
+2026-06-27 18:39:50 epoch=87/120 step=400/637 loss=0.94318 lr=0.00001211
+2026-06-27 18:39:55 epoch=87/120 step=450/637 loss=0.94219 lr=0.00001206
+2026-06-27 18:40:01 epoch=87/120 step=500/637 loss=0.94085 lr=0.00001201
+2026-06-27 18:40:06 epoch=87/120 step=550/637 loss=0.94040 lr=0.00001196
+2026-06-27 18:40:12 epoch=87/120 step=600/637 loss=0.93742 lr=0.00001191
+2026-06-27 18:40:25 convnext val mIoU=0.57737 background=0.63522 live_coral=0.68726 dead_coral=0.46948 bleached_coral=0.51751
+2026-06-27 18:40:31 epoch=88/120 step=50/637 loss=0.97361 lr=0.00001182
+2026-06-27 18:40:37 epoch=88/120 step=100/637 loss=0.95998 lr=0.00001177
+2026-06-27 18:40:43 epoch=88/120 step=150/637 loss=0.95222 lr=0.00001172
+2026-06-27 18:40:48 epoch=88/120 step=200/637 loss=0.95908 lr=0.00001167
+2026-06-27 18:40:54 epoch=88/120 step=250/637 loss=0.95168 lr=0.00001162
+2026-06-27 18:40:59 epoch=88/120 step=300/637 loss=0.94954 lr=0.00001157
+2026-06-27 18:41:05 epoch=88/120 step=350/637 loss=0.94761 lr=0.00001153
+2026-06-27 18:41:11 epoch=88/120 step=400/637 loss=0.95026 lr=0.00001148
+2026-06-27 18:41:16 epoch=88/120 step=450/637 loss=0.94910 lr=0.00001143
+2026-06-27 18:41:22 epoch=88/120 step=500/637 loss=0.94892 lr=0.00001138
+2026-06-27 18:41:27 epoch=88/120 step=550/637 loss=0.95061 lr=0.00001133
+2026-06-27 18:41:33 epoch=88/120 step=600/637 loss=0.94968 lr=0.00001128
+2026-06-27 18:41:46 convnext val mIoU=0.57597 background=0.63444 live_coral=0.68412 dead_coral=0.47342 bleached_coral=0.51191
+2026-06-27 18:41:52 epoch=89/120 step=50/637 loss=0.92305 lr=0.00001119
+2026-06-27 18:41:58 epoch=89/120 step=100/637 loss=0.91838 lr=0.00001115
+2026-06-27 18:42:03 epoch=89/120 step=150/637 loss=0.92312 lr=0.00001110
+2026-06-27 18:42:09 epoch=89/120 step=200/637 loss=0.92370 lr=0.00001105
+2026-06-27 18:42:14 epoch=89/120 step=250/637 loss=0.92198 lr=0.00001100
+2026-06-27 18:42:20 epoch=89/120 step=300/637 loss=0.93869 lr=0.00001095
+2026-06-27 18:42:26 epoch=89/120 step=350/637 loss=0.93156 lr=0.00001090
+2026-06-27 18:42:31 epoch=89/120 step=400/637 loss=0.93977 lr=0.00001086
+2026-06-27 18:42:37 epoch=89/120 step=450/637 loss=0.93256 lr=0.00001081
+2026-06-27 18:42:42 epoch=89/120 step=500/637 loss=0.93288 lr=0.00001076
+2026-06-27 18:42:48 epoch=89/120 step=550/637 loss=0.93254 lr=0.00001071
+2026-06-27 18:42:54 epoch=89/120 step=600/637 loss=0.93357 lr=0.00001066
+2026-06-27 18:43:06 convnext val mIoU=0.57832 background=0.63297 live_coral=0.69452 dead_coral=0.46681 bleached_coral=0.51898
+2026-06-27 18:43:14 epoch=90/120 step=50/637 loss=0.89643 lr=0.00001058
+2026-06-27 18:43:19 epoch=90/120 step=100/637 loss=0.89270 lr=0.00001053
+2026-06-27 18:43:24 epoch=90/120 step=150/637 loss=0.93332 lr=0.00001049
+2026-06-27 18:43:30 epoch=90/120 step=200/637 loss=0.93895 lr=0.00001044
+2026-06-27 18:43:36 epoch=90/120 step=250/637 loss=0.95042 lr=0.00001039
+2026-06-27 18:43:41 epoch=90/120 step=300/637 loss=0.94580 lr=0.00001034
+2026-06-27 18:43:47 epoch=90/120 step=350/637 loss=0.95197 lr=0.00001030
+2026-06-27 18:43:52 epoch=90/120 step=400/637 loss=0.94988 lr=0.00001025
+2026-06-27 18:43:58 epoch=90/120 step=450/637 loss=0.95441 lr=0.00001020
+2026-06-27 18:44:03 epoch=90/120 step=500/637 loss=0.94727 lr=0.00001016
+2026-06-27 18:44:09 epoch=90/120 step=550/637 loss=0.94296 lr=0.00001011
+2026-06-27 18:44:14 epoch=90/120 step=600/637 loss=0.94486 lr=0.00001006
+2026-06-27 18:44:27 convnext val mIoU=0.57581 background=0.63676 live_coral=0.68894 dead_coral=0.46696 bleached_coral=0.51058
+2026-06-27 18:44:35 epoch=91/120 step=50/637 loss=0.88629 lr=0.00000998
+2026-06-27 18:44:41 epoch=91/120 step=100/637 loss=0.93344 lr=0.00000994
+2026-06-27 18:44:46 epoch=91/120 step=150/637 loss=0.90958 lr=0.00000989
+2026-06-27 18:44:52 epoch=91/120 step=200/637 loss=0.92032 lr=0.00000984
+2026-06-27 18:44:57 epoch=91/120 step=250/637 loss=0.91255 lr=0.00000980
+2026-06-27 18:45:03 epoch=91/120 step=300/637 loss=0.92215 lr=0.00000975
+2026-06-27 18:45:08 epoch=91/120 step=350/637 loss=0.92842 lr=0.00000970
+2026-06-27 18:45:14 epoch=91/120 step=400/637 loss=0.94160 lr=0.00000966
+2026-06-27 18:45:20 epoch=91/120 step=450/637 loss=0.94393 lr=0.00000961
+2026-06-27 18:45:25 epoch=91/120 step=500/637 loss=0.94726 lr=0.00000957
+2026-06-27 18:45:30 epoch=91/120 step=550/637 loss=0.94620 lr=0.00000952
+2026-06-27 18:45:36 epoch=91/120 step=600/637 loss=0.94353 lr=0.00000948
+2026-06-27 18:45:49 convnext val mIoU=0.57780 background=0.63525 live_coral=0.69106 dead_coral=0.47151 bleached_coral=0.51338
+2026-06-27 18:45:56 epoch=92/120 step=50/637 loss=0.96351 lr=0.00000940
+2026-06-27 18:46:02 epoch=92/120 step=100/637 loss=0.93430 lr=0.00000935
+2026-06-27 18:46:07 epoch=92/120 step=150/637 loss=0.96545 lr=0.00000931
+2026-06-27 18:46:13 epoch=92/120 step=200/637 loss=0.95854 lr=0.00000926
+2026-06-27 18:46:18 epoch=92/120 step=250/637 loss=0.93788 lr=0.00000922
+2026-06-27 18:46:24 epoch=92/120 step=300/637 loss=0.93794 lr=0.00000917
+2026-06-27 18:46:29 epoch=92/120 step=350/637 loss=0.93910 lr=0.00000913
+2026-06-27 18:46:35 epoch=92/120 step=400/637 loss=0.93803 lr=0.00000908
+2026-06-27 18:46:40 epoch=92/120 step=450/637 loss=0.94587 lr=0.00000904
+2026-06-27 18:46:46 epoch=92/120 step=500/637 loss=0.93935 lr=0.00000899
+2026-06-27 18:46:51 epoch=92/120 step=550/637 loss=0.93812 lr=0.00000895
+2026-06-27 18:46:57 epoch=92/120 step=600/637 loss=0.93735 lr=0.00000891
+2026-06-27 18:47:09 convnext val mIoU=0.57376 background=0.63956 live_coral=0.69205 dead_coral=0.45148 bleached_coral=0.51195
+2026-06-27 18:47:16 epoch=93/120 step=50/637 loss=0.96764 lr=0.00000883
+2026-06-27 18:47:22 epoch=93/120 step=100/637 loss=0.94102 lr=0.00000879
+2026-06-27 18:47:27 epoch=93/120 step=150/637 loss=0.93186 lr=0.00000874
+2026-06-27 18:47:33 epoch=93/120 step=200/637 loss=0.92965 lr=0.00000870
+2026-06-27 18:47:38 epoch=93/120 step=250/637 loss=0.92371 lr=0.00000865
+2026-06-27 18:47:44 epoch=93/120 step=300/637 loss=0.92854 lr=0.00000861
+2026-06-27 18:47:49 epoch=93/120 step=350/637 loss=0.93222 lr=0.00000857
+2026-06-27 18:47:55 epoch=93/120 step=400/637 loss=0.92585 lr=0.00000852
+2026-06-27 18:48:00 epoch=93/120 step=450/637 loss=0.92847 lr=0.00000848
+2026-06-27 18:48:06 epoch=93/120 step=500/637 loss=0.92620 lr=0.00000844
+2026-06-27 18:48:11 epoch=93/120 step=550/637 loss=0.92598 lr=0.00000839
+2026-06-27 18:48:17 epoch=93/120 step=600/637 loss=0.92913 lr=0.00000835
+2026-06-27 18:48:30 convnext val mIoU=0.58056 background=0.63496 live_coral=0.69506 dead_coral=0.47411 bleached_coral=0.51811
+2026-06-27 18:48:33 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 18:48:38 epoch=94/120 step=50/637 loss=0.93089 lr=0.00000828
+2026-06-27 18:48:44 epoch=94/120 step=100/637 loss=0.94174 lr=0.00000823
+2026-06-27 18:48:50 epoch=94/120 step=150/637 loss=0.91714 lr=0.00000819
+2026-06-27 18:48:55 epoch=94/120 step=200/637 loss=0.91852 lr=0.00000815
+2026-06-27 18:49:01 epoch=94/120 step=250/637 loss=0.92139 lr=0.00000811
+2026-06-27 18:49:06 epoch=94/120 step=300/637 loss=0.94093 lr=0.00000806
+2026-06-27 18:49:12 epoch=94/120 step=350/637 loss=0.93481 lr=0.00000802
+2026-06-27 18:49:18 epoch=94/120 step=400/637 loss=0.93650 lr=0.00000798
+2026-06-27 18:49:23 epoch=94/120 step=450/637 loss=0.93913 lr=0.00000794
+2026-06-27 18:49:29 epoch=94/120 step=500/637 loss=0.93720 lr=0.00000790
+2026-06-27 18:49:34 epoch=94/120 step=550/637 loss=0.93969 lr=0.00000785
+2026-06-27 18:49:39 epoch=94/120 step=600/637 loss=0.93795 lr=0.00000781
+2026-06-27 18:49:52 convnext val mIoU=0.57644 background=0.63709 live_coral=0.69024 dead_coral=0.46215 bleached_coral=0.51628
+2026-06-27 18:49:59 epoch=95/120 step=50/637 loss=0.97895 lr=0.00000774
+2026-06-27 18:50:05 epoch=95/120 step=100/637 loss=0.93968 lr=0.00000770
+2026-06-27 18:50:10 epoch=95/120 step=150/637 loss=0.92664 lr=0.00000766
+2026-06-27 18:50:16 epoch=95/120 step=200/637 loss=0.90533 lr=0.00000762
+2026-06-27 18:50:21 epoch=95/120 step=250/637 loss=0.90324 lr=0.00000758
+2026-06-27 18:50:27 epoch=95/120 step=300/637 loss=0.91171 lr=0.00000753
+2026-06-27 18:50:33 epoch=95/120 step=350/637 loss=0.91477 lr=0.00000749
+2026-06-27 18:50:38 epoch=95/120 step=400/637 loss=0.91275 lr=0.00000745
+2026-06-27 18:50:44 epoch=95/120 step=450/637 loss=0.91183 lr=0.00000741
+2026-06-27 18:50:49 epoch=95/120 step=500/637 loss=0.91305 lr=0.00000737
+2026-06-27 18:50:55 epoch=95/120 step=550/637 loss=0.91256 lr=0.00000733
+2026-06-27 18:51:00 epoch=95/120 step=600/637 loss=0.91203 lr=0.00000729
+2026-06-27 18:51:13 convnext val mIoU=0.57562 background=0.63338 live_coral=0.69196 dead_coral=0.46001 bleached_coral=0.51713
+2026-06-27 18:51:20 epoch=96/120 step=50/637 loss=0.89810 lr=0.00000722
+2026-06-27 18:51:26 epoch=96/120 step=100/637 loss=0.90339 lr=0.00000718
+2026-06-27 18:51:31 epoch=96/120 step=150/637 loss=0.91701 lr=0.00000714
+2026-06-27 18:51:37 epoch=96/120 step=200/637 loss=0.91952 lr=0.00000710
+2026-06-27 18:51:43 epoch=96/120 step=250/637 loss=0.91866 lr=0.00000706
+2026-06-27 18:51:48 epoch=96/120 step=300/637 loss=0.91586 lr=0.00000702
+2026-06-27 18:51:53 epoch=96/120 step=350/637 loss=0.91059 lr=0.00000698
+2026-06-27 18:51:59 epoch=96/120 step=400/637 loss=0.91668 lr=0.00000694
+2026-06-27 18:52:04 epoch=96/120 step=450/637 loss=0.92361 lr=0.00000690
+2026-06-27 18:52:10 epoch=96/120 step=500/637 loss=0.92833 lr=0.00000686
+2026-06-27 18:52:15 epoch=96/120 step=550/637 loss=0.92741 lr=0.00000683
+2026-06-27 18:52:21 epoch=96/120 step=600/637 loss=0.92965 lr=0.00000679
+2026-06-27 18:52:33 convnext val mIoU=0.57735 background=0.63725 live_coral=0.69079 dead_coral=0.46305 bleached_coral=0.51829
+2026-06-27 18:52:41 epoch=97/120 step=50/637 loss=0.88977 lr=0.00000672
+2026-06-27 18:52:46 epoch=97/120 step=100/637 loss=0.93706 lr=0.00000668
+2026-06-27 18:52:52 epoch=97/120 step=150/637 loss=0.93214 lr=0.00000664
+2026-06-27 18:52:57 epoch=97/120 step=200/637 loss=0.94367 lr=0.00000660
+2026-06-27 18:53:03 epoch=97/120 step=250/637 loss=0.94201 lr=0.00000657
+2026-06-27 18:53:08 epoch=97/120 step=300/637 loss=0.93333 lr=0.00000653
+2026-06-27 18:53:14 epoch=97/120 step=350/637 loss=0.92793 lr=0.00000649
+2026-06-27 18:53:19 epoch=97/120 step=400/637 loss=0.93143 lr=0.00000645
+2026-06-27 18:53:25 epoch=97/120 step=450/637 loss=0.92306 lr=0.00000641
+2026-06-27 18:53:30 epoch=97/120 step=500/637 loss=0.92366 lr=0.00000638
+2026-06-27 18:53:36 epoch=97/120 step=550/637 loss=0.91650 lr=0.00000634
+2026-06-27 18:53:41 epoch=97/120 step=600/637 loss=0.91584 lr=0.00000630
+2026-06-27 18:53:54 convnext val mIoU=0.57644 background=0.63147 live_coral=0.68428 dead_coral=0.48002 bleached_coral=0.50998
+2026-06-27 18:54:01 epoch=98/120 step=50/637 loss=0.93175 lr=0.00000623
+2026-06-27 18:54:07 epoch=98/120 step=100/637 loss=0.92072 lr=0.00000620
+2026-06-27 18:54:12 epoch=98/120 step=150/637 loss=0.90146 lr=0.00000616
+2026-06-27 18:54:18 epoch=98/120 step=200/637 loss=0.91002 lr=0.00000612
+2026-06-27 18:54:23 epoch=98/120 step=250/637 loss=0.90124 lr=0.00000609
+2026-06-27 18:54:28 epoch=98/120 step=300/637 loss=0.90249 lr=0.00000605
+2026-06-27 18:54:34 epoch=98/120 step=350/637 loss=0.89984 lr=0.00000601
+2026-06-27 18:54:39 epoch=98/120 step=400/637 loss=0.90565 lr=0.00000598
+2026-06-27 18:54:45 epoch=98/120 step=450/637 loss=0.90726 lr=0.00000594
+2026-06-27 18:54:50 epoch=98/120 step=500/637 loss=0.91220 lr=0.00000590
+2026-06-27 18:54:56 epoch=98/120 step=550/637 loss=0.91040 lr=0.00000587
+2026-06-27 18:55:01 epoch=98/120 step=600/637 loss=0.90991 lr=0.00000583
+2026-06-27 18:55:14 convnext val mIoU=0.57784 background=0.63514 live_coral=0.68804 dead_coral=0.47350 bleached_coral=0.51468
+2026-06-27 18:55:21 epoch=99/120 step=50/637 loss=0.94303 lr=0.00000577
+2026-06-27 18:55:26 epoch=99/120 step=100/637 loss=0.98074 lr=0.00000573
+2026-06-27 18:55:32 epoch=99/120 step=150/637 loss=0.95056 lr=0.00000570
+2026-06-27 18:55:37 epoch=99/120 step=200/637 loss=0.95014 lr=0.00000566
+2026-06-27 18:55:43 epoch=99/120 step=250/637 loss=0.94433 lr=0.00000563
+2026-06-27 18:55:48 epoch=99/120 step=300/637 loss=0.94904 lr=0.00000559
+2026-06-27 18:55:54 epoch=99/120 step=350/637 loss=0.93900 lr=0.00000556
+2026-06-27 18:55:59 epoch=99/120 step=400/637 loss=0.93757 lr=0.00000552
+2026-06-27 18:56:05 epoch=99/120 step=450/637 loss=0.93531 lr=0.00000548
+2026-06-27 18:56:10 epoch=99/120 step=500/637 loss=0.93344 lr=0.00000545
+2026-06-27 18:56:16 epoch=99/120 step=550/637 loss=0.92901 lr=0.00000542
+2026-06-27 18:56:22 epoch=99/120 step=600/637 loss=0.92475 lr=0.00000538
+2026-06-27 18:56:34 convnext val mIoU=0.57737 background=0.63287 live_coral=0.68991 dead_coral=0.47306 bleached_coral=0.51365
+2026-06-27 18:56:41 epoch=100/120 step=50/637 loss=0.87408 lr=0.00000532
+2026-06-27 18:56:47 epoch=100/120 step=100/637 loss=0.85692 lr=0.00000529
+2026-06-27 18:56:52 epoch=100/120 step=150/637 loss=0.85499 lr=0.00000525
+2026-06-27 18:56:58 epoch=100/120 step=200/637 loss=0.87493 lr=0.00000522
+2026-06-27 18:57:03 epoch=100/120 step=250/637 loss=0.87735 lr=0.00000518
+2026-06-27 18:57:09 epoch=100/120 step=300/637 loss=0.88446 lr=0.00000515
+2026-06-27 18:57:14 epoch=100/120 step=350/637 loss=0.88585 lr=0.00000512
+2026-06-27 18:57:20 epoch=100/120 step=400/637 loss=0.89006 lr=0.00000508
+2026-06-27 18:57:25 epoch=100/120 step=450/637 loss=0.89104 lr=0.00000505
+2026-06-27 18:57:31 epoch=100/120 step=500/637 loss=0.89422 lr=0.00000501
+2026-06-27 18:57:36 epoch=100/120 step=550/637 loss=0.90238 lr=0.00000498
+2026-06-27 18:57:42 epoch=100/120 step=600/637 loss=0.91209 lr=0.00000495
+2026-06-27 18:57:54 convnext val mIoU=0.57943 background=0.63654 live_coral=0.69353 dead_coral=0.46906 bleached_coral=0.51858
+2026-06-27 18:58:01 epoch=101/120 step=50/637 loss=0.87252 lr=0.00000489
+2026-06-27 18:58:07 epoch=101/120 step=100/637 loss=0.92498 lr=0.00000486
+2026-06-27 18:58:12 epoch=101/120 step=150/637 loss=0.94578 lr=0.00000482
+2026-06-27 18:58:18 epoch=101/120 step=200/637 loss=0.92995 lr=0.00000479
+2026-06-27 18:58:24 epoch=101/120 step=250/637 loss=0.93344 lr=0.00000476
+2026-06-27 18:58:29 epoch=101/120 step=300/637 loss=0.91786 lr=0.00000473
+2026-06-27 18:58:35 epoch=101/120 step=350/637 loss=0.90961 lr=0.00000469
+2026-06-27 18:58:40 epoch=101/120 step=400/637 loss=0.91226 lr=0.00000466
+2026-06-27 18:58:46 epoch=101/120 step=450/637 loss=0.91222 lr=0.00000463
+2026-06-27 18:58:51 epoch=101/120 step=500/637 loss=0.91674 lr=0.00000460
+2026-06-27 18:58:57 epoch=101/120 step=550/637 loss=0.91593 lr=0.00000457
+2026-06-27 18:59:02 epoch=101/120 step=600/637 loss=0.91707 lr=0.00000454
+2026-06-27 18:59:15 convnext val mIoU=0.57793 background=0.63860 live_coral=0.69228 dead_coral=0.46427 bleached_coral=0.51657
+2026-06-27 18:59:22 epoch=102/120 step=50/637 loss=0.87684 lr=0.00000448
+2026-06-27 18:59:27 epoch=102/120 step=100/637 loss=0.89334 lr=0.00000445
+2026-06-27 18:59:33 epoch=102/120 step=150/637 loss=0.91806 lr=0.00000442
+2026-06-27 18:59:38 epoch=102/120 step=200/637 loss=0.90287 lr=0.00000439
+2026-06-27 18:59:44 epoch=102/120 step=250/637 loss=0.90348 lr=0.00000436
+2026-06-27 18:59:49 epoch=102/120 step=300/637 loss=0.91478 lr=0.00000432
+2026-06-27 18:59:55 epoch=102/120 step=350/637 loss=0.91570 lr=0.00000429
+2026-06-27 19:00:00 epoch=102/120 step=400/637 loss=0.91457 lr=0.00000426
+2026-06-27 19:00:05 epoch=102/120 step=450/637 loss=0.91214 lr=0.00000423
+2026-06-27 19:00:11 epoch=102/120 step=500/637 loss=0.91294 lr=0.00000420
+2026-06-27 19:00:17 epoch=102/120 step=550/637 loss=0.91617 lr=0.00000417
+2026-06-27 19:00:22 epoch=102/120 step=600/637 loss=0.91962 lr=0.00000414
+2026-06-27 19:00:35 convnext val mIoU=0.57916 background=0.63693 live_coral=0.69002 dead_coral=0.47443 bleached_coral=0.51525
+2026-06-27 19:00:42 epoch=103/120 step=50/637 loss=0.90812 lr=0.00000409
+2026-06-27 19:00:47 epoch=103/120 step=100/637 loss=0.91712 lr=0.00000406
+2026-06-27 19:00:53 epoch=103/120 step=150/637 loss=0.92114 lr=0.00000403
+2026-06-27 19:00:58 epoch=103/120 step=200/637 loss=0.92512 lr=0.00000400
+2026-06-27 19:01:04 epoch=103/120 step=250/637 loss=0.92195 lr=0.00000397
+2026-06-27 19:01:09 epoch=103/120 step=300/637 loss=0.92277 lr=0.00000394
+2026-06-27 19:01:15 epoch=103/120 step=350/637 loss=0.92366 lr=0.00000391
+2026-06-27 19:01:20 epoch=103/120 step=400/637 loss=0.92431 lr=0.00000388
+2026-06-27 19:01:26 epoch=103/120 step=450/637 loss=0.91991 lr=0.00000385
+2026-06-27 19:01:32 epoch=103/120 step=500/637 loss=0.92286 lr=0.00000382
+2026-06-27 19:01:37 epoch=103/120 step=550/637 loss=0.92225 lr=0.00000380
+2026-06-27 19:01:43 epoch=103/120 step=600/637 loss=0.92209 lr=0.00000377
+2026-06-27 19:01:55 convnext val mIoU=0.57872 background=0.63843 live_coral=0.68849 dead_coral=0.46997 bleached_coral=0.51800
+2026-06-27 19:02:02 epoch=104/120 step=50/637 loss=0.89156 lr=0.00000372
+2026-06-27 19:02:08 epoch=104/120 step=100/637 loss=0.90465 lr=0.00000369
+2026-06-27 19:02:13 epoch=104/120 step=150/637 loss=0.90118 lr=0.00000366
+2026-06-27 19:02:19 epoch=104/120 step=200/637 loss=0.90737 lr=0.00000363
+2026-06-27 19:02:24 epoch=104/120 step=250/637 loss=0.91738 lr=0.00000360
+2026-06-27 19:02:29 epoch=104/120 step=300/637 loss=0.91786 lr=0.00000358
+2026-06-27 19:02:35 epoch=104/120 step=350/637 loss=0.93013 lr=0.00000355
+2026-06-27 19:02:41 epoch=104/120 step=400/637 loss=0.92764 lr=0.00000352
+2026-06-27 19:02:46 epoch=104/120 step=450/637 loss=0.92437 lr=0.00000349
+2026-06-27 19:02:52 epoch=104/120 step=500/637 loss=0.92135 lr=0.00000347
+2026-06-27 19:02:57 epoch=104/120 step=550/637 loss=0.92107 lr=0.00000344
+2026-06-27 19:03:03 epoch=104/120 step=600/637 loss=0.91859 lr=0.00000341
+2026-06-27 19:03:16 convnext val mIoU=0.57979 background=0.63830 live_coral=0.69088 dead_coral=0.47221 bleached_coral=0.51774
+2026-06-27 19:03:22 epoch=105/120 step=50/637 loss=0.88496 lr=0.00000337
+2026-06-27 19:03:28 epoch=105/120 step=100/637 loss=0.89158 lr=0.00000334
+2026-06-27 19:03:33 epoch=105/120 step=150/637 loss=0.90079 lr=0.00000331
+2026-06-27 19:03:39 epoch=105/120 step=200/637 loss=0.91376 lr=0.00000329
+2026-06-27 19:03:44 epoch=105/120 step=250/637 loss=0.91033 lr=0.00000326
+2026-06-27 19:03:50 epoch=105/120 step=300/637 loss=0.90878 lr=0.00000323
+2026-06-27 19:03:55 epoch=105/120 step=350/637 loss=0.91328 lr=0.00000321
+2026-06-27 19:04:01 epoch=105/120 step=400/637 loss=0.91189 lr=0.00000318
+2026-06-27 19:04:06 epoch=105/120 step=450/637 loss=0.90851 lr=0.00000315
+2026-06-27 19:04:12 epoch=105/120 step=500/637 loss=0.90492 lr=0.00000313
+2026-06-27 19:04:17 epoch=105/120 step=550/637 loss=0.91312 lr=0.00000310
+2026-06-27 19:04:23 epoch=105/120 step=600/637 loss=0.91382 lr=0.00000308
+2026-06-27 19:04:35 convnext val mIoU=0.58014 background=0.63679 live_coral=0.69119 dead_coral=0.47558 bleached_coral=0.51699
+2026-06-27 19:04:42 epoch=106/120 step=50/637 loss=0.92615 lr=0.00000303
+2026-06-27 19:04:48 epoch=106/120 step=100/637 loss=0.89086 lr=0.00000301
+2026-06-27 19:04:53 epoch=106/120 step=150/637 loss=0.91465 lr=0.00000298
+2026-06-27 19:04:59 epoch=106/120 step=200/637 loss=0.92356 lr=0.00000296
+2026-06-27 19:05:04 epoch=106/120 step=250/637 loss=0.92970 lr=0.00000293
+2026-06-27 19:05:10 epoch=106/120 step=300/637 loss=0.92236 lr=0.00000291
+2026-06-27 19:05:15 epoch=106/120 step=350/637 loss=0.91450 lr=0.00000288
+2026-06-27 19:05:21 epoch=106/120 step=400/637 loss=0.91049 lr=0.00000286
+2026-06-27 19:05:26 epoch=106/120 step=450/637 loss=0.91557 lr=0.00000284
+2026-06-27 19:05:32 epoch=106/120 step=500/637 loss=0.91547 lr=0.00000281
+2026-06-27 19:05:37 epoch=106/120 step=550/637 loss=0.90819 lr=0.00000279
+2026-06-27 19:05:43 epoch=106/120 step=600/637 loss=0.90820 lr=0.00000276
+2026-06-27 19:05:55 convnext val mIoU=0.57997 background=0.63693 live_coral=0.69464 dead_coral=0.46929 bleached_coral=0.51904
+2026-06-27 19:06:02 epoch=107/120 step=50/637 loss=0.95449 lr=0.00000272
+2026-06-27 19:06:08 epoch=107/120 step=100/637 loss=0.94127 lr=0.00000270
+2026-06-27 19:06:14 epoch=107/120 step=150/637 loss=0.93336 lr=0.00000268
+2026-06-27 19:06:19 epoch=107/120 step=200/637 loss=0.93258 lr=0.00000265
+2026-06-27 19:06:25 epoch=107/120 step=250/637 loss=0.92087 lr=0.00000263
+2026-06-27 19:06:30 epoch=107/120 step=300/637 loss=0.92510 lr=0.00000261
+2026-06-27 19:06:36 epoch=107/120 step=350/637 loss=0.92143 lr=0.00000258
+2026-06-27 19:06:41 epoch=107/120 step=400/637 loss=0.91501 lr=0.00000256
+2026-06-27 19:06:47 epoch=107/120 step=450/637 loss=0.91288 lr=0.00000254
+2026-06-27 19:06:52 epoch=107/120 step=500/637 loss=0.91222 lr=0.00000251
+2026-06-27 19:06:58 epoch=107/120 step=550/637 loss=0.91356 lr=0.00000249
+2026-06-27 19:07:03 epoch=107/120 step=600/637 loss=0.90997 lr=0.00000247
+2026-06-27 19:07:16 convnext val mIoU=0.58002 background=0.63600 live_coral=0.69658 dead_coral=0.46766 bleached_coral=0.51985
+2026-06-27 19:07:23 epoch=108/120 step=50/637 loss=0.90241 lr=0.00000243
+2026-06-27 19:07:28 epoch=108/120 step=100/637 loss=0.90864 lr=0.00000241
+2026-06-27 19:07:34 epoch=108/120 step=150/637 loss=0.91325 lr=0.00000239
+2026-06-27 19:07:39 epoch=108/120 step=200/637 loss=0.91619 lr=0.00000237
+2026-06-27 19:07:45 epoch=108/120 step=250/637 loss=0.91427 lr=0.00000234
+2026-06-27 19:07:51 epoch=108/120 step=300/637 loss=0.92216 lr=0.00000232
+2026-06-27 19:07:56 epoch=108/120 step=350/637 loss=0.91828 lr=0.00000230
+2026-06-27 19:08:02 epoch=108/120 step=400/637 loss=0.91696 lr=0.00000228
+2026-06-27 19:08:07 epoch=108/120 step=450/637 loss=0.91142 lr=0.00000226
+2026-06-27 19:08:13 epoch=108/120 step=500/637 loss=0.91457 lr=0.00000224
+2026-06-27 19:08:18 epoch=108/120 step=550/637 loss=0.91318 lr=0.00000222
+2026-06-27 19:08:24 epoch=108/120 step=600/637 loss=0.91123 lr=0.00000220
+2026-06-27 19:08:37 convnext val mIoU=0.58119 background=0.63538 live_coral=0.69577 dead_coral=0.47281 bleached_coral=0.52081
+2026-06-27 19:08:42 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 19:08:47 epoch=109/120 step=50/637 loss=0.91823 lr=0.00000216
+2026-06-27 19:08:53 epoch=109/120 step=100/637 loss=0.91575 lr=0.00000214
+2026-06-27 19:08:58 epoch=109/120 step=150/637 loss=0.91554 lr=0.00000212
+2026-06-27 19:09:04 epoch=109/120 step=200/637 loss=0.92969 lr=0.00000210
+2026-06-27 19:09:09 epoch=109/120 step=250/637 loss=0.91415 lr=0.00000208
+2026-06-27 19:09:15 epoch=109/120 step=300/637 loss=0.90729 lr=0.00000206
+2026-06-27 19:09:20 epoch=109/120 step=350/637 loss=0.91031 lr=0.00000204
+2026-06-27 19:09:26 epoch=109/120 step=400/637 loss=0.92023 lr=0.00000202
+2026-06-27 19:09:31 epoch=109/120 step=450/637 loss=0.92263 lr=0.00000200
+2026-06-27 19:09:36 epoch=109/120 step=500/637 loss=0.92211 lr=0.00000198
+2026-06-27 19:09:42 epoch=109/120 step=550/637 loss=0.91806 lr=0.00000196
+2026-06-27 19:09:47 epoch=109/120 step=600/637 loss=0.91742 lr=0.00000194
+2026-06-27 19:10:00 convnext val mIoU=0.58007 background=0.63449 live_coral=0.69339 dead_coral=0.47439 bleached_coral=0.51799
+2026-06-27 19:10:07 epoch=110/120 step=50/637 loss=0.90248 lr=0.00000191
+2026-06-27 19:10:12 epoch=110/120 step=100/637 loss=0.90753 lr=0.00000189
+2026-06-27 19:10:18 epoch=110/120 step=150/637 loss=0.90981 lr=0.00000187
+2026-06-27 19:10:24 epoch=110/120 step=200/637 loss=0.90801 lr=0.00000186
+2026-06-27 19:10:29 epoch=110/120 step=250/637 loss=0.90803 lr=0.00000184
+2026-06-27 19:10:35 epoch=110/120 step=300/637 loss=0.91591 lr=0.00000182
+2026-06-27 19:10:41 epoch=110/120 step=350/637 loss=0.91958 lr=0.00000180
+2026-06-27 19:10:46 epoch=110/120 step=400/637 loss=0.91747 lr=0.00000178
+2026-06-27 19:10:52 epoch=110/120 step=450/637 loss=0.91334 lr=0.00000177
+2026-06-27 19:10:58 epoch=110/120 step=500/637 loss=0.91716 lr=0.00000175
+2026-06-27 19:11:03 epoch=110/120 step=550/637 loss=0.91799 lr=0.00000173
+2026-06-27 19:11:09 epoch=110/120 step=600/637 loss=0.91448 lr=0.00000171
+2026-06-27 19:11:21 convnext val mIoU=0.57999 background=0.63539 live_coral=0.69499 dead_coral=0.47042 bleached_coral=0.51914
+2026-06-27 19:11:28 epoch=111/120 step=50/637 loss=0.92649 lr=0.00000168
+2026-06-27 19:11:34 epoch=111/120 step=100/637 loss=0.91060 lr=0.00000167
+2026-06-27 19:11:39 epoch=111/120 step=150/637 loss=0.93414 lr=0.00000165
+2026-06-27 19:11:44 epoch=111/120 step=200/637 loss=0.91436 lr=0.00000163
+2026-06-27 19:11:50 epoch=111/120 step=250/637 loss=0.90731 lr=0.00000162
+2026-06-27 19:11:56 epoch=111/120 step=300/637 loss=0.90723 lr=0.00000160
+2026-06-27 19:12:01 epoch=111/120 step=350/637 loss=0.90303 lr=0.00000158
+2026-06-27 19:12:07 epoch=111/120 step=400/637 loss=0.90525 lr=0.00000157
+2026-06-27 19:12:12 epoch=111/120 step=450/637 loss=0.90656 lr=0.00000155
+2026-06-27 19:12:17 epoch=111/120 step=500/637 loss=0.90589 lr=0.00000154
+2026-06-27 19:12:23 epoch=111/120 step=550/637 loss=0.91299 lr=0.00000152
+2026-06-27 19:12:29 epoch=111/120 step=600/637 loss=0.91696 lr=0.00000150
+2026-06-27 19:12:41 convnext val mIoU=0.58003 background=0.63552 live_coral=0.69312 dead_coral=0.47455 bleached_coral=0.51693
+2026-06-27 19:12:49 epoch=112/120 step=50/637 loss=0.85523 lr=0.00000148
+2026-06-27 19:12:54 epoch=112/120 step=100/637 loss=0.89514 lr=0.00000146
+2026-06-27 19:13:00 epoch=112/120 step=150/637 loss=0.92328 lr=0.00000145
+2026-06-27 19:13:05 epoch=112/120 step=200/637 loss=0.92441 lr=0.00000143
+2026-06-27 19:13:11 epoch=112/120 step=250/637 loss=0.92477 lr=0.00000142
+2026-06-27 19:13:16 epoch=112/120 step=300/637 loss=0.92806 lr=0.00000140
+2026-06-27 19:13:22 epoch=112/120 step=350/637 loss=0.92937 lr=0.00000139
+2026-06-27 19:13:27 epoch=112/120 step=400/637 loss=0.92489 lr=0.00000137
+2026-06-27 19:13:33 epoch=112/120 step=450/637 loss=0.91899 lr=0.00000136
+2026-06-27 19:13:38 epoch=112/120 step=500/637 loss=0.91263 lr=0.00000134
+2026-06-27 19:13:44 epoch=112/120 step=550/637 loss=0.90861 lr=0.00000133
+2026-06-27 19:13:49 epoch=112/120 step=600/637 loss=0.91475 lr=0.00000132
+2026-06-27 19:14:02 convnext val mIoU=0.58080 background=0.63569 live_coral=0.69575 dead_coral=0.47316 bleached_coral=0.51861
+2026-06-27 19:14:10 epoch=113/120 step=50/637 loss=0.87437 lr=0.00000129
+2026-06-27 19:14:15 epoch=113/120 step=100/637 loss=0.91811 lr=0.00000128
+2026-06-27 19:14:21 epoch=113/120 step=150/637 loss=0.90331 lr=0.00000127
+2026-06-27 19:14:26 epoch=113/120 step=200/637 loss=0.91485 lr=0.00000125
+2026-06-27 19:14:32 epoch=113/120 step=250/637 loss=0.89848 lr=0.00000124
+2026-06-27 19:14:37 epoch=113/120 step=300/637 loss=0.90114 lr=0.00000123
+2026-06-27 19:14:43 epoch=113/120 step=350/637 loss=0.90454 lr=0.00000121
+2026-06-27 19:14:48 epoch=113/120 step=400/637 loss=0.90203 lr=0.00000120
+2026-06-27 19:14:54 epoch=113/120 step=450/637 loss=0.90135 lr=0.00000119
+2026-06-27 19:14:59 epoch=113/120 step=500/637 loss=0.90254 lr=0.00000118
+2026-06-27 19:15:05 epoch=113/120 step=550/637 loss=0.90661 lr=0.00000116
+2026-06-27 19:15:10 epoch=113/120 step=600/637 loss=0.91190 lr=0.00000115
+2026-06-27 19:15:23 convnext val mIoU=0.58057 background=0.63545 live_coral=0.69539 dead_coral=0.47131 bleached_coral=0.52014
+2026-06-27 19:15:30 epoch=114/120 step=50/637 loss=0.95780 lr=0.00000113
+2026-06-27 19:15:36 epoch=114/120 step=100/637 loss=0.91921 lr=0.00000112
+2026-06-27 19:15:42 epoch=114/120 step=150/637 loss=0.92140 lr=0.00000111
+2026-06-27 19:15:47 epoch=114/120 step=200/637 loss=0.91393 lr=0.00000109
+2026-06-27 19:15:53 epoch=114/120 step=250/637 loss=0.92114 lr=0.00000108
+2026-06-27 19:15:58 epoch=114/120 step=300/637 loss=0.92902 lr=0.00000107
+2026-06-27 19:16:04 epoch=114/120 step=350/637 loss=0.93183 lr=0.00000106
+2026-06-27 19:16:09 epoch=114/120 step=400/637 loss=0.92819 lr=0.00000105
+2026-06-27 19:16:15 epoch=114/120 step=450/637 loss=0.92188 lr=0.00000104
+2026-06-27 19:16:20 epoch=114/120 step=500/637 loss=0.92658 lr=0.00000103
+2026-06-27 19:16:26 epoch=114/120 step=550/637 loss=0.92703 lr=0.00000102
+2026-06-27 19:16:32 epoch=114/120 step=600/637 loss=0.92598 lr=0.00000101
+2026-06-27 19:16:44 convnext val mIoU=0.58124 background=0.63613 live_coral=0.69624 dead_coral=0.47176 bleached_coral=0.52083
+2026-06-27 19:16:47 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 19:16:53 epoch=115/120 step=50/637 loss=0.90531 lr=0.00000099
+2026-06-27 19:16:58 epoch=115/120 step=100/637 loss=0.89157 lr=0.00000098
+2026-06-27 19:17:04 epoch=115/120 step=150/637 loss=0.91328 lr=0.00000097
+2026-06-27 19:17:09 epoch=115/120 step=200/637 loss=0.91855 lr=0.00000096
+2026-06-27 19:17:15 epoch=115/120 step=250/637 loss=0.92630 lr=0.00000095
+2026-06-27 19:17:20 epoch=115/120 step=300/637 loss=0.92386 lr=0.00000094
+2026-06-27 19:17:26 epoch=115/120 step=350/637 loss=0.91587 lr=0.00000093
+2026-06-27 19:17:31 epoch=115/120 step=400/637 loss=0.92496 lr=0.00000092
+2026-06-27 19:17:37 epoch=115/120 step=450/637 loss=0.92131 lr=0.00000091
+2026-06-27 19:17:42 epoch=115/120 step=500/637 loss=0.92431 lr=0.00000090
+2026-06-27 19:17:48 epoch=115/120 step=550/637 loss=0.92756 lr=0.00000089
+2026-06-27 19:17:53 epoch=115/120 step=600/637 loss=0.92466 lr=0.00000088
+2026-06-27 19:18:06 convnext val mIoU=0.58115 background=0.63715 live_coral=0.69520 dead_coral=0.47104 bleached_coral=0.52120
+2026-06-27 19:18:13 epoch=116/120 step=50/637 loss=0.88756 lr=0.00000087
+2026-06-27 19:18:18 epoch=116/120 step=100/637 loss=0.88536 lr=0.00000086
+2026-06-27 19:18:24 epoch=116/120 step=150/637 loss=0.89535 lr=0.00000085
+2026-06-27 19:18:29 epoch=116/120 step=200/637 loss=0.89865 lr=0.00000084
+2026-06-27 19:18:35 epoch=116/120 step=250/637 loss=0.89878 lr=0.00000083
+2026-06-27 19:18:41 epoch=116/120 step=300/637 loss=0.90594 lr=0.00000083
+2026-06-27 19:18:46 epoch=116/120 step=350/637 loss=0.91000 lr=0.00000082
+2026-06-27 19:18:52 epoch=116/120 step=400/637 loss=0.90777 lr=0.00000081
+2026-06-27 19:18:57 epoch=116/120 step=450/637 loss=0.90411 lr=0.00000080
+2026-06-27 19:19:02 epoch=116/120 step=500/637 loss=0.90553 lr=0.00000080
+2026-06-27 19:19:08 epoch=116/120 step=550/637 loss=0.90516 lr=0.00000079
+2026-06-27 19:19:14 epoch=116/120 step=600/637 loss=0.90787 lr=0.00000078
+2026-06-27 19:19:26 convnext val mIoU=0.58147 background=0.63710 live_coral=0.69516 dead_coral=0.47284 bleached_coral=0.52076
+2026-06-27 19:19:29 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 19:19:35 epoch=117/120 step=50/637 loss=0.88526 lr=0.00000077
+2026-06-27 19:19:40 epoch=117/120 step=100/637 loss=0.89920 lr=0.00000076
+2026-06-27 19:19:46 epoch=117/120 step=150/637 loss=0.88886 lr=0.00000076
+2026-06-27 19:19:51 epoch=117/120 step=200/637 loss=0.90205 lr=0.00000075
+2026-06-27 19:19:57 epoch=117/120 step=250/637 loss=0.90828 lr=0.00000074
+2026-06-27 19:20:02 epoch=117/120 step=300/637 loss=0.91227 lr=0.00000074
+2026-06-27 19:20:08 epoch=117/120 step=350/637 loss=0.91173 lr=0.00000073
+2026-06-27 19:20:13 epoch=117/120 step=400/637 loss=0.91446 lr=0.00000073
+2026-06-27 19:20:19 epoch=117/120 step=450/637 loss=0.91937 lr=0.00000072
+2026-06-27 19:20:24 epoch=117/120 step=500/637 loss=0.91590 lr=0.00000071
+2026-06-27 19:20:30 epoch=117/120 step=550/637 loss=0.91228 lr=0.00000071
+2026-06-27 19:20:35 epoch=117/120 step=600/637 loss=0.91155 lr=0.00000070
+2026-06-27 19:20:48 convnext val mIoU=0.58160 background=0.63659 live_coral=0.69570 dead_coral=0.47365 bleached_coral=0.52045
+2026-06-27 19:20:51 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 19:20:56 epoch=118/120 step=50/637 loss=0.88411 lr=0.00000069
+2026-06-27 19:21:02 epoch=118/120 step=100/637 loss=0.86724 lr=0.00000069
+2026-06-27 19:21:07 epoch=118/120 step=150/637 loss=0.88293 lr=0.00000068
+2026-06-27 19:21:13 epoch=118/120 step=200/637 loss=0.88814 lr=0.00000068
+2026-06-27 19:21:18 epoch=118/120 step=250/637 loss=0.87398 lr=0.00000068
+2026-06-27 19:21:24 epoch=118/120 step=300/637 loss=0.87732 lr=0.00000067
+2026-06-27 19:21:30 epoch=118/120 step=350/637 loss=0.89237 lr=0.00000067
+2026-06-27 19:21:35 epoch=118/120 step=400/637 loss=0.89431 lr=0.00000066
+2026-06-27 19:21:41 epoch=118/120 step=450/637 loss=0.89530 lr=0.00000066
+2026-06-27 19:21:46 epoch=118/120 step=500/637 loss=0.90175 lr=0.00000065
+2026-06-27 19:21:51 epoch=118/120 step=550/637 loss=0.90503 lr=0.00000065
+2026-06-27 19:21:57 epoch=118/120 step=600/637 loss=0.90279 lr=0.00000065
+2026-06-27 19:22:10 convnext val mIoU=0.58205 background=0.63573 live_coral=0.69663 dead_coral=0.47507 bleached_coral=0.52077
+2026-06-27 19:22:12 saved /root/autodl-tmp/coral/coral_seg/work_dirs/convnext_l_fpn/best.pth
+2026-06-27 19:22:18 epoch=119/120 step=50/637 loss=0.90192 lr=0.00000064
+2026-06-27 19:22:23 epoch=119/120 step=100/637 loss=0.94251 lr=0.00000064
+2026-06-27 19:22:29 epoch=119/120 step=150/637 loss=0.93112 lr=0.00000063
+2026-06-27 19:22:35 epoch=119/120 step=200/637 loss=0.92171 lr=0.00000063
+2026-06-27 19:22:40 epoch=119/120 step=250/637 loss=0.91461 lr=0.00000063
+2026-06-27 19:22:46 epoch=119/120 step=300/637 loss=0.90766 lr=0.00000063
+2026-06-27 19:22:51 epoch=119/120 step=350/637 loss=0.90207 lr=0.00000062
+2026-06-27 19:22:57 epoch=119/120 step=400/637 loss=0.90497 lr=0.00000062
+2026-06-27 19:23:02 epoch=119/120 step=450/637 loss=0.90217 lr=0.00000062
+2026-06-27 19:23:08 epoch=119/120 step=500/637 loss=0.90533 lr=0.00000062
+2026-06-27 19:23:13 epoch=119/120 step=550/637 loss=0.89849 lr=0.00000061
+2026-06-27 19:23:19 epoch=119/120 step=600/637 loss=0.89272 lr=0.00000061
+2026-06-27 19:23:31 convnext val mIoU=0.58152 background=0.63609 live_coral=0.69677 dead_coral=0.47260 bleached_coral=0.52062
+2026-06-27 19:23:38 epoch=120/120 step=50/637 loss=0.94527 lr=0.00000061
+2026-06-27 19:23:44 epoch=120/120 step=100/637 loss=0.92194 lr=0.00000061
+2026-06-27 19:23:49 epoch=120/120 step=150/637 loss=0.89593 lr=0.00000061
+2026-06-27 19:23:55 epoch=120/120 step=200/637 loss=0.90339 lr=0.00000061
+2026-06-27 19:24:00 epoch=120/120 step=250/637 loss=0.90734 lr=0.00000060
+2026-06-27 19:24:06 epoch=120/120 step=300/637 loss=0.90563 lr=0.00000060
+2026-06-27 19:24:11 epoch=120/120 step=350/637 loss=0.90149 lr=0.00000060
+2026-06-27 19:24:17 epoch=120/120 step=400/637 loss=0.90353 lr=0.00000060
+2026-06-27 19:24:22 epoch=120/120 step=450/637 loss=0.90547 lr=0.00000060
+2026-06-27 19:24:28 epoch=120/120 step=500/637 loss=0.89891 lr=0.00000060
+2026-06-27 19:24:33 epoch=120/120 step=550/637 loss=0.90143 lr=0.00000060
+2026-06-27 19:24:39 epoch=120/120 step=600/637 loss=0.90034 lr=0.00000060
+2026-06-27 19:24:51 convnext val mIoU=0.58150 background=0.63727 live_coral=0.69518 dead_coral=0.47396 bleached_coral=0.51958
diff --git a/coral_seg/logs/mask2former_train.log b/coral_seg/logs/mask2former_train.log
new file mode 100644
index 0000000000000000000000000000000000000000..7257a0795d58d8c7a48158116d39b58d94b9c71a
--- /dev/null
+++ b/coral_seg/logs/mask2former_train.log
@@ -0,0 +1,2112 @@
+/root/miniconda3/lib/python3.12/site-packages/albumentations/check_version.py:147: UserWarning: Error fetching version info The read operation timed out
+  data = fetch_version_info()
+Some weights of Mask2FormerForUniversalSegmentation were not initialized from the model checkpoint at /root/autodl-tmp/coral/coral_seg/pretrained/mask2former-swin-base-ade-semantic and are newly initialized because the shapes did not match:
+- class_predictor.bias: found shape torch.Size([151]) in the checkpoint and torch.Size([5]) in the model instantiated
+- class_predictor.weight: found shape torch.Size([151, 256]) in the checkpoint and torch.Size([5, 256]) in the model instantiated
+- criterion.empty_weight: found shape torch.Size([151]) in the checkpoint and torch.Size([5]) in the model instantiated
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+Gradient checkpointing is not supported by this Mask2Former class; continuing without it.
+2026-06-28 00:10:08 epoch=1/80 step=50/1275 loss=79.23299 lr=0.00000024
+2026-06-28 00:10:17 epoch=1/80 step=100/1275 loss=80.45763 lr=0.00000048
+2026-06-28 00:10:26 epoch=1/80 step=150/1275 loss=80.23668 lr=0.00000071
+2026-06-28 00:10:35 epoch=1/80 step=200/1275 loss=78.87463 lr=0.00000095
+2026-06-28 00:10:44 epoch=1/80 step=250/1275 loss=78.36546 lr=0.00000118
+2026-06-28 00:10:53 epoch=1/80 step=300/1275 loss=77.14048 lr=0.00000142
+2026-06-28 00:11:03 epoch=1/80 step=350/1275 loss=75.40767 lr=0.00000165
+2026-06-28 00:11:12 epoch=1/80 step=400/1275 loss=74.12854 lr=0.00000189
+2026-06-28 00:11:22 epoch=1/80 step=450/1275 loss=72.83806 lr=0.00000212
+2026-06-28 00:11:31 epoch=1/80 step=500/1275 loss=71.63940 lr=0.00000236
+2026-06-28 00:11:40 epoch=1/80 step=550/1275 loss=70.72709 lr=0.00000259
+2026-06-28 00:11:49 epoch=1/80 step=600/1275 loss=69.86032 lr=0.00000283
+2026-06-28 00:11:58 epoch=1/80 step=650/1275 loss=69.25281 lr=0.00000306
+2026-06-28 00:12:07 epoch=1/80 step=700/1275 loss=68.51925 lr=0.00000330
+2026-06-28 00:12:16 epoch=1/80 step=750/1275 loss=67.95634 lr=0.00000353
+2026-06-28 00:12:25 epoch=1/80 step=800/1275 loss=67.41840 lr=0.00000377
+2026-06-28 00:12:34 epoch=1/80 step=850/1275 loss=66.72002 lr=0.00000400
+2026-06-28 00:12:43 epoch=1/80 step=900/1275 loss=66.22502 lr=0.00000424
+2026-06-28 00:12:53 epoch=1/80 step=950/1275 loss=65.78272 lr=0.00000448
+2026-06-28 00:13:03 epoch=1/80 step=1000/1275 loss=65.44148 lr=0.00000471
+2026-06-28 00:13:13 epoch=1/80 step=1050/1275 loss=65.05752 lr=0.00000495
+2026-06-28 00:13:22 epoch=1/80 step=1100/1275 loss=64.71113 lr=0.00000518
+2026-06-28 00:13:30 epoch=1/80 step=1150/1275 loss=64.27971 lr=0.00000542
+2026-06-28 00:13:40 epoch=1/80 step=1200/1275 loss=63.86790 lr=0.00000565
+2026-06-28 00:13:50 epoch=1/80 step=1250/1275 loss=63.48827 lr=0.00000589
+2026-06-28 00:14:06 mask2former val mIoU=0.24111 background=0.51006 live_coral=0.45438 dead_coral=0.00000 bleached_coral=0.00000
+2026-06-28 00:14:08 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 00:14:17 epoch=2/80 step=50/1275 loss=56.61759 lr=0.00000624
+2026-06-28 00:14:27 epoch=2/80 step=100/1275 loss=55.39151 lr=0.00000648
+2026-06-28 00:14:36 epoch=2/80 step=150/1275 loss=55.58797 lr=0.00000671
+2026-06-28 00:14:45 epoch=2/80 step=200/1275 loss=55.56453 lr=0.00000695
+2026-06-28 00:14:54 epoch=2/80 step=250/1275 loss=55.75241 lr=0.00000718
+2026-06-28 00:15:03 epoch=2/80 step=300/1275 loss=54.86559 lr=0.00000742
+2026-06-28 00:15:12 epoch=2/80 step=350/1275 loss=54.23354 lr=0.00000765
+2026-06-28 00:15:21 epoch=2/80 step=400/1275 loss=53.96094 lr=0.00000789
+2026-06-28 00:15:30 epoch=2/80 step=450/1275 loss=53.66849 lr=0.00000812
+2026-06-28 00:15:39 epoch=2/80 step=500/1275 loss=53.71973 lr=0.00000836
+2026-06-28 00:15:48 epoch=2/80 step=550/1275 loss=53.61763 lr=0.00000859
+2026-06-28 00:15:57 epoch=2/80 step=600/1275 loss=53.60526 lr=0.00000883
+2026-06-28 00:16:07 epoch=2/80 step=650/1275 loss=53.60959 lr=0.00000906
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+2026-06-28 00:16:34 epoch=2/80 step=800/1275 loss=53.24970 lr=0.00000977
+2026-06-28 00:16:43 epoch=2/80 step=850/1275 loss=53.20850 lr=0.00001000
+2026-06-28 00:16:53 epoch=2/80 step=900/1275 loss=53.07355 lr=0.00001024
+2026-06-28 00:17:02 epoch=2/80 step=950/1275 loss=52.96768 lr=0.00001048
+2026-06-28 00:17:11 epoch=2/80 step=1000/1275 loss=52.94520 lr=0.00001071
+2026-06-28 00:17:20 epoch=2/80 step=1050/1275 loss=52.88939 lr=0.00001095
+2026-06-28 00:17:29 epoch=2/80 step=1100/1275 loss=52.85194 lr=0.00001118
+2026-06-28 00:17:38 epoch=2/80 step=1150/1275 loss=52.76499 lr=0.00001142
+2026-06-28 00:17:47 epoch=2/80 step=1200/1275 loss=52.71266 lr=0.00001165
+2026-06-28 00:17:56 epoch=2/80 step=1250/1275 loss=52.63813 lr=0.00001189
+2026-06-28 00:18:11 mask2former val mIoU=0.38760 background=0.56976 live_coral=0.54400 dead_coral=0.13660 bleached_coral=0.30004
+2026-06-28 00:18:13 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 00:18:22 epoch=3/80 step=50/1275 loss=51.30424 lr=0.00001224
+2026-06-28 00:18:31 epoch=3/80 step=100/1275 loss=50.70522 lr=0.00001248
+2026-06-28 00:18:40 epoch=3/80 step=150/1275 loss=50.54665 lr=0.00001271
+2026-06-28 00:18:49 epoch=3/80 step=200/1275 loss=50.14774 lr=0.00001295
+2026-06-28 00:18:58 epoch=3/80 step=250/1275 loss=50.40976 lr=0.00001318
+2026-06-28 00:19:07 epoch=3/80 step=300/1275 loss=50.46124 lr=0.00001342
+2026-06-28 00:19:16 epoch=3/80 step=350/1275 loss=50.23125 lr=0.00001365
+2026-06-28 00:19:26 epoch=3/80 step=400/1275 loss=49.93141 lr=0.00001389
+2026-06-28 00:19:35 epoch=3/80 step=450/1275 loss=49.72136 lr=0.00001412
+2026-06-28 00:19:44 epoch=3/80 step=500/1275 loss=49.68723 lr=0.00001436
+2026-06-28 00:19:53 epoch=3/80 step=550/1275 loss=49.87890 lr=0.00001459
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+2026-06-28 00:20:30 epoch=3/80 step=750/1275 loss=49.62111 lr=0.00001553
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+2026-06-28 00:21:20 epoch=3/80 step=1000/1275 loss=49.53358 lr=0.00001671
+2026-06-28 00:21:29 epoch=3/80 step=1050/1275 loss=49.45098 lr=0.00001695
+2026-06-28 00:21:39 epoch=3/80 step=1100/1275 loss=49.43785 lr=0.00001718
+2026-06-28 00:21:49 epoch=3/80 step=1150/1275 loss=49.40860 lr=0.00001742
+2026-06-28 00:21:59 epoch=3/80 step=1200/1275 loss=49.37218 lr=0.00001765
+2026-06-28 00:22:09 epoch=3/80 step=1250/1275 loss=49.39879 lr=0.00001789
+2026-06-28 00:22:25 mask2former val mIoU=0.42372 background=0.57730 live_coral=0.51613 dead_coral=0.25693 bleached_coral=0.34450
+2026-06-28 00:22:26 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 00:22:37 epoch=4/80 step=50/1275 loss=50.19224 lr=0.00001824
+2026-06-28 00:22:48 epoch=4/80 step=100/1275 loss=49.57249 lr=0.00001848
+2026-06-28 00:22:57 epoch=4/80 step=150/1275 loss=48.94128 lr=0.00001871
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+2026-06-28 00:23:17 epoch=4/80 step=250/1275 loss=48.97186 lr=0.00001918
+2026-06-28 00:23:27 epoch=4/80 step=300/1275 loss=48.90754 lr=0.00001942
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+2026-06-28 00:25:41 epoch=4/80 step=950/1275 loss=48.38833 lr=0.00002248
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+2026-06-28 00:26:03 epoch=4/80 step=1050/1275 loss=48.36235 lr=0.00002295
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+2026-06-28 00:27:01 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 00:27:11 epoch=5/80 step=50/1275 loss=44.13570 lr=0.00002424
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+2026-06-28 00:35:48 mask2former val mIoU=0.44569 background=0.53754 live_coral=0.53626 dead_coral=0.32108 bleached_coral=0.38788
+2026-06-28 00:35:50 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
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+2026-06-28 00:39:59 mask2former val mIoU=0.43958 background=0.56216 live_coral=0.57221 dead_coral=0.27000 bleached_coral=0.35394
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+2026-06-28 00:47:52 epoch=9/80 step=1250/1275 loss=45.20311 lr=0.00002979
+2026-06-28 00:48:07 mask2former val mIoU=0.44253 background=0.59187 live_coral=0.61640 dead_coral=0.20259 bleached_coral=0.35925
+2026-06-28 00:48:17 epoch=10/80 step=50/1275 loss=46.67998 lr=0.00002979
+2026-06-28 00:48:26 epoch=10/80 step=100/1275 loss=45.69051 lr=0.00002978
+2026-06-28 00:48:35 epoch=10/80 step=150/1275 loss=45.25876 lr=0.00002978
+2026-06-28 00:48:45 epoch=10/80 step=200/1275 loss=45.01225 lr=0.00002978
+2026-06-28 00:48:55 epoch=10/80 step=250/1275 loss=44.76821 lr=0.00002977
+2026-06-28 00:49:04 epoch=10/80 step=300/1275 loss=45.04688 lr=0.00002977
+2026-06-28 00:49:13 epoch=10/80 step=350/1275 loss=45.04481 lr=0.00002976
+2026-06-28 00:49:22 epoch=10/80 step=400/1275 loss=45.05534 lr=0.00002976
+2026-06-28 00:49:31 epoch=10/80 step=450/1275 loss=45.10826 lr=0.00002975
+2026-06-28 00:49:40 epoch=10/80 step=500/1275 loss=45.20077 lr=0.00002975
+2026-06-28 00:49:50 epoch=10/80 step=550/1275 loss=45.21705 lr=0.00002974
+2026-06-28 00:49:59 epoch=10/80 step=600/1275 loss=45.10766 lr=0.00002974
+2026-06-28 00:50:08 epoch=10/80 step=650/1275 loss=45.07288 lr=0.00002974
+2026-06-28 00:50:17 epoch=10/80 step=700/1275 loss=45.08709 lr=0.00002973
+2026-06-28 00:50:26 epoch=10/80 step=750/1275 loss=45.20565 lr=0.00002973
+2026-06-28 00:50:35 epoch=10/80 step=800/1275 loss=45.12612 lr=0.00002972
+2026-06-28 00:50:44 epoch=10/80 step=850/1275 loss=45.15490 lr=0.00002972
+2026-06-28 00:50:52 epoch=10/80 step=900/1275 loss=45.09635 lr=0.00002971
+2026-06-28 00:51:01 epoch=10/80 step=950/1275 loss=45.04414 lr=0.00002971
+2026-06-28 00:51:10 epoch=10/80 step=1000/1275 loss=44.92718 lr=0.00002970
+2026-06-28 00:51:19 epoch=10/80 step=1050/1275 loss=44.92336 lr=0.00002970
+2026-06-28 00:51:29 epoch=10/80 step=1100/1275 loss=44.83008 lr=0.00002969
+2026-06-28 00:51:38 epoch=10/80 step=1150/1275 loss=44.76065 lr=0.00002969
+2026-06-28 00:51:47 epoch=10/80 step=1200/1275 loss=44.84740 lr=0.00002968
+2026-06-28 00:51:57 epoch=10/80 step=1250/1275 loss=44.81518 lr=0.00002968
+2026-06-28 00:52:12 mask2former val mIoU=0.45992 background=0.58236 live_coral=0.57427 dead_coral=0.34236 bleached_coral=0.34070
+2026-06-28 00:52:14 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 00:52:23 epoch=11/80 step=50/1275 loss=43.12285 lr=0.00002967
+2026-06-28 00:52:32 epoch=11/80 step=100/1275 loss=44.22265 lr=0.00002967
+2026-06-28 00:52:41 epoch=11/80 step=150/1275 loss=44.04896 lr=0.00002966
+2026-06-28 00:52:49 epoch=11/80 step=200/1275 loss=44.07407 lr=0.00002965
+2026-06-28 00:52:58 epoch=11/80 step=250/1275 loss=44.66355 lr=0.00002965
+2026-06-28 00:53:07 epoch=11/80 step=300/1275 loss=44.72115 lr=0.00002964
+2026-06-28 00:53:16 epoch=11/80 step=350/1275 loss=44.66563 lr=0.00002964
+2026-06-28 00:53:25 epoch=11/80 step=400/1275 loss=44.48182 lr=0.00002963
+2026-06-28 00:53:34 epoch=11/80 step=450/1275 loss=44.66374 lr=0.00002963
+2026-06-28 00:53:43 epoch=11/80 step=500/1275 loss=44.60607 lr=0.00002962
+2026-06-28 00:53:52 epoch=11/80 step=550/1275 loss=44.68757 lr=0.00002962
+2026-06-28 00:54:01 epoch=11/80 step=600/1275 loss=44.70226 lr=0.00002961
+2026-06-28 00:54:11 epoch=11/80 step=650/1275 loss=44.72141 lr=0.00002961
+2026-06-28 00:54:20 epoch=11/80 step=700/1275 loss=44.68083 lr=0.00002960
+2026-06-28 00:54:30 epoch=11/80 step=750/1275 loss=44.50728 lr=0.00002960
+2026-06-28 00:54:39 epoch=11/80 step=800/1275 loss=44.49063 lr=0.00002959
+2026-06-28 00:54:48 epoch=11/80 step=850/1275 loss=44.43797 lr=0.00002958
+2026-06-28 00:54:58 epoch=11/80 step=900/1275 loss=44.44509 lr=0.00002958
+2026-06-28 00:55:07 epoch=11/80 step=950/1275 loss=44.45394 lr=0.00002957
+2026-06-28 00:55:16 epoch=11/80 step=1000/1275 loss=44.46704 lr=0.00002957
+2026-06-28 00:55:25 epoch=11/80 step=1050/1275 loss=44.46896 lr=0.00002956
+2026-06-28 00:55:34 epoch=11/80 step=1100/1275 loss=44.30909 lr=0.00002955
+2026-06-28 00:55:42 epoch=11/80 step=1150/1275 loss=44.31843 lr=0.00002955
+2026-06-28 00:55:51 epoch=11/80 step=1200/1275 loss=44.35084 lr=0.00002954
+2026-06-28 00:56:00 epoch=11/80 step=1250/1275 loss=44.53289 lr=0.00002954
+2026-06-28 00:56:15 mask2former val mIoU=0.46980 background=0.57876 live_coral=0.60575 dead_coral=0.24719 bleached_coral=0.44749
+2026-06-28 00:56:17 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 00:56:27 epoch=12/80 step=50/1275 loss=45.59056 lr=0.00002953
+2026-06-28 00:56:37 epoch=12/80 step=100/1275 loss=43.04634 lr=0.00002952
+2026-06-28 00:56:47 epoch=12/80 step=150/1275 loss=42.94974 lr=0.00002952
+2026-06-28 00:56:57 epoch=12/80 step=200/1275 loss=44.01489 lr=0.00002951
+2026-06-28 00:57:06 epoch=12/80 step=250/1275 loss=43.96218 lr=0.00002950
+2026-06-28 00:57:15 epoch=12/80 step=300/1275 loss=44.31683 lr=0.00002950
+2026-06-28 00:57:24 epoch=12/80 step=350/1275 loss=44.18426 lr=0.00002949
+2026-06-28 00:57:34 epoch=12/80 step=400/1275 loss=44.08711 lr=0.00002948
+2026-06-28 00:57:43 epoch=12/80 step=450/1275 loss=44.09476 lr=0.00002948
+2026-06-28 00:57:52 epoch=12/80 step=500/1275 loss=43.90494 lr=0.00002947
+2026-06-28 00:58:00 epoch=12/80 step=550/1275 loss=43.93347 lr=0.00002946
+2026-06-28 00:58:09 epoch=12/80 step=600/1275 loss=43.94091 lr=0.00002946
+2026-06-28 00:58:18 epoch=12/80 step=650/1275 loss=44.05360 lr=0.00002945
+2026-06-28 00:58:27 epoch=12/80 step=700/1275 loss=44.00367 lr=0.00002944
+2026-06-28 00:58:36 epoch=12/80 step=750/1275 loss=44.07711 lr=0.00002944
+2026-06-28 00:58:45 epoch=12/80 step=800/1275 loss=44.14702 lr=0.00002943
+2026-06-28 00:58:54 epoch=12/80 step=850/1275 loss=44.05772 lr=0.00002942
+2026-06-28 00:59:03 epoch=12/80 step=900/1275 loss=44.13459 lr=0.00002942
+2026-06-28 00:59:12 epoch=12/80 step=950/1275 loss=44.08782 lr=0.00002941
+2026-06-28 00:59:21 epoch=12/80 step=1000/1275 loss=44.11478 lr=0.00002940
+2026-06-28 00:59:30 epoch=12/80 step=1050/1275 loss=44.09490 lr=0.00002940
+2026-06-28 00:59:39 epoch=12/80 step=1100/1275 loss=44.04303 lr=0.00002939
+2026-06-28 00:59:48 epoch=12/80 step=1150/1275 loss=44.00902 lr=0.00002938
+2026-06-28 00:59:58 epoch=12/80 step=1200/1275 loss=44.00494 lr=0.00002938
+2026-06-28 01:00:08 epoch=12/80 step=1250/1275 loss=44.11559 lr=0.00002937
+2026-06-28 01:00:24 mask2former val mIoU=0.48547 background=0.57766 live_coral=0.60178 dead_coral=0.30675 bleached_coral=0.45568
+2026-06-28 01:00:25 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 01:00:36 epoch=13/80 step=50/1275 loss=40.48944 lr=0.00002936
+2026-06-28 01:00:45 epoch=13/80 step=100/1275 loss=42.57813 lr=0.00002935
+2026-06-28 01:00:56 epoch=13/80 step=150/1275 loss=43.74563 lr=0.00002934
+2026-06-28 01:01:05 epoch=13/80 step=200/1275 loss=43.68061 lr=0.00002934
+2026-06-28 01:01:15 epoch=13/80 step=250/1275 loss=43.51021 lr=0.00002933
+2026-06-28 01:01:24 epoch=13/80 step=300/1275 loss=43.57511 lr=0.00002932
+2026-06-28 01:01:33 epoch=13/80 step=350/1275 loss=43.15061 lr=0.00002932
+2026-06-28 01:01:43 epoch=13/80 step=400/1275 loss=43.40369 lr=0.00002931
+2026-06-28 01:01:53 epoch=13/80 step=450/1275 loss=43.76851 lr=0.00002930
+2026-06-28 01:02:03 epoch=13/80 step=500/1275 loss=43.79065 lr=0.00002929
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+2026-06-28 01:02:33 epoch=13/80 step=650/1275 loss=43.68879 lr=0.00002927
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+2026-06-28 01:02:51 epoch=13/80 step=750/1275 loss=43.57895 lr=0.00002926
+2026-06-28 01:03:00 epoch=13/80 step=800/1275 loss=43.58526 lr=0.00002925
+2026-06-28 01:03:09 epoch=13/80 step=850/1275 loss=43.67249 lr=0.00002924
+2026-06-28 01:03:18 epoch=13/80 step=900/1275 loss=43.64525 lr=0.00002923
+2026-06-28 01:03:27 epoch=13/80 step=950/1275 loss=43.55586 lr=0.00002923
+2026-06-28 01:03:36 epoch=13/80 step=1000/1275 loss=43.64304 lr=0.00002922
+2026-06-28 01:03:45 epoch=13/80 step=1050/1275 loss=43.70458 lr=0.00002921
+2026-06-28 01:03:54 epoch=13/80 step=1100/1275 loss=43.64771 lr=0.00002920
+2026-06-28 01:04:03 epoch=13/80 step=1150/1275 loss=43.51322 lr=0.00002919
+2026-06-28 01:04:12 epoch=13/80 step=1200/1275 loss=43.62357 lr=0.00002919
+2026-06-28 01:04:21 epoch=13/80 step=1250/1275 loss=43.63576 lr=0.00002918
+2026-06-28 01:04:35 mask2former val mIoU=0.48295 background=0.58397 live_coral=0.59048 dead_coral=0.32056 bleached_coral=0.43679
+2026-06-28 01:04:46 epoch=14/80 step=50/1275 loss=45.06324 lr=0.00002917
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+2026-06-28 01:05:42 epoch=14/80 step=350/1275 loss=43.54994 lr=0.00002912
+2026-06-28 01:05:51 epoch=14/80 step=400/1275 loss=43.24586 lr=0.00002911
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+2026-06-28 01:07:13 epoch=14/80 step=850/1275 loss=43.32253 lr=0.00002903
+2026-06-28 01:07:23 epoch=14/80 step=900/1275 loss=43.30864 lr=0.00002902
+2026-06-28 01:07:33 epoch=14/80 step=950/1275 loss=43.40337 lr=0.00002901
+2026-06-28 01:07:42 epoch=14/80 step=1000/1275 loss=43.42095 lr=0.00002901
+2026-06-28 01:07:51 epoch=14/80 step=1050/1275 loss=43.43340 lr=0.00002900
+2026-06-28 01:08:00 epoch=14/80 step=1100/1275 loss=43.40943 lr=0.00002899
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+2026-06-28 01:08:27 epoch=14/80 step=1250/1275 loss=43.25581 lr=0.00002896
+2026-06-28 01:08:42 mask2former val mIoU=0.48157 background=0.56994 live_coral=0.59837 dead_coral=0.30822 bleached_coral=0.44977
+2026-06-28 01:08:53 epoch=15/80 step=50/1275 loss=42.47154 lr=0.00002895
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+2026-06-28 01:12:04 epoch=15/80 step=1100/1275 loss=42.97919 lr=0.00002875
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+2026-06-28 01:12:45 mask2former val mIoU=0.48890 background=0.59620 live_coral=0.62567 dead_coral=0.31653 bleached_coral=0.41722
+2026-06-28 01:12:47 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
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+2026-06-28 01:16:50 mask2former val mIoU=0.49604 background=0.61193 live_coral=0.62846 dead_coral=0.31189 bleached_coral=0.43186
+2026-06-28 01:16:52 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 01:17:01 epoch=17/80 step=50/1275 loss=41.80684 lr=0.00002844
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+2026-06-28 01:18:06 epoch=17/80 step=400/1275 loss=42.07371 lr=0.00002836
+2026-06-28 01:18:15 epoch=17/80 step=450/1275 loss=42.18055 lr=0.00002835
+2026-06-28 01:18:24 epoch=17/80 step=500/1275 loss=42.14315 lr=0.00002834
+2026-06-28 01:18:33 epoch=17/80 step=550/1275 loss=41.97695 lr=0.00002833
+2026-06-28 01:18:42 epoch=17/80 step=600/1275 loss=42.06486 lr=0.00002832
+2026-06-28 01:18:51 epoch=17/80 step=650/1275 loss=41.90378 lr=0.00002831
+2026-06-28 01:19:01 epoch=17/80 step=700/1275 loss=41.84342 lr=0.00002830
+2026-06-28 01:19:10 epoch=17/80 step=750/1275 loss=42.00496 lr=0.00002828
+2026-06-28 01:19:19 epoch=17/80 step=800/1275 loss=41.92487 lr=0.00002827
+2026-06-28 01:19:28 epoch=17/80 step=850/1275 loss=42.03581 lr=0.00002826
+2026-06-28 01:19:37 epoch=17/80 step=900/1275 loss=42.02903 lr=0.00002825
+2026-06-28 01:19:46 epoch=17/80 step=950/1275 loss=42.14927 lr=0.00002824
+2026-06-28 01:19:56 epoch=17/80 step=1000/1275 loss=42.17360 lr=0.00002823
+2026-06-28 01:20:05 epoch=17/80 step=1050/1275 loss=42.19677 lr=0.00002822
+2026-06-28 01:20:15 epoch=17/80 step=1100/1275 loss=42.17318 lr=0.00002820
+2026-06-28 01:20:24 epoch=17/80 step=1150/1275 loss=42.28532 lr=0.00002819
+2026-06-28 01:20:33 epoch=17/80 step=1200/1275 loss=42.23549 lr=0.00002818
+2026-06-28 01:20:42 epoch=17/80 step=1250/1275 loss=42.31443 lr=0.00002817
+2026-06-28 01:20:57 mask2former val mIoU=0.49250 background=0.59406 live_coral=0.60923 dead_coral=0.36706 bleached_coral=0.39964
+2026-06-28 01:21:07 epoch=18/80 step=50/1275 loss=41.74127 lr=0.00002815
+2026-06-28 01:21:16 epoch=18/80 step=100/1275 loss=41.82139 lr=0.00002814
+2026-06-28 01:21:25 epoch=18/80 step=150/1275 loss=41.81569 lr=0.00002813
+2026-06-28 01:21:35 epoch=18/80 step=200/1275 loss=42.51024 lr=0.00002812
+2026-06-28 01:21:44 epoch=18/80 step=250/1275 loss=42.86066 lr=0.00002810
+2026-06-28 01:21:53 epoch=18/80 step=300/1275 loss=42.47920 lr=0.00002809
+2026-06-28 01:22:02 epoch=18/80 step=350/1275 loss=42.28683 lr=0.00002808
+2026-06-28 01:22:11 epoch=18/80 step=400/1275 loss=42.14088 lr=0.00002807
+2026-06-28 01:22:21 epoch=18/80 step=450/1275 loss=41.80365 lr=0.00002806
+2026-06-28 01:22:30 epoch=18/80 step=500/1275 loss=42.05078 lr=0.00002804
+2026-06-28 01:22:39 epoch=18/80 step=550/1275 loss=42.09539 lr=0.00002803
+2026-06-28 01:22:48 epoch=18/80 step=600/1275 loss=41.87874 lr=0.00002802
+2026-06-28 01:22:58 epoch=18/80 step=650/1275 loss=41.85205 lr=0.00002801
+2026-06-28 01:23:07 epoch=18/80 step=700/1275 loss=41.99917 lr=0.00002800
+2026-06-28 01:23:17 epoch=18/80 step=750/1275 loss=42.09466 lr=0.00002798
+2026-06-28 01:23:27 epoch=18/80 step=800/1275 loss=42.09561 lr=0.00002797
+2026-06-28 01:23:36 epoch=18/80 step=850/1275 loss=42.01777 lr=0.00002796
+2026-06-28 01:23:45 epoch=18/80 step=900/1275 loss=42.14031 lr=0.00002795
+2026-06-28 01:23:54 epoch=18/80 step=950/1275 loss=42.01626 lr=0.00002793
+2026-06-28 01:24:03 epoch=18/80 step=1000/1275 loss=42.03395 lr=0.00002792
+2026-06-28 01:24:13 epoch=18/80 step=1050/1275 loss=42.13517 lr=0.00002791
+2026-06-28 01:24:22 epoch=18/80 step=1100/1275 loss=42.10502 lr=0.00002790
+2026-06-28 01:24:31 epoch=18/80 step=1150/1275 loss=42.15614 lr=0.00002788
+2026-06-28 01:24:40 epoch=18/80 step=1200/1275 loss=42.02006 lr=0.00002787
+2026-06-28 01:24:50 epoch=18/80 step=1250/1275 loss=41.90999 lr=0.00002786
+2026-06-28 01:25:05 mask2former val mIoU=0.48190 background=0.52652 live_coral=0.61754 dead_coral=0.36427 bleached_coral=0.41928
+2026-06-28 01:25:15 epoch=19/80 step=50/1275 loss=44.87555 lr=0.00002784
+2026-06-28 01:25:25 epoch=19/80 step=100/1275 loss=43.77080 lr=0.00002783
+2026-06-28 01:25:35 epoch=19/80 step=150/1275 loss=41.50079 lr=0.00002781
+2026-06-28 01:25:45 epoch=19/80 step=200/1275 loss=40.65963 lr=0.00002780
+2026-06-28 01:25:55 epoch=19/80 step=250/1275 loss=41.17156 lr=0.00002779
+2026-06-28 01:26:04 epoch=19/80 step=300/1275 loss=41.24033 lr=0.00002778
+2026-06-28 01:26:14 epoch=19/80 step=350/1275 loss=41.32761 lr=0.00002776
+2026-06-28 01:26:23 epoch=19/80 step=400/1275 loss=41.14675 lr=0.00002775
+2026-06-28 01:26:32 epoch=19/80 step=450/1275 loss=41.13100 lr=0.00002774
+2026-06-28 01:26:41 epoch=19/80 step=500/1275 loss=40.94716 lr=0.00002772
+2026-06-28 01:26:51 epoch=19/80 step=550/1275 loss=41.21476 lr=0.00002771
+2026-06-28 01:27:00 epoch=19/80 step=600/1275 loss=41.41386 lr=0.00002770
+2026-06-28 01:27:09 epoch=19/80 step=650/1275 loss=41.63766 lr=0.00002769
+2026-06-28 01:27:18 epoch=19/80 step=700/1275 loss=41.56884 lr=0.00002767
+2026-06-28 01:27:27 epoch=19/80 step=750/1275 loss=41.39912 lr=0.00002766
+2026-06-28 01:27:35 epoch=19/80 step=800/1275 loss=41.41882 lr=0.00002765
+2026-06-28 01:27:45 epoch=19/80 step=850/1275 loss=41.46821 lr=0.00002763
+2026-06-28 01:27:55 epoch=19/80 step=900/1275 loss=41.46691 lr=0.00002762
+2026-06-28 01:28:04 epoch=19/80 step=950/1275 loss=41.56476 lr=0.00002761
+2026-06-28 01:28:13 epoch=19/80 step=1000/1275 loss=41.58271 lr=0.00002759
+2026-06-28 01:28:22 epoch=19/80 step=1050/1275 loss=41.63216 lr=0.00002758
+2026-06-28 01:28:31 epoch=19/80 step=1100/1275 loss=41.74018 lr=0.00002757
+2026-06-28 01:28:40 epoch=19/80 step=1150/1275 loss=41.71170 lr=0.00002755
+2026-06-28 01:28:49 epoch=19/80 step=1200/1275 loss=41.71801 lr=0.00002754
+2026-06-28 01:28:58 epoch=19/80 step=1250/1275 loss=41.77738 lr=0.00002753
+2026-06-28 01:29:14 mask2former val mIoU=0.48291 background=0.56480 live_coral=0.60647 dead_coral=0.35230 bleached_coral=0.40807
+2026-06-28 01:29:24 epoch=20/80 step=50/1275 loss=41.67140 lr=0.00002751
+2026-06-28 01:29:33 epoch=20/80 step=100/1275 loss=41.57165 lr=0.00002749
+2026-06-28 01:29:42 epoch=20/80 step=150/1275 loss=41.90005 lr=0.00002748
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+2026-06-28 01:30:00 epoch=20/80 step=250/1275 loss=41.64515 lr=0.00002745
+2026-06-28 01:30:09 epoch=20/80 step=300/1275 loss=41.48090 lr=0.00002744
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+2026-06-28 01:30:27 epoch=20/80 step=400/1275 loss=41.21240 lr=0.00002741
+2026-06-28 01:30:36 epoch=20/80 step=450/1275 loss=41.47590 lr=0.00002740
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+2026-06-28 01:32:03 epoch=20/80 step=900/1275 loss=41.22793 lr=0.00002727
+2026-06-28 01:32:12 epoch=20/80 step=950/1275 loss=41.32143 lr=0.00002726
+2026-06-28 01:32:22 epoch=20/80 step=1000/1275 loss=41.49111 lr=0.00002724
+2026-06-28 01:32:31 epoch=20/80 step=1050/1275 loss=41.36187 lr=0.00002723
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+2026-06-28 01:33:08 epoch=20/80 step=1250/1275 loss=41.38564 lr=0.00002717
+2026-06-28 01:33:22 mask2former val mIoU=0.48349 background=0.54456 live_coral=0.63744 dead_coral=0.31617 bleached_coral=0.43578
+2026-06-28 01:33:32 epoch=21/80 step=50/1275 loss=39.34835 lr=0.00002715
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+2026-06-28 01:37:28 mask2former val mIoU=0.51006 background=0.59958 live_coral=0.63533 dead_coral=0.33663 bleached_coral=0.46873
+2026-06-28 01:37:30 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 01:37:39 epoch=22/80 step=50/1275 loss=41.85327 lr=0.00002677
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+2026-06-28 01:41:36 mask2former val mIoU=0.50358 background=0.58637 live_coral=0.62215 dead_coral=0.34039 bleached_coral=0.46541
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+2026-06-28 01:45:34 epoch=23/80 step=1250/1275 loss=39.97797 lr=0.00002598
+2026-06-28 01:45:49 mask2former val mIoU=0.51066 background=0.60997 live_coral=0.62706 dead_coral=0.34970 bleached_coral=0.45592
+2026-06-28 01:45:51 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
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+2026-06-28 01:49:32 epoch=24/80 step=1200/1275 loss=39.66080 lr=0.00002557
+2026-06-28 01:49:41 epoch=24/80 step=1250/1275 loss=39.63664 lr=0.00002555
+2026-06-28 01:49:55 mask2former val mIoU=0.50235 background=0.59831 live_coral=0.64603 dead_coral=0.32485 bleached_coral=0.44020
+2026-06-28 01:50:06 epoch=25/80 step=50/1275 loss=34.78468 lr=0.00002552
+2026-06-28 01:50:16 epoch=25/80 step=100/1275 loss=37.31194 lr=0.00002551
+2026-06-28 01:50:25 epoch=25/80 step=150/1275 loss=37.52622 lr=0.00002549
+2026-06-28 01:50:35 epoch=25/80 step=200/1275 loss=38.38961 lr=0.00002547
+2026-06-28 01:50:45 epoch=25/80 step=250/1275 loss=38.34233 lr=0.00002545
+2026-06-28 01:50:54 epoch=25/80 step=300/1275 loss=38.59195 lr=0.00002543
+2026-06-28 01:51:03 epoch=25/80 step=350/1275 loss=38.75767 lr=0.00002542
+2026-06-28 01:51:13 epoch=25/80 step=400/1275 loss=38.71032 lr=0.00002540
+2026-06-28 01:51:23 epoch=25/80 step=450/1275 loss=38.82064 lr=0.00002538
+2026-06-28 01:51:33 epoch=25/80 step=500/1275 loss=38.78673 lr=0.00002536
+2026-06-28 01:51:44 epoch=25/80 step=550/1275 loss=38.68274 lr=0.00002535
+2026-06-28 01:51:54 epoch=25/80 step=600/1275 loss=38.83515 lr=0.00002533
+2026-06-28 01:52:03 epoch=25/80 step=650/1275 loss=38.72441 lr=0.00002531
+2026-06-28 01:52:12 epoch=25/80 step=700/1275 loss=38.76329 lr=0.00002529
+2026-06-28 01:52:22 epoch=25/80 step=750/1275 loss=38.69218 lr=0.00002528
+2026-06-28 01:52:32 epoch=25/80 step=800/1275 loss=38.78119 lr=0.00002526
+2026-06-28 01:52:41 epoch=25/80 step=850/1275 loss=38.95364 lr=0.00002524
+2026-06-28 01:52:51 epoch=25/80 step=900/1275 loss=39.08030 lr=0.00002522
+2026-06-28 01:53:00 epoch=25/80 step=950/1275 loss=39.15593 lr=0.00002520
+2026-06-28 01:53:09 epoch=25/80 step=1000/1275 loss=39.01860 lr=0.00002519
+2026-06-28 01:53:18 epoch=25/80 step=1050/1275 loss=39.06178 lr=0.00002517
+2026-06-28 01:53:28 epoch=25/80 step=1100/1275 loss=39.12606 lr=0.00002515
+2026-06-28 01:53:37 epoch=25/80 step=1150/1275 loss=39.21456 lr=0.00002513
+2026-06-28 01:53:46 epoch=25/80 step=1200/1275 loss=39.25385 lr=0.00002511
+2026-06-28 01:53:55 epoch=25/80 step=1250/1275 loss=39.36460 lr=0.00002510
+2026-06-28 01:54:10 mask2former val mIoU=0.51426 background=0.61438 live_coral=0.64156 dead_coral=0.32837 bleached_coral=0.47272
+2026-06-28 01:54:12 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 01:54:22 epoch=26/80 step=50/1275 loss=40.83070 lr=0.00002507
+2026-06-28 01:54:31 epoch=26/80 step=100/1275 loss=39.17956 lr=0.00002505
+2026-06-28 01:54:40 epoch=26/80 step=150/1275 loss=39.31581 lr=0.00002503
+2026-06-28 01:54:49 epoch=26/80 step=200/1275 loss=39.68495 lr=0.00002501
+2026-06-28 01:54:59 epoch=26/80 step=250/1275 loss=38.92879 lr=0.00002500
+2026-06-28 01:55:08 epoch=26/80 step=300/1275 loss=39.00574 lr=0.00002498
+2026-06-28 01:55:18 epoch=26/80 step=350/1275 loss=39.26999 lr=0.00002496
+2026-06-28 01:55:27 epoch=26/80 step=400/1275 loss=38.99935 lr=0.00002494
+2026-06-28 01:55:36 epoch=26/80 step=450/1275 loss=38.92527 lr=0.00002492
+2026-06-28 01:55:45 epoch=26/80 step=500/1275 loss=38.80803 lr=0.00002490
+2026-06-28 01:55:54 epoch=26/80 step=550/1275 loss=38.89923 lr=0.00002489
+2026-06-28 01:56:03 epoch=26/80 step=600/1275 loss=38.88732 lr=0.00002487
+2026-06-28 01:56:12 epoch=26/80 step=650/1275 loss=38.83418 lr=0.00002485
+2026-06-28 01:56:22 epoch=26/80 step=700/1275 loss=38.70608 lr=0.00002483
+2026-06-28 01:56:31 epoch=26/80 step=750/1275 loss=38.66583 lr=0.00002481
+2026-06-28 01:56:40 epoch=26/80 step=800/1275 loss=38.61074 lr=0.00002479
+2026-06-28 01:56:50 epoch=26/80 step=850/1275 loss=38.68259 lr=0.00002477
+2026-06-28 01:56:59 epoch=26/80 step=900/1275 loss=38.73746 lr=0.00002476
+2026-06-28 01:57:09 epoch=26/80 step=950/1275 loss=38.67783 lr=0.00002474
+2026-06-28 01:57:18 epoch=26/80 step=1000/1275 loss=38.63504 lr=0.00002472
+2026-06-28 01:57:28 epoch=26/80 step=1050/1275 loss=38.65178 lr=0.00002470
+2026-06-28 01:57:37 epoch=26/80 step=1100/1275 loss=38.76480 lr=0.00002468
+2026-06-28 01:57:48 epoch=26/80 step=1150/1275 loss=38.73717 lr=0.00002466
+2026-06-28 01:57:57 epoch=26/80 step=1200/1275 loss=38.73023 lr=0.00002464
+2026-06-28 01:58:07 epoch=26/80 step=1250/1275 loss=38.78063 lr=0.00002463
+2026-06-28 01:58:23 mask2former val mIoU=0.51171 background=0.57774 live_coral=0.64780 dead_coral=0.36463 bleached_coral=0.45666
+2026-06-28 01:58:33 epoch=27/80 step=50/1275 loss=37.88324 lr=0.00002460
+2026-06-28 01:58:42 epoch=27/80 step=100/1275 loss=36.64675 lr=0.00002458
+2026-06-28 01:58:51 epoch=27/80 step=150/1275 loss=37.23680 lr=0.00002456
+2026-06-28 01:59:00 epoch=27/80 step=200/1275 loss=37.90232 lr=0.00002454
+2026-06-28 01:59:09 epoch=27/80 step=250/1275 loss=37.74998 lr=0.00002452
+2026-06-28 01:59:18 epoch=27/80 step=300/1275 loss=38.17654 lr=0.00002450
+2026-06-28 01:59:28 epoch=27/80 step=350/1275 loss=37.79424 lr=0.00002448
+2026-06-28 01:59:37 epoch=27/80 step=400/1275 loss=38.22825 lr=0.00002446
+2026-06-28 01:59:47 epoch=27/80 step=450/1275 loss=37.99108 lr=0.00002445
+2026-06-28 01:59:56 epoch=27/80 step=500/1275 loss=38.18361 lr=0.00002443
+2026-06-28 02:00:05 epoch=27/80 step=550/1275 loss=38.17083 lr=0.00002441
+2026-06-28 02:00:15 epoch=27/80 step=600/1275 loss=38.27800 lr=0.00002439
+2026-06-28 02:00:25 epoch=27/80 step=650/1275 loss=38.17551 lr=0.00002437
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+2026-06-28 02:00:44 epoch=27/80 step=750/1275 loss=38.33765 lr=0.00002433
+2026-06-28 02:00:53 epoch=27/80 step=800/1275 loss=38.25203 lr=0.00002431
+2026-06-28 02:01:03 epoch=27/80 step=850/1275 loss=38.23623 lr=0.00002429
+2026-06-28 02:01:12 epoch=27/80 step=900/1275 loss=38.26524 lr=0.00002427
+2026-06-28 02:01:21 epoch=27/80 step=950/1275 loss=38.13755 lr=0.00002425
+2026-06-28 02:01:30 epoch=27/80 step=1000/1275 loss=38.12430 lr=0.00002423
+2026-06-28 02:01:39 epoch=27/80 step=1050/1275 loss=38.01257 lr=0.00002422
+2026-06-28 02:01:48 epoch=27/80 step=1100/1275 loss=38.02777 lr=0.00002420
+2026-06-28 02:01:57 epoch=27/80 step=1150/1275 loss=38.13262 lr=0.00002418
+2026-06-28 02:02:06 epoch=27/80 step=1200/1275 loss=38.09909 lr=0.00002416
+2026-06-28 02:02:16 epoch=27/80 step=1250/1275 loss=38.08928 lr=0.00002414
+2026-06-28 02:02:31 mask2former val mIoU=0.50973 background=0.60110 live_coral=0.62656 dead_coral=0.36125 bleached_coral=0.44999
+2026-06-28 02:02:41 epoch=28/80 step=50/1275 loss=35.99199 lr=0.00002411
+2026-06-28 02:02:50 epoch=28/80 step=100/1275 loss=36.00867 lr=0.00002409
+2026-06-28 02:03:00 epoch=28/80 step=150/1275 loss=36.32557 lr=0.00002407
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+2026-06-28 02:03:20 epoch=28/80 step=250/1275 loss=36.65182 lr=0.00002403
+2026-06-28 02:03:30 epoch=28/80 step=300/1275 loss=36.93433 lr=0.00002401
+2026-06-28 02:03:40 epoch=28/80 step=350/1275 loss=36.99707 lr=0.00002399
+2026-06-28 02:03:49 epoch=28/80 step=400/1275 loss=37.28363 lr=0.00002397
+2026-06-28 02:03:58 epoch=28/80 step=450/1275 loss=37.31963 lr=0.00002395
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+2026-06-28 02:04:34 epoch=28/80 step=650/1275 loss=37.27306 lr=0.00002387
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+2026-06-28 02:04:53 epoch=28/80 step=750/1275 loss=37.43821 lr=0.00002383
+2026-06-28 02:05:02 epoch=28/80 step=800/1275 loss=37.41010 lr=0.00002381
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+2026-06-28 02:05:21 epoch=28/80 step=900/1275 loss=37.47708 lr=0.00002377
+2026-06-28 02:05:30 epoch=28/80 step=950/1275 loss=37.60763 lr=0.00002375
+2026-06-28 02:05:40 epoch=28/80 step=1000/1275 loss=37.61330 lr=0.00002373
+2026-06-28 02:05:49 epoch=28/80 step=1050/1275 loss=37.69158 lr=0.00002372
+2026-06-28 02:05:58 epoch=28/80 step=1100/1275 loss=37.70992 lr=0.00002370
+2026-06-28 02:06:07 epoch=28/80 step=1150/1275 loss=37.73817 lr=0.00002368
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+2026-06-28 02:06:40 mask2former val mIoU=0.50536 background=0.58479 live_coral=0.64132 dead_coral=0.37593 bleached_coral=0.41942
+2026-06-28 02:06:50 epoch=29/80 step=50/1275 loss=34.10921 lr=0.00002361
+2026-06-28 02:06:59 epoch=29/80 step=100/1275 loss=34.45957 lr=0.00002358
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+2026-06-28 02:07:54 epoch=29/80 step=400/1275 loss=36.27707 lr=0.00002346
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+2026-06-28 02:10:03 epoch=29/80 step=1100/1275 loss=37.19326 lr=0.00002318
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+2026-06-28 02:10:46 mask2former val mIoU=0.52965 background=0.59683 live_coral=0.66110 dead_coral=0.37017 bleached_coral=0.49050
+2026-06-28 02:10:48 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 02:10:57 epoch=30/80 step=50/1275 loss=38.33957 lr=0.00002309
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+2026-06-28 02:14:53 mask2former val mIoU=0.51799 background=0.59913 live_coral=0.66327 dead_coral=0.34808 bleached_coral=0.46150
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+2026-06-28 02:18:39 epoch=31/80 step=1250/1275 loss=36.85789 lr=0.00002204
+2026-06-28 02:18:55 mask2former val mIoU=0.53151 background=0.59549 live_coral=0.66427 dead_coral=0.39036 bleached_coral=0.47592
+2026-06-28 02:18:57 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 02:19:06 epoch=32/80 step=50/1275 loss=37.00034 lr=0.00002201
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+2026-06-28 02:20:48 epoch=32/80 step=600/1275 loss=35.75860 lr=0.00002177
+2026-06-28 02:20:57 epoch=32/80 step=650/1275 loss=35.65740 lr=0.00002175
+2026-06-28 02:21:06 epoch=32/80 step=700/1275 loss=35.55977 lr=0.00002173
+2026-06-28 02:21:16 epoch=32/80 step=750/1275 loss=35.72764 lr=0.00002170
+2026-06-28 02:21:25 epoch=32/80 step=800/1275 loss=35.86584 lr=0.00002168
+2026-06-28 02:21:35 epoch=32/80 step=850/1275 loss=35.87724 lr=0.00002166
+2026-06-28 02:21:44 epoch=32/80 step=900/1275 loss=35.97727 lr=0.00002164
+2026-06-28 02:21:53 epoch=32/80 step=950/1275 loss=36.05735 lr=0.00002162
+2026-06-28 02:22:03 epoch=32/80 step=1000/1275 loss=35.98166 lr=0.00002159
+2026-06-28 02:22:12 epoch=32/80 step=1050/1275 loss=36.18002 lr=0.00002157
+2026-06-28 02:22:21 epoch=32/80 step=1100/1275 loss=36.14727 lr=0.00002155
+2026-06-28 02:22:30 epoch=32/80 step=1150/1275 loss=36.14571 lr=0.00002153
+2026-06-28 02:22:39 epoch=32/80 step=1200/1275 loss=36.13059 lr=0.00002151
+2026-06-28 02:22:48 epoch=32/80 step=1250/1275 loss=36.19463 lr=0.00002148
+2026-06-28 02:23:03 mask2former val mIoU=0.53428 background=0.59835 live_coral=0.66692 dead_coral=0.38970 bleached_coral=0.48215
+2026-06-28 02:23:05 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 02:23:16 epoch=33/80 step=50/1275 loss=36.47649 lr=0.00002145
+2026-06-28 02:23:26 epoch=33/80 step=100/1275 loss=34.91689 lr=0.00002143
+2026-06-28 02:23:36 epoch=33/80 step=150/1275 loss=34.18952 lr=0.00002141
+2026-06-28 02:23:45 epoch=33/80 step=200/1275 loss=34.15125 lr=0.00002138
+2026-06-28 02:23:55 epoch=33/80 step=250/1275 loss=34.08973 lr=0.00002136
+2026-06-28 02:24:06 epoch=33/80 step=300/1275 loss=34.67306 lr=0.00002134
+2026-06-28 02:24:17 epoch=33/80 step=350/1275 loss=34.91742 lr=0.00002132
+2026-06-28 02:24:27 epoch=33/80 step=400/1275 loss=35.33588 lr=0.00002130
+2026-06-28 02:24:38 epoch=33/80 step=450/1275 loss=35.17794 lr=0.00002127
+2026-06-28 02:24:49 epoch=33/80 step=500/1275 loss=35.51658 lr=0.00002125
+2026-06-28 02:25:00 epoch=33/80 step=550/1275 loss=35.52553 lr=0.00002123
+2026-06-28 02:25:11 epoch=33/80 step=600/1275 loss=35.57311 lr=0.00002121
+2026-06-28 02:25:22 epoch=33/80 step=650/1275 loss=35.66944 lr=0.00002118
+2026-06-28 02:25:32 epoch=33/80 step=700/1275 loss=35.60247 lr=0.00002116
+2026-06-28 02:25:42 epoch=33/80 step=750/1275 loss=35.72167 lr=0.00002114
+2026-06-28 02:25:51 epoch=33/80 step=800/1275 loss=35.70104 lr=0.00002112
+2026-06-28 02:26:01 epoch=33/80 step=850/1275 loss=35.72775 lr=0.00002110
+2026-06-28 02:26:10 epoch=33/80 step=900/1275 loss=35.74170 lr=0.00002107
+2026-06-28 02:26:19 epoch=33/80 step=950/1275 loss=35.73528 lr=0.00002105
+2026-06-28 02:26:29 epoch=33/80 step=1000/1275 loss=35.75143 lr=0.00002103
+2026-06-28 02:26:39 epoch=33/80 step=1050/1275 loss=35.73171 lr=0.00002101
+2026-06-28 02:26:48 epoch=33/80 step=1100/1275 loss=35.74312 lr=0.00002098
+2026-06-28 02:26:57 epoch=33/80 step=1150/1275 loss=35.79957 lr=0.00002096
+2026-06-28 02:27:06 epoch=33/80 step=1200/1275 loss=35.83389 lr=0.00002094
+2026-06-28 02:27:15 epoch=33/80 step=1250/1275 loss=35.81289 lr=0.00002092
+2026-06-28 02:27:31 mask2former val mIoU=0.48904 background=0.57316 live_coral=0.61066 dead_coral=0.38990 bleached_coral=0.38242
+2026-06-28 02:27:41 epoch=34/80 step=50/1275 loss=32.32117 lr=0.00002088
+2026-06-28 02:27:50 epoch=34/80 step=100/1275 loss=33.56612 lr=0.00002086
+2026-06-28 02:27:59 epoch=34/80 step=150/1275 loss=34.66477 lr=0.00002084
+2026-06-28 02:28:08 epoch=34/80 step=200/1275 loss=34.82203 lr=0.00002081
+2026-06-28 02:28:17 epoch=34/80 step=250/1275 loss=34.70454 lr=0.00002079
+2026-06-28 02:28:26 epoch=34/80 step=300/1275 loss=34.77546 lr=0.00002077
+2026-06-28 02:28:35 epoch=34/80 step=350/1275 loss=34.95674 lr=0.00002075
+2026-06-28 02:28:44 epoch=34/80 step=400/1275 loss=35.01347 lr=0.00002072
+2026-06-28 02:28:53 epoch=34/80 step=450/1275 loss=35.01863 lr=0.00002070
+2026-06-28 02:29:02 epoch=34/80 step=500/1275 loss=34.98030 lr=0.00002068
+2026-06-28 02:29:11 epoch=34/80 step=550/1275 loss=34.91635 lr=0.00002066
+2026-06-28 02:29:20 epoch=34/80 step=600/1275 loss=34.96530 lr=0.00002063
+2026-06-28 02:29:29 epoch=34/80 step=650/1275 loss=35.00850 lr=0.00002061
+2026-06-28 02:29:39 epoch=34/80 step=700/1275 loss=34.99469 lr=0.00002059
+2026-06-28 02:29:48 epoch=34/80 step=750/1275 loss=35.04247 lr=0.00002057
+2026-06-28 02:29:58 epoch=34/80 step=800/1275 loss=35.29177 lr=0.00002054
+2026-06-28 02:30:08 epoch=34/80 step=850/1275 loss=35.38867 lr=0.00002052
+2026-06-28 02:30:17 epoch=34/80 step=900/1275 loss=35.57279 lr=0.00002050
+2026-06-28 02:30:27 epoch=34/80 step=950/1275 loss=35.40256 lr=0.00002047
+2026-06-28 02:30:36 epoch=34/80 step=1000/1275 loss=35.46554 lr=0.00002045
+2026-06-28 02:30:45 epoch=34/80 step=1050/1275 loss=35.41652 lr=0.00002043
+2026-06-28 02:30:54 epoch=34/80 step=1100/1275 loss=35.50828 lr=0.00002041
+2026-06-28 02:31:03 epoch=34/80 step=1150/1275 loss=35.48719 lr=0.00002038
+2026-06-28 02:31:12 epoch=34/80 step=1200/1275 loss=35.51859 lr=0.00002036
+2026-06-28 02:31:21 epoch=34/80 step=1250/1275 loss=35.50105 lr=0.00002034
+2026-06-28 02:31:36 mask2former val mIoU=0.51121 background=0.60518 live_coral=0.65245 dead_coral=0.32056 bleached_coral=0.46664
+2026-06-28 02:31:47 epoch=35/80 step=50/1275 loss=38.96756 lr=0.00002030
+2026-06-28 02:31:57 epoch=35/80 step=100/1275 loss=37.23572 lr=0.00002028
+2026-06-28 02:32:07 epoch=35/80 step=150/1275 loss=36.02804 lr=0.00002026
+2026-06-28 02:32:16 epoch=35/80 step=200/1275 loss=35.30286 lr=0.00002023
+2026-06-28 02:32:25 epoch=35/80 step=250/1275 loss=34.86659 lr=0.00002021
+2026-06-28 02:32:34 epoch=35/80 step=300/1275 loss=34.54477 lr=0.00002019
+2026-06-28 02:32:43 epoch=35/80 step=350/1275 loss=35.10630 lr=0.00002017
+2026-06-28 02:32:52 epoch=35/80 step=400/1275 loss=34.79738 lr=0.00002014
+2026-06-28 02:33:01 epoch=35/80 step=450/1275 loss=34.48246 lr=0.00002012
+2026-06-28 02:33:10 epoch=35/80 step=500/1275 loss=34.39621 lr=0.00002010
+2026-06-28 02:33:20 epoch=35/80 step=550/1275 loss=34.58889 lr=0.00002007
+2026-06-28 02:33:30 epoch=35/80 step=600/1275 loss=34.69200 lr=0.00002005
+2026-06-28 02:33:40 epoch=35/80 step=650/1275 loss=34.75288 lr=0.00002003
+2026-06-28 02:33:49 epoch=35/80 step=700/1275 loss=34.76614 lr=0.00002000
+2026-06-28 02:33:58 epoch=35/80 step=750/1275 loss=34.75745 lr=0.00001998
+2026-06-28 02:34:08 epoch=35/80 step=800/1275 loss=34.72300 lr=0.00001996
+2026-06-28 02:34:17 epoch=35/80 step=850/1275 loss=34.90557 lr=0.00001994
+2026-06-28 02:34:26 epoch=35/80 step=900/1275 loss=34.99570 lr=0.00001991
+2026-06-28 02:34:35 epoch=35/80 step=950/1275 loss=35.05577 lr=0.00001989
+2026-06-28 02:34:45 epoch=35/80 step=1000/1275 loss=35.01658 lr=0.00001987
+2026-06-28 02:34:54 epoch=35/80 step=1050/1275 loss=34.96677 lr=0.00001984
+2026-06-28 02:35:03 epoch=35/80 step=1100/1275 loss=34.84595 lr=0.00001982
+2026-06-28 02:35:12 epoch=35/80 step=1150/1275 loss=34.79131 lr=0.00001980
+2026-06-28 02:35:21 epoch=35/80 step=1200/1275 loss=34.91986 lr=0.00001977
+2026-06-28 02:35:30 epoch=35/80 step=1250/1275 loss=34.91773 lr=0.00001975
+2026-06-28 02:35:45 mask2former val mIoU=0.52515 background=0.60672 live_coral=0.65769 dead_coral=0.38746 bleached_coral=0.44873
+2026-06-28 02:35:56 epoch=36/80 step=50/1275 loss=35.59398 lr=0.00001972
+2026-06-28 02:36:05 epoch=36/80 step=100/1275 loss=36.60957 lr=0.00001969
+2026-06-28 02:36:14 epoch=36/80 step=150/1275 loss=35.81460 lr=0.00001967
+2026-06-28 02:36:23 epoch=36/80 step=200/1275 loss=35.04125 lr=0.00001965
+2026-06-28 02:36:32 epoch=36/80 step=250/1275 loss=34.58704 lr=0.00001962
+2026-06-28 02:36:42 epoch=36/80 step=300/1275 loss=35.10380 lr=0.00001960
+2026-06-28 02:36:51 epoch=36/80 step=350/1275 loss=34.85394 lr=0.00001958
+2026-06-28 02:37:00 epoch=36/80 step=400/1275 loss=34.86727 lr=0.00001955
+2026-06-28 02:37:10 epoch=36/80 step=450/1275 loss=35.10706 lr=0.00001953
+2026-06-28 02:37:19 epoch=36/80 step=500/1275 loss=34.90922 lr=0.00001951
+2026-06-28 02:37:29 epoch=36/80 step=550/1275 loss=34.76051 lr=0.00001948
+2026-06-28 02:37:38 epoch=36/80 step=600/1275 loss=34.80908 lr=0.00001946
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+2026-06-28 02:37:56 epoch=36/80 step=700/1275 loss=34.79454 lr=0.00001941
+2026-06-28 02:38:05 epoch=36/80 step=750/1275 loss=34.71090 lr=0.00001939
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+2026-06-28 02:38:24 epoch=36/80 step=850/1275 loss=34.59987 lr=0.00001934
+2026-06-28 02:38:35 epoch=36/80 step=900/1275 loss=34.60306 lr=0.00001932
+2026-06-28 02:38:46 epoch=36/80 step=950/1275 loss=34.54954 lr=0.00001930
+2026-06-28 02:38:57 epoch=36/80 step=1000/1275 loss=34.57855 lr=0.00001927
+2026-06-28 02:39:07 epoch=36/80 step=1050/1275 loss=34.68593 lr=0.00001925
+2026-06-28 02:39:17 epoch=36/80 step=1100/1275 loss=34.63885 lr=0.00001923
+2026-06-28 02:39:26 epoch=36/80 step=1150/1275 loss=34.70187 lr=0.00001920
+2026-06-28 02:39:36 epoch=36/80 step=1200/1275 loss=34.80351 lr=0.00001918
+2026-06-28 02:39:47 epoch=36/80 step=1250/1275 loss=34.73685 lr=0.00001916
+2026-06-28 02:40:03 mask2former val mIoU=0.54119 background=0.58323 live_coral=0.67334 dead_coral=0.41736 bleached_coral=0.49081
+2026-06-28 02:40:06 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 02:40:16 epoch=37/80 step=50/1275 loss=30.22616 lr=0.00001912
+2026-06-28 02:40:27 epoch=37/80 step=100/1275 loss=32.08061 lr=0.00001910
+2026-06-28 02:40:38 epoch=37/80 step=150/1275 loss=32.15224 lr=0.00001907
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+2026-06-28 02:40:58 epoch=37/80 step=250/1275 loss=32.84883 lr=0.00001903
+2026-06-28 02:41:08 epoch=37/80 step=300/1275 loss=32.84217 lr=0.00001900
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+2026-06-28 02:44:38 mask2former val mIoU=0.52440 background=0.58338 live_coral=0.66427 dead_coral=0.38194 bleached_coral=0.46801
+2026-06-28 02:44:50 epoch=38/80 step=50/1275 loss=35.92639 lr=0.00001852
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+2026-06-28 02:49:12 mask2former val mIoU=0.52501 background=0.59504 live_coral=0.66089 dead_coral=0.40154 bleached_coral=0.44256
+2026-06-28 02:49:23 epoch=39/80 step=50/1275 loss=34.51827 lr=0.00001791
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+2026-06-28 02:52:39 epoch=39/80 step=1000/1275 loss=33.71127 lr=0.00001745
+2026-06-28 02:52:49 epoch=39/80 step=1050/1275 loss=33.54009 lr=0.00001743
+2026-06-28 02:53:00 epoch=39/80 step=1100/1275 loss=33.62717 lr=0.00001740
+2026-06-28 02:53:10 epoch=39/80 step=1150/1275 loss=33.62140 lr=0.00001738
+2026-06-28 02:53:20 epoch=39/80 step=1200/1275 loss=33.52059 lr=0.00001736
+2026-06-28 02:53:31 epoch=39/80 step=1250/1275 loss=33.55507 lr=0.00001733
+2026-06-28 02:53:46 mask2former val mIoU=0.55255 background=0.60499 live_coral=0.68686 dead_coral=0.40600 bleached_coral=0.51234
+2026-06-28 02:53:48 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 02:53:58 epoch=40/80 step=50/1275 loss=33.23812 lr=0.00001730
+2026-06-28 02:54:09 epoch=40/80 step=100/1275 loss=32.31700 lr=0.00001727
+2026-06-28 02:54:20 epoch=40/80 step=150/1275 loss=32.44401 lr=0.00001725
+2026-06-28 02:54:31 epoch=40/80 step=200/1275 loss=32.42168 lr=0.00001722
+2026-06-28 02:54:41 epoch=40/80 step=250/1275 loss=32.23034 lr=0.00001720
+2026-06-28 02:54:52 epoch=40/80 step=300/1275 loss=32.18336 lr=0.00001717
+2026-06-28 02:55:03 epoch=40/80 step=350/1275 loss=32.29784 lr=0.00001715
+2026-06-28 02:55:13 epoch=40/80 step=400/1275 loss=32.43825 lr=0.00001713
+2026-06-28 02:55:23 epoch=40/80 step=450/1275 loss=32.72589 lr=0.00001710
+2026-06-28 02:55:33 epoch=40/80 step=500/1275 loss=32.67233 lr=0.00001708
+2026-06-28 02:55:44 epoch=40/80 step=550/1275 loss=32.78257 lr=0.00001705
+2026-06-28 02:55:55 epoch=40/80 step=600/1275 loss=32.69956 lr=0.00001703
+2026-06-28 02:56:05 epoch=40/80 step=650/1275 loss=32.75414 lr=0.00001701
+2026-06-28 02:56:14 epoch=40/80 step=700/1275 loss=32.86271 lr=0.00001698
+2026-06-28 02:56:24 epoch=40/80 step=750/1275 loss=32.73983 lr=0.00001696
+2026-06-28 02:56:34 epoch=40/80 step=800/1275 loss=32.70013 lr=0.00001693
+2026-06-28 02:56:44 epoch=40/80 step=850/1275 loss=32.60137 lr=0.00001691
+2026-06-28 02:56:55 epoch=40/80 step=900/1275 loss=32.69371 lr=0.00001688
+2026-06-28 02:57:05 epoch=40/80 step=950/1275 loss=32.81132 lr=0.00001686
+2026-06-28 02:57:16 epoch=40/80 step=1000/1275 loss=32.80130 lr=0.00001684
+2026-06-28 02:57:26 epoch=40/80 step=1050/1275 loss=32.89747 lr=0.00001681
+2026-06-28 02:57:37 epoch=40/80 step=1100/1275 loss=32.96664 lr=0.00001679
+2026-06-28 02:57:48 epoch=40/80 step=1150/1275 loss=32.94302 lr=0.00001676
+2026-06-28 02:57:59 epoch=40/80 step=1200/1275 loss=32.94090 lr=0.00001674
+2026-06-28 02:58:10 epoch=40/80 step=1250/1275 loss=32.86633 lr=0.00001671
+2026-06-28 02:58:27 mask2former val mIoU=0.54927 background=0.60251 live_coral=0.68209 dead_coral=0.42064 bleached_coral=0.49185
+2026-06-28 02:58:39 epoch=41/80 step=50/1275 loss=33.44699 lr=0.00001668
+2026-06-28 02:58:49 epoch=41/80 step=100/1275 loss=32.70189 lr=0.00001665
+2026-06-28 02:59:00 epoch=41/80 step=150/1275 loss=33.30547 lr=0.00001663
+2026-06-28 02:59:11 epoch=41/80 step=200/1275 loss=32.76440 lr=0.00001661
+2026-06-28 02:59:22 epoch=41/80 step=250/1275 loss=33.04626 lr=0.00001658
+2026-06-28 02:59:32 epoch=41/80 step=300/1275 loss=32.70456 lr=0.00001656
+2026-06-28 02:59:42 epoch=41/80 step=350/1275 loss=33.03631 lr=0.00001653
+2026-06-28 02:59:53 epoch=41/80 step=400/1275 loss=33.04528 lr=0.00001651
+2026-06-28 03:00:04 epoch=41/80 step=450/1275 loss=33.06666 lr=0.00001648
+2026-06-28 03:00:15 epoch=41/80 step=500/1275 loss=32.83727 lr=0.00001646
+2026-06-28 03:00:26 epoch=41/80 step=550/1275 loss=32.75927 lr=0.00001644
+2026-06-28 03:00:37 epoch=41/80 step=600/1275 loss=33.04255 lr=0.00001641
+2026-06-28 03:00:48 epoch=41/80 step=650/1275 loss=32.91703 lr=0.00001639
+2026-06-28 03:00:59 epoch=41/80 step=700/1275 loss=32.92954 lr=0.00001636
+2026-06-28 03:01:10 epoch=41/80 step=750/1275 loss=32.84954 lr=0.00001634
+2026-06-28 03:01:19 epoch=41/80 step=800/1275 loss=32.86529 lr=0.00001631
+2026-06-28 03:01:29 epoch=41/80 step=850/1275 loss=32.91395 lr=0.00001629
+2026-06-28 03:01:39 epoch=41/80 step=900/1275 loss=32.81486 lr=0.00001626
+2026-06-28 03:01:49 epoch=41/80 step=950/1275 loss=32.88482 lr=0.00001624
+2026-06-28 03:02:00 epoch=41/80 step=1000/1275 loss=32.86106 lr=0.00001622
+2026-06-28 03:02:11 epoch=41/80 step=1050/1275 loss=32.78921 lr=0.00001619
+2026-06-28 03:02:21 epoch=41/80 step=1100/1275 loss=32.74759 lr=0.00001617
+2026-06-28 03:02:31 epoch=41/80 step=1150/1275 loss=32.77057 lr=0.00001614
+2026-06-28 03:02:41 epoch=41/80 step=1200/1275 loss=32.66986 lr=0.00001612
+2026-06-28 03:02:52 epoch=41/80 step=1250/1275 loss=32.62246 lr=0.00001609
+2026-06-28 03:03:10 mask2former val mIoU=0.54685 background=0.59151 live_coral=0.67567 dead_coral=0.41314 bleached_coral=0.50708
+2026-06-28 03:03:21 epoch=42/80 step=50/1275 loss=29.18440 lr=0.00001606
+2026-06-28 03:03:32 epoch=42/80 step=100/1275 loss=30.79773 lr=0.00001603
+2026-06-28 03:03:42 epoch=42/80 step=150/1275 loss=31.64386 lr=0.00001601
+2026-06-28 03:03:53 epoch=42/80 step=200/1275 loss=31.57040 lr=0.00001599
+2026-06-28 03:04:03 epoch=42/80 step=250/1275 loss=32.08150 lr=0.00001596
+2026-06-28 03:04:13 epoch=42/80 step=300/1275 loss=32.15517 lr=0.00001594
+2026-06-28 03:04:23 epoch=42/80 step=350/1275 loss=32.03355 lr=0.00001591
+2026-06-28 03:04:34 epoch=42/80 step=400/1275 loss=32.03222 lr=0.00001589
+2026-06-28 03:04:45 epoch=42/80 step=450/1275 loss=31.96921 lr=0.00001586
+2026-06-28 03:04:56 epoch=42/80 step=500/1275 loss=32.11906 lr=0.00001584
+2026-06-28 03:05:07 epoch=42/80 step=550/1275 loss=32.17039 lr=0.00001581
+2026-06-28 03:05:17 epoch=42/80 step=600/1275 loss=32.15971 lr=0.00001579
+2026-06-28 03:05:28 epoch=42/80 step=650/1275 loss=32.27520 lr=0.00001577
+2026-06-28 03:05:39 epoch=42/80 step=700/1275 loss=32.25037 lr=0.00001574
+2026-06-28 03:05:50 epoch=42/80 step=750/1275 loss=32.30339 lr=0.00001572
+2026-06-28 03:06:00 epoch=42/80 step=800/1275 loss=32.29176 lr=0.00001569
+2026-06-28 03:06:11 epoch=42/80 step=850/1275 loss=32.22085 lr=0.00001567
+2026-06-28 03:06:22 epoch=42/80 step=900/1275 loss=32.22833 lr=0.00001564
+2026-06-28 03:06:33 epoch=42/80 step=950/1275 loss=32.12810 lr=0.00001562
+2026-06-28 03:06:43 epoch=42/80 step=1000/1275 loss=32.16665 lr=0.00001560
+2026-06-28 03:06:53 epoch=42/80 step=1050/1275 loss=32.21649 lr=0.00001557
+2026-06-28 03:07:03 epoch=42/80 step=1100/1275 loss=32.15351 lr=0.00001555
+2026-06-28 03:07:12 epoch=42/80 step=1150/1275 loss=32.12275 lr=0.00001552
+2026-06-28 03:07:22 epoch=42/80 step=1200/1275 loss=32.07111 lr=0.00001550
+2026-06-28 03:07:32 epoch=42/80 step=1250/1275 loss=31.96614 lr=0.00001547
+2026-06-28 03:07:48 mask2former val mIoU=0.53301 background=0.57803 live_coral=0.66549 dead_coral=0.39945 bleached_coral=0.48907
+2026-06-28 03:07:59 epoch=43/80 step=50/1275 loss=33.41165 lr=0.00001544
+2026-06-28 03:08:10 epoch=43/80 step=100/1275 loss=32.84230 lr=0.00001541
+2026-06-28 03:08:21 epoch=43/80 step=150/1275 loss=32.43496 lr=0.00001539
+2026-06-28 03:08:30 epoch=43/80 step=200/1275 loss=33.15912 lr=0.00001536
+2026-06-28 03:08:40 epoch=43/80 step=250/1275 loss=32.80105 lr=0.00001534
+2026-06-28 03:08:50 epoch=43/80 step=300/1275 loss=32.68929 lr=0.00001531
+2026-06-28 03:09:00 epoch=43/80 step=350/1275 loss=32.61383 lr=0.00001529
+2026-06-28 03:09:09 epoch=43/80 step=400/1275 loss=32.37558 lr=0.00001527
+2026-06-28 03:09:19 epoch=43/80 step=450/1275 loss=32.53800 lr=0.00001524
+2026-06-28 03:09:28 epoch=43/80 step=500/1275 loss=32.50263 lr=0.00001522
+2026-06-28 03:09:38 epoch=43/80 step=550/1275 loss=32.19962 lr=0.00001519
+2026-06-28 03:09:48 epoch=43/80 step=600/1275 loss=31.98489 lr=0.00001517
+2026-06-28 03:09:58 epoch=43/80 step=650/1275 loss=32.14343 lr=0.00001514
+2026-06-28 03:10:08 epoch=43/80 step=700/1275 loss=32.06448 lr=0.00001512
+2026-06-28 03:10:17 epoch=43/80 step=750/1275 loss=32.16433 lr=0.00001510
+2026-06-28 03:10:27 epoch=43/80 step=800/1275 loss=32.34712 lr=0.00001507
+2026-06-28 03:10:36 epoch=43/80 step=850/1275 loss=32.21287 lr=0.00001505
+2026-06-28 03:10:46 epoch=43/80 step=900/1275 loss=32.20349 lr=0.00001502
+2026-06-28 03:10:57 epoch=43/80 step=950/1275 loss=32.18883 lr=0.00001500
+2026-06-28 03:11:07 epoch=43/80 step=1000/1275 loss=32.16152 lr=0.00001497
+2026-06-28 03:11:17 epoch=43/80 step=1050/1275 loss=32.16597 lr=0.00001495
+2026-06-28 03:11:28 epoch=43/80 step=1100/1275 loss=32.16553 lr=0.00001492
+2026-06-28 03:11:38 epoch=43/80 step=1150/1275 loss=32.09216 lr=0.00001490
+2026-06-28 03:11:48 epoch=43/80 step=1200/1275 loss=31.97902 lr=0.00001488
+2026-06-28 03:11:58 epoch=43/80 step=1250/1275 loss=31.97526 lr=0.00001485
+2026-06-28 03:12:14 mask2former val mIoU=0.52760 background=0.57882 live_coral=0.66152 dead_coral=0.40490 bleached_coral=0.46516
+2026-06-28 03:12:25 epoch=44/80 step=50/1275 loss=29.87800 lr=0.00001481
+2026-06-28 03:12:35 epoch=44/80 step=100/1275 loss=31.18763 lr=0.00001479
+2026-06-28 03:12:46 epoch=44/80 step=150/1275 loss=31.25381 lr=0.00001477
+2026-06-28 03:12:57 epoch=44/80 step=200/1275 loss=31.92880 lr=0.00001474
+2026-06-28 03:13:08 epoch=44/80 step=250/1275 loss=31.38690 lr=0.00001472
+2026-06-28 03:13:19 epoch=44/80 step=300/1275 loss=31.66442 lr=0.00001469
+2026-06-28 03:13:30 epoch=44/80 step=350/1275 loss=31.37202 lr=0.00001467
+2026-06-28 03:13:41 epoch=44/80 step=400/1275 loss=31.21183 lr=0.00001464
+2026-06-28 03:13:53 epoch=44/80 step=450/1275 loss=31.28084 lr=0.00001462
+2026-06-28 03:14:03 epoch=44/80 step=500/1275 loss=31.27363 lr=0.00001460
+2026-06-28 03:14:14 epoch=44/80 step=550/1275 loss=31.41219 lr=0.00001457
+2026-06-28 03:14:24 epoch=44/80 step=600/1275 loss=31.55453 lr=0.00001455
+2026-06-28 03:14:34 epoch=44/80 step=650/1275 loss=31.52246 lr=0.00001452
+2026-06-28 03:14:45 epoch=44/80 step=700/1275 loss=31.45371 lr=0.00001450
+2026-06-28 03:14:55 epoch=44/80 step=750/1275 loss=31.49624 lr=0.00001447
+2026-06-28 03:15:06 epoch=44/80 step=800/1275 loss=31.60379 lr=0.00001445
+2026-06-28 03:15:16 epoch=44/80 step=850/1275 loss=31.58539 lr=0.00001442
+2026-06-28 03:15:27 epoch=44/80 step=900/1275 loss=31.59468 lr=0.00001440
+2026-06-28 03:15:37 epoch=44/80 step=950/1275 loss=31.59948 lr=0.00001438
+2026-06-28 03:15:48 epoch=44/80 step=1000/1275 loss=31.58645 lr=0.00001435
+2026-06-28 03:15:58 epoch=44/80 step=1050/1275 loss=31.62853 lr=0.00001433
+2026-06-28 03:16:09 epoch=44/80 step=1100/1275 loss=31.62476 lr=0.00001430
+2026-06-28 03:16:20 epoch=44/80 step=1150/1275 loss=31.58571 lr=0.00001428
+2026-06-28 03:16:31 epoch=44/80 step=1200/1275 loss=31.52768 lr=0.00001425
+2026-06-28 03:16:42 epoch=44/80 step=1250/1275 loss=31.54839 lr=0.00001423
+2026-06-28 03:16:58 mask2former val mIoU=0.55255 background=0.59575 live_coral=0.67355 dead_coral=0.43630 bleached_coral=0.50461
+2026-06-28 03:17:00 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 03:17:10 epoch=45/80 step=50/1275 loss=32.70751 lr=0.00001419
+2026-06-28 03:17:20 epoch=45/80 step=100/1275 loss=31.40533 lr=0.00001417
+2026-06-28 03:17:30 epoch=45/80 step=150/1275 loss=30.64254 lr=0.00001414
+2026-06-28 03:17:40 epoch=45/80 step=200/1275 loss=30.73947 lr=0.00001412
+2026-06-28 03:17:50 epoch=45/80 step=250/1275 loss=30.71596 lr=0.00001410
+2026-06-28 03:18:01 epoch=45/80 step=300/1275 loss=30.89768 lr=0.00001407
+2026-06-28 03:18:11 epoch=45/80 step=350/1275 loss=30.78552 lr=0.00001405
+2026-06-28 03:18:22 epoch=45/80 step=400/1275 loss=30.87465 lr=0.00001402
+2026-06-28 03:18:33 epoch=45/80 step=450/1275 loss=30.80945 lr=0.00001400
+2026-06-28 03:18:44 epoch=45/80 step=500/1275 loss=31.07842 lr=0.00001397
+2026-06-28 03:18:56 epoch=45/80 step=550/1275 loss=31.21414 lr=0.00001395
+2026-06-28 03:19:06 epoch=45/80 step=600/1275 loss=31.17565 lr=0.00001393
+2026-06-28 03:19:17 epoch=45/80 step=650/1275 loss=31.16422 lr=0.00001390
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+2026-06-28 03:19:38 epoch=45/80 step=750/1275 loss=31.04889 lr=0.00001385
+2026-06-28 03:19:49 epoch=45/80 step=800/1275 loss=31.11276 lr=0.00001383
+2026-06-28 03:19:59 epoch=45/80 step=850/1275 loss=31.06013 lr=0.00001380
+2026-06-28 03:20:10 epoch=45/80 step=900/1275 loss=31.07778 lr=0.00001378
+2026-06-28 03:20:20 epoch=45/80 step=950/1275 loss=31.10940 lr=0.00001376
+2026-06-28 03:20:30 epoch=45/80 step=1000/1275 loss=31.14622 lr=0.00001373
+2026-06-28 03:20:41 epoch=45/80 step=1050/1275 loss=31.15188 lr=0.00001371
+2026-06-28 03:20:52 epoch=45/80 step=1100/1275 loss=31.06341 lr=0.00001368
+2026-06-28 03:21:03 epoch=45/80 step=1150/1275 loss=31.07241 lr=0.00001366
+2026-06-28 03:21:13 epoch=45/80 step=1200/1275 loss=31.05771 lr=0.00001363
+2026-06-28 03:21:23 epoch=45/80 step=1250/1275 loss=31.09854 lr=0.00001361
+2026-06-28 03:21:41 mask2former val mIoU=0.55071 background=0.61052 live_coral=0.68008 dead_coral=0.40294 bleached_coral=0.50931
+2026-06-28 03:21:53 epoch=46/80 step=50/1275 loss=28.43230 lr=0.00001357
+2026-06-28 03:22:02 epoch=46/80 step=100/1275 loss=29.24203 lr=0.00001355
+2026-06-28 03:22:12 epoch=46/80 step=150/1275 loss=30.12338 lr=0.00001352
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+2026-06-28 03:22:32 epoch=46/80 step=250/1275 loss=30.55069 lr=0.00001348
+2026-06-28 03:22:42 epoch=46/80 step=300/1275 loss=30.58164 lr=0.00001345
+2026-06-28 03:22:52 epoch=46/80 step=350/1275 loss=30.57373 lr=0.00001343
+2026-06-28 03:23:02 epoch=46/80 step=400/1275 loss=30.30264 lr=0.00001340
+2026-06-28 03:23:13 epoch=46/80 step=450/1275 loss=30.33861 lr=0.00001338
+2026-06-28 03:23:24 epoch=46/80 step=500/1275 loss=30.57563 lr=0.00001336
+2026-06-28 03:23:34 epoch=46/80 step=550/1275 loss=30.66990 lr=0.00001333
+2026-06-28 03:23:46 epoch=46/80 step=600/1275 loss=30.66447 lr=0.00001331
+2026-06-28 03:23:57 epoch=46/80 step=650/1275 loss=30.71521 lr=0.00001328
+2026-06-28 03:24:08 epoch=46/80 step=700/1275 loss=30.69249 lr=0.00001326
+2026-06-28 03:24:19 epoch=46/80 step=750/1275 loss=30.75691 lr=0.00001323
+2026-06-28 03:24:29 epoch=46/80 step=800/1275 loss=30.68953 lr=0.00001321
+2026-06-28 03:24:38 epoch=46/80 step=850/1275 loss=30.60005 lr=0.00001319
+2026-06-28 03:24:48 epoch=46/80 step=900/1275 loss=30.65373 lr=0.00001316
+2026-06-28 03:24:57 epoch=46/80 step=950/1275 loss=30.84797 lr=0.00001314
+2026-06-28 03:25:07 epoch=46/80 step=1000/1275 loss=30.93956 lr=0.00001311
+2026-06-28 03:25:16 epoch=46/80 step=1050/1275 loss=30.95143 lr=0.00001309
+2026-06-28 03:25:25 epoch=46/80 step=1100/1275 loss=31.05717 lr=0.00001307
+2026-06-28 03:25:35 epoch=46/80 step=1150/1275 loss=31.07985 lr=0.00001304
+2026-06-28 03:25:45 epoch=46/80 step=1200/1275 loss=31.03997 lr=0.00001302
+2026-06-28 03:25:56 epoch=46/80 step=1250/1275 loss=31.11167 lr=0.00001299
+2026-06-28 03:26:13 mask2former val mIoU=0.54326 background=0.59746 live_coral=0.69016 dead_coral=0.36777 bleached_coral=0.51765
+2026-06-28 03:26:25 epoch=47/80 step=50/1275 loss=32.30772 lr=0.00001296
+2026-06-28 03:26:35 epoch=47/80 step=100/1275 loss=31.41097 lr=0.00001293
+2026-06-28 03:26:45 epoch=47/80 step=150/1275 loss=30.12183 lr=0.00001291
+2026-06-28 03:26:55 epoch=47/80 step=200/1275 loss=30.10930 lr=0.00001288
+2026-06-28 03:27:06 epoch=47/80 step=250/1275 loss=30.38294 lr=0.00001286
+2026-06-28 03:27:16 epoch=47/80 step=300/1275 loss=30.64395 lr=0.00001284
+2026-06-28 03:27:26 epoch=47/80 step=350/1275 loss=30.32566 lr=0.00001281
+2026-06-28 03:27:37 epoch=47/80 step=400/1275 loss=30.55880 lr=0.00001279
+2026-06-28 03:27:47 epoch=47/80 step=450/1275 loss=30.61336 lr=0.00001276
+2026-06-28 03:27:57 epoch=47/80 step=500/1275 loss=30.58835 lr=0.00001274
+2026-06-28 03:28:07 epoch=47/80 step=550/1275 loss=30.40994 lr=0.00001272
+2026-06-28 03:28:16 epoch=47/80 step=600/1275 loss=30.61654 lr=0.00001269
+2026-06-28 03:28:25 epoch=47/80 step=650/1275 loss=30.57438 lr=0.00001267
+2026-06-28 03:28:35 epoch=47/80 step=700/1275 loss=30.51378 lr=0.00001264
+2026-06-28 03:28:45 epoch=47/80 step=750/1275 loss=30.62537 lr=0.00001262
+2026-06-28 03:28:56 epoch=47/80 step=800/1275 loss=30.67651 lr=0.00001260
+2026-06-28 03:29:07 epoch=47/80 step=850/1275 loss=30.82097 lr=0.00001257
+2026-06-28 03:29:18 epoch=47/80 step=900/1275 loss=30.86130 lr=0.00001255
+2026-06-28 03:29:29 epoch=47/80 step=950/1275 loss=30.81246 lr=0.00001252
+2026-06-28 03:29:39 epoch=47/80 step=1000/1275 loss=30.78703 lr=0.00001250
+2026-06-28 03:29:50 epoch=47/80 step=1050/1275 loss=30.72603 lr=0.00001248
+2026-06-28 03:30:00 epoch=47/80 step=1100/1275 loss=30.74700 lr=0.00001245
+2026-06-28 03:30:10 epoch=47/80 step=1150/1275 loss=30.79495 lr=0.00001243
+2026-06-28 03:30:21 epoch=47/80 step=1200/1275 loss=30.74146 lr=0.00001240
+2026-06-28 03:30:30 epoch=47/80 step=1250/1275 loss=30.83223 lr=0.00001238
+2026-06-28 03:30:47 mask2former val mIoU=0.55994 background=0.59454 live_coral=0.68691 dead_coral=0.43863 bleached_coral=0.51969
+2026-06-28 03:30:48 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 03:30:59 epoch=48/80 step=50/1275 loss=30.62121 lr=0.00001234
+2026-06-28 03:31:09 epoch=48/80 step=100/1275 loss=30.64027 lr=0.00001232
+2026-06-28 03:31:19 epoch=48/80 step=150/1275 loss=30.27731 lr=0.00001230
+2026-06-28 03:31:31 epoch=48/80 step=200/1275 loss=29.84887 lr=0.00001227
+2026-06-28 03:31:42 epoch=48/80 step=250/1275 loss=29.97327 lr=0.00001225
+2026-06-28 03:31:53 epoch=48/80 step=300/1275 loss=29.99436 lr=0.00001222
+2026-06-28 03:32:04 epoch=48/80 step=350/1275 loss=29.99365 lr=0.00001220
+2026-06-28 03:32:15 epoch=48/80 step=400/1275 loss=29.94020 lr=0.00001218
+2026-06-28 03:32:26 epoch=48/80 step=450/1275 loss=29.92719 lr=0.00001215
+2026-06-28 03:32:37 epoch=48/80 step=500/1275 loss=29.84826 lr=0.00001213
+2026-06-28 03:32:48 epoch=48/80 step=550/1275 loss=29.91469 lr=0.00001210
+2026-06-28 03:32:58 epoch=48/80 step=600/1275 loss=29.92690 lr=0.00001208
+2026-06-28 03:33:08 epoch=48/80 step=650/1275 loss=29.88644 lr=0.00001206
+2026-06-28 03:33:19 epoch=48/80 step=700/1275 loss=29.93379 lr=0.00001203
+2026-06-28 03:33:29 epoch=48/80 step=750/1275 loss=29.89561 lr=0.00001201
+2026-06-28 03:33:39 epoch=48/80 step=800/1275 loss=29.84923 lr=0.00001199
+2026-06-28 03:33:49 epoch=48/80 step=850/1275 loss=29.78232 lr=0.00001196
+2026-06-28 03:34:00 epoch=48/80 step=900/1275 loss=29.87570 lr=0.00001194
+2026-06-28 03:34:11 epoch=48/80 step=950/1275 loss=29.94820 lr=0.00001191
+2026-06-28 03:34:21 epoch=48/80 step=1000/1275 loss=29.99327 lr=0.00001189
+2026-06-28 03:34:32 epoch=48/80 step=1050/1275 loss=30.06244 lr=0.00001187
+2026-06-28 03:34:42 epoch=48/80 step=1100/1275 loss=30.08314 lr=0.00001184
+2026-06-28 03:34:52 epoch=48/80 step=1150/1275 loss=30.12388 lr=0.00001182
+2026-06-28 03:35:02 epoch=48/80 step=1200/1275 loss=30.09575 lr=0.00001179
+2026-06-28 03:35:13 epoch=48/80 step=1250/1275 loss=30.13645 lr=0.00001177
+2026-06-28 03:35:29 mask2former val mIoU=0.55476 background=0.62206 live_coral=0.68019 dead_coral=0.42519 bleached_coral=0.49158
+2026-06-28 03:35:41 epoch=49/80 step=50/1275 loss=31.51476 lr=0.00001174
+2026-06-28 03:35:52 epoch=49/80 step=100/1275 loss=32.23113 lr=0.00001171
+2026-06-28 03:36:01 epoch=49/80 step=150/1275 loss=31.69795 lr=0.00001169
+2026-06-28 03:36:11 epoch=49/80 step=200/1275 loss=30.66537 lr=0.00001166
+2026-06-28 03:36:21 epoch=49/80 step=250/1275 loss=30.44555 lr=0.00001164
+2026-06-28 03:36:32 epoch=49/80 step=300/1275 loss=30.24230 lr=0.00001162
+2026-06-28 03:36:43 epoch=49/80 step=350/1275 loss=30.08578 lr=0.00001159
+2026-06-28 03:36:53 epoch=49/80 step=400/1275 loss=30.02908 lr=0.00001157
+2026-06-28 03:37:04 epoch=49/80 step=450/1275 loss=30.11839 lr=0.00001155
+2026-06-28 03:37:15 epoch=49/80 step=500/1275 loss=30.21461 lr=0.00001152
+2026-06-28 03:37:26 epoch=49/80 step=550/1275 loss=30.28743 lr=0.00001150
+2026-06-28 03:37:35 epoch=49/80 step=600/1275 loss=30.19322 lr=0.00001147
+2026-06-28 03:37:46 epoch=49/80 step=650/1275 loss=30.13760 lr=0.00001145
+2026-06-28 03:37:56 epoch=49/80 step=700/1275 loss=30.09372 lr=0.00001143
+2026-06-28 03:38:07 epoch=49/80 step=750/1275 loss=29.98824 lr=0.00001140
+2026-06-28 03:38:17 epoch=49/80 step=800/1275 loss=29.95509 lr=0.00001138
+2026-06-28 03:38:28 epoch=49/80 step=850/1275 loss=29.98995 lr=0.00001136
+2026-06-28 03:38:38 epoch=49/80 step=900/1275 loss=29.88994 lr=0.00001133
+2026-06-28 03:38:48 epoch=49/80 step=950/1275 loss=29.89011 lr=0.00001131
+2026-06-28 03:38:58 epoch=49/80 step=1000/1275 loss=29.81689 lr=0.00001129
+2026-06-28 03:39:09 epoch=49/80 step=1050/1275 loss=29.63118 lr=0.00001126
+2026-06-28 03:39:20 epoch=49/80 step=1100/1275 loss=29.64191 lr=0.00001124
+2026-06-28 03:39:30 epoch=49/80 step=1150/1275 loss=29.56839 lr=0.00001122
+2026-06-28 03:39:41 epoch=49/80 step=1200/1275 loss=29.62506 lr=0.00001119
+2026-06-28 03:39:51 epoch=49/80 step=1250/1275 loss=29.66591 lr=0.00001117
+2026-06-28 03:40:07 mask2former val mIoU=0.54763 background=0.58965 live_coral=0.67273 dead_coral=0.43024 bleached_coral=0.49791
+2026-06-28 03:40:19 epoch=50/80 step=50/1275 loss=29.73970 lr=0.00001113
+2026-06-28 03:40:30 epoch=50/80 step=100/1275 loss=30.56222 lr=0.00001111
+2026-06-28 03:40:41 epoch=50/80 step=150/1275 loss=29.97334 lr=0.00001109
+2026-06-28 03:40:52 epoch=50/80 step=200/1275 loss=29.49539 lr=0.00001106
+2026-06-28 03:41:03 epoch=50/80 step=250/1275 loss=29.09413 lr=0.00001104
+2026-06-28 03:41:13 epoch=50/80 step=300/1275 loss=28.99167 lr=0.00001102
+2026-06-28 03:41:24 epoch=50/80 step=350/1275 loss=29.41351 lr=0.00001099
+2026-06-28 03:41:35 epoch=50/80 step=400/1275 loss=29.53240 lr=0.00001097
+2026-06-28 03:41:46 epoch=50/80 step=450/1275 loss=29.56488 lr=0.00001095
+2026-06-28 03:41:58 epoch=50/80 step=500/1275 loss=29.49599 lr=0.00001092
+2026-06-28 03:42:08 epoch=50/80 step=550/1275 loss=29.49625 lr=0.00001090
+2026-06-28 03:42:19 epoch=50/80 step=600/1275 loss=29.65394 lr=0.00001088
+2026-06-28 03:42:29 epoch=50/80 step=650/1275 loss=29.71945 lr=0.00001085
+2026-06-28 03:42:40 epoch=50/80 step=700/1275 loss=29.70408 lr=0.00001083
+2026-06-28 03:42:51 epoch=50/80 step=750/1275 loss=29.57398 lr=0.00001081
+2026-06-28 03:43:02 epoch=50/80 step=800/1275 loss=29.68812 lr=0.00001078
+2026-06-28 03:43:12 epoch=50/80 step=850/1275 loss=29.65860 lr=0.00001076
+2026-06-28 03:43:23 epoch=50/80 step=900/1275 loss=29.65198 lr=0.00001074
+2026-06-28 03:43:32 epoch=50/80 step=950/1275 loss=29.67739 lr=0.00001071
+2026-06-28 03:43:42 epoch=50/80 step=1000/1275 loss=29.70404 lr=0.00001069
+2026-06-28 03:43:51 epoch=50/80 step=1050/1275 loss=29.64292 lr=0.00001067
+2026-06-28 03:44:01 epoch=50/80 step=1100/1275 loss=29.66376 lr=0.00001064
+2026-06-28 03:44:11 epoch=50/80 step=1150/1275 loss=29.74309 lr=0.00001062
+2026-06-28 03:44:22 epoch=50/80 step=1200/1275 loss=29.72453 lr=0.00001060
+2026-06-28 03:44:32 epoch=50/80 step=1250/1275 loss=29.64658 lr=0.00001057
+2026-06-28 03:44:49 mask2former val mIoU=0.56396 background=0.61873 live_coral=0.68604 dead_coral=0.45262 bleached_coral=0.49846
+2026-06-28 03:44:51 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 03:45:01 epoch=51/80 step=50/1275 loss=28.98431 lr=0.00001054
+2026-06-28 03:45:12 epoch=51/80 step=100/1275 loss=28.77074 lr=0.00001051
+2026-06-28 03:45:21 epoch=51/80 step=150/1275 loss=28.30031 lr=0.00001049
+2026-06-28 03:45:32 epoch=51/80 step=200/1275 loss=29.87862 lr=0.00001047
+2026-06-28 03:45:43 epoch=51/80 step=250/1275 loss=29.87652 lr=0.00001045
+2026-06-28 03:45:52 epoch=51/80 step=300/1275 loss=29.76773 lr=0.00001042
+2026-06-28 03:46:01 epoch=51/80 step=350/1275 loss=29.50572 lr=0.00001040
+2026-06-28 03:46:11 epoch=51/80 step=400/1275 loss=29.32137 lr=0.00001038
+2026-06-28 03:46:22 epoch=51/80 step=450/1275 loss=29.82844 lr=0.00001035
+2026-06-28 03:46:31 epoch=51/80 step=500/1275 loss=30.06617 lr=0.00001033
+2026-06-28 03:46:41 epoch=51/80 step=550/1275 loss=29.79516 lr=0.00001031
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+2026-06-28 03:47:21 epoch=51/80 step=750/1275 loss=29.82418 lr=0.00001021
+2026-06-28 03:47:32 epoch=51/80 step=800/1275 loss=29.70003 lr=0.00001019
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+2026-06-28 03:47:54 epoch=51/80 step=900/1275 loss=29.61363 lr=0.00001015
+2026-06-28 03:48:05 epoch=51/80 step=950/1275 loss=29.58456 lr=0.00001012
+2026-06-28 03:48:16 epoch=51/80 step=1000/1275 loss=29.70388 lr=0.00001010
+2026-06-28 03:48:27 epoch=51/80 step=1050/1275 loss=29.48646 lr=0.00001008
+2026-06-28 03:48:37 epoch=51/80 step=1100/1275 loss=29.47268 lr=0.00001005
+2026-06-28 03:48:48 epoch=51/80 step=1150/1275 loss=29.38128 lr=0.00001003
+2026-06-28 03:48:59 epoch=51/80 step=1200/1275 loss=29.37006 lr=0.00001001
+2026-06-28 03:49:10 epoch=51/80 step=1250/1275 loss=29.35896 lr=0.00000999
+2026-06-28 03:49:27 mask2former val mIoU=0.56100 background=0.62910 live_coral=0.68864 dead_coral=0.44724 bleached_coral=0.47902
+2026-06-28 03:49:39 epoch=52/80 step=50/1275 loss=26.97492 lr=0.00000995
+2026-06-28 03:49:49 epoch=52/80 step=100/1275 loss=27.54030 lr=0.00000993
+2026-06-28 03:50:00 epoch=52/80 step=150/1275 loss=28.25616 lr=0.00000991
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+2026-06-28 03:50:55 epoch=52/80 step=400/1275 loss=28.56733 lr=0.00000979
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+2026-06-28 03:51:26 epoch=52/80 step=550/1275 loss=28.59828 lr=0.00000972
+2026-06-28 03:51:35 epoch=52/80 step=600/1275 loss=28.79501 lr=0.00000970
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+2026-06-28 03:52:18 epoch=52/80 step=800/1275 loss=29.06540 lr=0.00000961
+2026-06-28 03:52:28 epoch=52/80 step=850/1275 loss=29.03996 lr=0.00000959
+2026-06-28 03:52:39 epoch=52/80 step=900/1275 loss=29.06833 lr=0.00000956
+2026-06-28 03:52:49 epoch=52/80 step=950/1275 loss=29.08383 lr=0.00000954
+2026-06-28 03:52:59 epoch=52/80 step=1000/1275 loss=28.96845 lr=0.00000952
+2026-06-28 03:53:09 epoch=52/80 step=1050/1275 loss=28.99158 lr=0.00000950
+2026-06-28 03:53:20 epoch=52/80 step=1100/1275 loss=28.96375 lr=0.00000947
+2026-06-28 03:53:30 epoch=52/80 step=1150/1275 loss=29.01909 lr=0.00000945
+2026-06-28 03:53:40 epoch=52/80 step=1200/1275 loss=29.03207 lr=0.00000943
+2026-06-28 03:53:50 epoch=52/80 step=1250/1275 loss=29.03935 lr=0.00000941
+2026-06-28 03:54:06 mask2former val mIoU=0.53989 background=0.61966 live_coral=0.66987 dead_coral=0.41168 bleached_coral=0.45834
+2026-06-28 03:54:17 epoch=53/80 step=50/1275 loss=27.29902 lr=0.00000937
+2026-06-28 03:54:28 epoch=53/80 step=100/1275 loss=28.30206 lr=0.00000935
+2026-06-28 03:54:39 epoch=53/80 step=150/1275 loss=28.18039 lr=0.00000933
+2026-06-28 03:54:50 epoch=53/80 step=200/1275 loss=28.05362 lr=0.00000931
+2026-06-28 03:55:01 epoch=53/80 step=250/1275 loss=28.52660 lr=0.00000928
+2026-06-28 03:55:12 epoch=53/80 step=300/1275 loss=28.72346 lr=0.00000926
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+2026-06-28 03:55:34 epoch=53/80 step=400/1275 loss=28.23184 lr=0.00000922
+2026-06-28 03:55:43 epoch=53/80 step=450/1275 loss=28.05006 lr=0.00000919
+2026-06-28 03:55:53 epoch=53/80 step=500/1275 loss=27.99423 lr=0.00000917
+2026-06-28 03:56:03 epoch=53/80 step=550/1275 loss=28.10154 lr=0.00000915
+2026-06-28 03:56:13 epoch=53/80 step=600/1275 loss=28.20057 lr=0.00000913
+2026-06-28 03:56:23 epoch=53/80 step=650/1275 loss=28.31856 lr=0.00000910
+2026-06-28 03:56:32 epoch=53/80 step=700/1275 loss=28.47445 lr=0.00000908
+2026-06-28 03:56:42 epoch=53/80 step=750/1275 loss=28.53287 lr=0.00000906
+2026-06-28 03:56:52 epoch=53/80 step=800/1275 loss=28.60136 lr=0.00000904
+2026-06-28 03:57:02 epoch=53/80 step=850/1275 loss=28.56585 lr=0.00000902
+2026-06-28 03:57:12 epoch=53/80 step=900/1275 loss=28.58976 lr=0.00000899
+2026-06-28 03:57:23 epoch=53/80 step=950/1275 loss=28.57869 lr=0.00000897
+2026-06-28 03:57:33 epoch=53/80 step=1000/1275 loss=28.59168 lr=0.00000895
+2026-06-28 03:57:43 epoch=53/80 step=1050/1275 loss=28.65246 lr=0.00000893
+2026-06-28 03:57:53 epoch=53/80 step=1100/1275 loss=28.78485 lr=0.00000890
+2026-06-28 03:58:04 epoch=53/80 step=1150/1275 loss=28.94038 lr=0.00000888
+2026-06-28 03:58:14 epoch=53/80 step=1200/1275 loss=28.85243 lr=0.00000886
+2026-06-28 03:58:25 epoch=53/80 step=1250/1275 loss=28.81037 lr=0.00000884
+2026-06-28 03:58:42 mask2former val mIoU=0.54286 background=0.61482 live_coral=0.66956 dead_coral=0.42133 bleached_coral=0.46572
+2026-06-28 03:58:53 epoch=54/80 step=50/1275 loss=29.22696 lr=0.00000881
+2026-06-28 03:59:03 epoch=54/80 step=100/1275 loss=28.35589 lr=0.00000878
+2026-06-28 03:59:14 epoch=54/80 step=150/1275 loss=27.59344 lr=0.00000876
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+2026-06-28 04:00:39 epoch=54/80 step=550/1275 loss=28.23284 lr=0.00000859
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+2026-06-28 04:01:21 epoch=54/80 step=750/1275 loss=28.37179 lr=0.00000850
+2026-06-28 04:01:31 epoch=54/80 step=800/1275 loss=28.35226 lr=0.00000848
+2026-06-28 04:01:42 epoch=54/80 step=850/1275 loss=28.45725 lr=0.00000845
+2026-06-28 04:01:52 epoch=54/80 step=900/1275 loss=28.44835 lr=0.00000843
+2026-06-28 04:02:03 epoch=54/80 step=950/1275 loss=28.50973 lr=0.00000841
+2026-06-28 04:02:14 epoch=54/80 step=1000/1275 loss=28.50492 lr=0.00000839
+2026-06-28 04:02:25 epoch=54/80 step=1050/1275 loss=28.49370 lr=0.00000837
+2026-06-28 04:02:36 epoch=54/80 step=1100/1275 loss=28.44732 lr=0.00000835
+2026-06-28 04:02:46 epoch=54/80 step=1150/1275 loss=28.38496 lr=0.00000832
+2026-06-28 04:02:56 epoch=54/80 step=1200/1275 loss=28.38320 lr=0.00000830
+2026-06-28 04:03:07 epoch=54/80 step=1250/1275 loss=28.34150 lr=0.00000828
+2026-06-28 04:03:24 mask2former val mIoU=0.54190 background=0.61201 live_coral=0.68256 dead_coral=0.37360 bleached_coral=0.49944
+2026-06-28 04:03:35 epoch=55/80 step=50/1275 loss=30.29290 lr=0.00000825
+2026-06-28 04:03:46 epoch=55/80 step=100/1275 loss=28.98471 lr=0.00000823
+2026-06-28 04:03:57 epoch=55/80 step=150/1275 loss=29.37878 lr=0.00000821
+2026-06-28 04:04:07 epoch=55/80 step=200/1275 loss=29.76856 lr=0.00000818
+2026-06-28 04:04:18 epoch=55/80 step=250/1275 loss=29.67672 lr=0.00000816
+2026-06-28 04:04:28 epoch=55/80 step=300/1275 loss=29.35123 lr=0.00000814
+2026-06-28 04:04:38 epoch=55/80 step=350/1275 loss=29.15091 lr=0.00000812
+2026-06-28 04:04:48 epoch=55/80 step=400/1275 loss=28.70753 lr=0.00000810
+2026-06-28 04:04:59 epoch=55/80 step=450/1275 loss=28.48727 lr=0.00000808
+2026-06-28 04:05:10 epoch=55/80 step=500/1275 loss=28.39381 lr=0.00000805
+2026-06-28 04:05:22 epoch=55/80 step=550/1275 loss=28.12287 lr=0.00000803
+2026-06-28 04:05:33 epoch=55/80 step=600/1275 loss=28.00363 lr=0.00000801
+2026-06-28 04:05:43 epoch=55/80 step=650/1275 loss=27.88592 lr=0.00000799
+2026-06-28 04:05:54 epoch=55/80 step=700/1275 loss=27.99337 lr=0.00000797
+2026-06-28 04:06:05 epoch=55/80 step=750/1275 loss=28.09261 lr=0.00000795
+2026-06-28 04:06:15 epoch=55/80 step=800/1275 loss=28.05921 lr=0.00000793
+2026-06-28 04:06:25 epoch=55/80 step=850/1275 loss=28.12165 lr=0.00000791
+2026-06-28 04:06:36 epoch=55/80 step=900/1275 loss=28.24311 lr=0.00000788
+2026-06-28 04:06:47 epoch=55/80 step=950/1275 loss=28.26791 lr=0.00000786
+2026-06-28 04:06:58 epoch=55/80 step=1000/1275 loss=28.28135 lr=0.00000784
+2026-06-28 04:07:09 epoch=55/80 step=1050/1275 loss=28.17790 lr=0.00000782
+2026-06-28 04:07:20 epoch=55/80 step=1100/1275 loss=28.26822 lr=0.00000780
+2026-06-28 04:07:30 epoch=55/80 step=1150/1275 loss=28.21241 lr=0.00000778
+2026-06-28 04:07:41 epoch=55/80 step=1200/1275 loss=28.17298 lr=0.00000776
+2026-06-28 04:07:52 epoch=55/80 step=1250/1275 loss=28.10367 lr=0.00000774
+2026-06-28 04:08:08 mask2former val mIoU=0.55201 background=0.60432 live_coral=0.68392 dead_coral=0.41833 bleached_coral=0.50147
+2026-06-28 04:08:19 epoch=56/80 step=50/1275 loss=26.36524 lr=0.00000770
+2026-06-28 04:08:29 epoch=56/80 step=100/1275 loss=27.00452 lr=0.00000768
+2026-06-28 04:08:39 epoch=56/80 step=150/1275 loss=26.96178 lr=0.00000766
+2026-06-28 04:08:49 epoch=56/80 step=200/1275 loss=27.13569 lr=0.00000764
+2026-06-28 04:08:59 epoch=56/80 step=250/1275 loss=27.67127 lr=0.00000762
+2026-06-28 04:09:09 epoch=56/80 step=300/1275 loss=27.72841 lr=0.00000760
+2026-06-28 04:09:20 epoch=56/80 step=350/1275 loss=27.66007 lr=0.00000758
+2026-06-28 04:09:31 epoch=56/80 step=400/1275 loss=27.54193 lr=0.00000756
+2026-06-28 04:09:41 epoch=56/80 step=450/1275 loss=28.04706 lr=0.00000754
+2026-06-28 04:09:52 epoch=56/80 step=500/1275 loss=28.35476 lr=0.00000751
+2026-06-28 04:10:03 epoch=56/80 step=550/1275 loss=28.66493 lr=0.00000749
+2026-06-28 04:10:14 epoch=56/80 step=600/1275 loss=28.54597 lr=0.00000747
+2026-06-28 04:10:24 epoch=56/80 step=650/1275 loss=28.66022 lr=0.00000745
+2026-06-28 04:10:34 epoch=56/80 step=700/1275 loss=28.58212 lr=0.00000743
+2026-06-28 04:10:45 epoch=56/80 step=750/1275 loss=28.61778 lr=0.00000741
+2026-06-28 04:10:56 epoch=56/80 step=800/1275 loss=28.49202 lr=0.00000739
+2026-06-28 04:11:06 epoch=56/80 step=850/1275 loss=28.44916 lr=0.00000737
+2026-06-28 04:11:16 epoch=56/80 step=900/1275 loss=28.43368 lr=0.00000735
+2026-06-28 04:11:26 epoch=56/80 step=950/1275 loss=28.42642 lr=0.00000733
+2026-06-28 04:11:37 epoch=56/80 step=1000/1275 loss=28.48302 lr=0.00000731
+2026-06-28 04:11:47 epoch=56/80 step=1050/1275 loss=28.50116 lr=0.00000729
+2026-06-28 04:11:57 epoch=56/80 step=1100/1275 loss=28.47215 lr=0.00000727
+2026-06-28 04:12:07 epoch=56/80 step=1150/1275 loss=28.40379 lr=0.00000724
+2026-06-28 04:12:17 epoch=56/80 step=1200/1275 loss=28.35645 lr=0.00000722
+2026-06-28 04:12:27 epoch=56/80 step=1250/1275 loss=28.29399 lr=0.00000720
+2026-06-28 04:12:44 mask2former val mIoU=0.55229 background=0.62277 live_coral=0.68418 dead_coral=0.41488 bleached_coral=0.48732
+2026-06-28 04:12:54 epoch=57/80 step=50/1275 loss=26.65041 lr=0.00000717
+2026-06-28 04:13:04 epoch=57/80 step=100/1275 loss=27.35716 lr=0.00000715
+2026-06-28 04:13:15 epoch=57/80 step=150/1275 loss=26.70864 lr=0.00000713
+2026-06-28 04:13:25 epoch=57/80 step=200/1275 loss=27.03119 lr=0.00000711
+2026-06-28 04:13:36 epoch=57/80 step=250/1275 loss=27.32717 lr=0.00000709
+2026-06-28 04:13:46 epoch=57/80 step=300/1275 loss=27.23208 lr=0.00000707
+2026-06-28 04:13:57 epoch=57/80 step=350/1275 loss=27.55901 lr=0.00000705
+2026-06-28 04:14:07 epoch=57/80 step=400/1275 loss=27.63889 lr=0.00000703
+2026-06-28 04:14:17 epoch=57/80 step=450/1275 loss=27.79080 lr=0.00000701
+2026-06-28 04:14:27 epoch=57/80 step=500/1275 loss=27.76977 lr=0.00000699
+2026-06-28 04:14:38 epoch=57/80 step=550/1275 loss=27.85545 lr=0.00000697
+2026-06-28 04:14:48 epoch=57/80 step=600/1275 loss=27.89271 lr=0.00000695
+2026-06-28 04:14:57 epoch=57/80 step=650/1275 loss=27.90369 lr=0.00000693
+2026-06-28 04:15:07 epoch=57/80 step=700/1275 loss=28.08904 lr=0.00000691
+2026-06-28 04:15:17 epoch=57/80 step=750/1275 loss=28.18086 lr=0.00000689
+2026-06-28 04:15:28 epoch=57/80 step=800/1275 loss=28.04349 lr=0.00000687
+2026-06-28 04:15:38 epoch=57/80 step=850/1275 loss=28.01515 lr=0.00000685
+2026-06-28 04:15:49 epoch=57/80 step=900/1275 loss=28.00169 lr=0.00000683
+2026-06-28 04:16:00 epoch=57/80 step=950/1275 loss=27.92388 lr=0.00000681
+2026-06-28 04:16:10 epoch=57/80 step=1000/1275 loss=27.88886 lr=0.00000679
+2026-06-28 04:16:20 epoch=57/80 step=1050/1275 loss=27.84305 lr=0.00000677
+2026-06-28 04:16:31 epoch=57/80 step=1100/1275 loss=27.86832 lr=0.00000675
+2026-06-28 04:16:41 epoch=57/80 step=1150/1275 loss=27.93950 lr=0.00000673
+2026-06-28 04:16:52 epoch=57/80 step=1200/1275 loss=27.90628 lr=0.00000670
+2026-06-28 04:17:02 epoch=57/80 step=1250/1275 loss=27.91326 lr=0.00000668
+2026-06-28 04:17:19 mask2former val mIoU=0.56072 background=0.61992 live_coral=0.70175 dead_coral=0.42369 bleached_coral=0.49752
+2026-06-28 04:17:29 epoch=58/80 step=50/1275 loss=25.70086 lr=0.00000665
+2026-06-28 04:17:39 epoch=58/80 step=100/1275 loss=27.06925 lr=0.00000663
+2026-06-28 04:17:50 epoch=58/80 step=150/1275 loss=26.67888 lr=0.00000661
+2026-06-28 04:18:01 epoch=58/80 step=200/1275 loss=27.04845 lr=0.00000659
+2026-06-28 04:18:11 epoch=58/80 step=250/1275 loss=26.94693 lr=0.00000658
+2026-06-28 04:18:22 epoch=58/80 step=300/1275 loss=27.04276 lr=0.00000656
+2026-06-28 04:18:33 epoch=58/80 step=350/1275 loss=26.85553 lr=0.00000654
+2026-06-28 04:18:44 epoch=58/80 step=400/1275 loss=27.01211 lr=0.00000652
+2026-06-28 04:18:55 epoch=58/80 step=450/1275 loss=26.96347 lr=0.00000650
+2026-06-28 04:19:05 epoch=58/80 step=500/1275 loss=27.33580 lr=0.00000648
+2026-06-28 04:19:15 epoch=58/80 step=550/1275 loss=27.64871 lr=0.00000646
+2026-06-28 04:19:24 epoch=58/80 step=600/1275 loss=27.76233 lr=0.00000644
+2026-06-28 04:19:33 epoch=58/80 step=650/1275 loss=27.71026 lr=0.00000642
+2026-06-28 04:19:43 epoch=58/80 step=700/1275 loss=27.78368 lr=0.00000640
+2026-06-28 04:19:54 epoch=58/80 step=750/1275 loss=27.86461 lr=0.00000638
+2026-06-28 04:20:05 epoch=58/80 step=800/1275 loss=27.66014 lr=0.00000636
+2026-06-28 04:20:15 epoch=58/80 step=850/1275 loss=27.62852 lr=0.00000634
+2026-06-28 04:20:25 epoch=58/80 step=900/1275 loss=27.72675 lr=0.00000632
+2026-06-28 04:20:36 epoch=58/80 step=950/1275 loss=27.83170 lr=0.00000630
+2026-06-28 04:20:48 epoch=58/80 step=1000/1275 loss=27.92708 lr=0.00000628
+2026-06-28 04:20:59 epoch=58/80 step=1050/1275 loss=27.93996 lr=0.00000626
+2026-06-28 04:21:09 epoch=58/80 step=1100/1275 loss=27.89198 lr=0.00000624
+2026-06-28 04:21:20 epoch=58/80 step=1150/1275 loss=27.81003 lr=0.00000622
+2026-06-28 04:21:31 epoch=58/80 step=1200/1275 loss=27.78502 lr=0.00000620
+2026-06-28 04:21:42 epoch=58/80 step=1250/1275 loss=27.76554 lr=0.00000618
+2026-06-28 04:21:59 mask2former val mIoU=0.55596 background=0.62184 live_coral=0.69299 dead_coral=0.43038 bleached_coral=0.47863
+2026-06-28 04:22:10 epoch=59/80 step=50/1275 loss=27.17803 lr=0.00000615
+2026-06-28 04:22:20 epoch=59/80 step=100/1275 loss=25.91735 lr=0.00000613
+2026-06-28 04:22:31 epoch=59/80 step=150/1275 loss=26.27047 lr=0.00000611
+2026-06-28 04:22:41 epoch=59/80 step=200/1275 loss=26.66386 lr=0.00000609
+2026-06-28 04:22:51 epoch=59/80 step=250/1275 loss=27.14304 lr=0.00000607
+2026-06-28 04:23:02 epoch=59/80 step=300/1275 loss=27.17607 lr=0.00000606
+2026-06-28 04:23:13 epoch=59/80 step=350/1275 loss=27.42428 lr=0.00000604
+2026-06-28 04:23:23 epoch=59/80 step=400/1275 loss=27.23395 lr=0.00000602
+2026-06-28 04:23:33 epoch=59/80 step=450/1275 loss=27.09179 lr=0.00000600
+2026-06-28 04:23:44 epoch=59/80 step=500/1275 loss=27.15406 lr=0.00000598
+2026-06-28 04:23:54 epoch=59/80 step=550/1275 loss=27.19233 lr=0.00000596
+2026-06-28 04:24:05 epoch=59/80 step=600/1275 loss=27.14983 lr=0.00000594
+2026-06-28 04:24:16 epoch=59/80 step=650/1275 loss=27.24001 lr=0.00000592
+2026-06-28 04:24:26 epoch=59/80 step=700/1275 loss=27.14951 lr=0.00000590
+2026-06-28 04:24:36 epoch=59/80 step=750/1275 loss=27.23510 lr=0.00000588
+2026-06-28 04:24:45 epoch=59/80 step=800/1275 loss=27.28292 lr=0.00000586
+2026-06-28 04:24:55 epoch=59/80 step=850/1275 loss=27.36585 lr=0.00000584
+2026-06-28 04:25:06 epoch=59/80 step=900/1275 loss=27.25058 lr=0.00000583
+2026-06-28 04:25:16 epoch=59/80 step=950/1275 loss=27.21273 lr=0.00000581
+2026-06-28 04:25:27 epoch=59/80 step=1000/1275 loss=27.23164 lr=0.00000579
+2026-06-28 04:25:38 epoch=59/80 step=1050/1275 loss=27.26956 lr=0.00000577
+2026-06-28 04:25:48 epoch=59/80 step=1100/1275 loss=27.39763 lr=0.00000575
+2026-06-28 04:25:58 epoch=59/80 step=1150/1275 loss=27.52495 lr=0.00000573
+2026-06-28 04:26:08 epoch=59/80 step=1200/1275 loss=27.45148 lr=0.00000571
+2026-06-28 04:26:18 epoch=59/80 step=1250/1275 loss=27.52440 lr=0.00000569
+2026-06-28 04:26:35 mask2former val mIoU=0.55851 background=0.62002 live_coral=0.68681 dead_coral=0.44335 bleached_coral=0.48387
+2026-06-28 04:26:45 epoch=60/80 step=50/1275 loss=27.93942 lr=0.00000567
+2026-06-28 04:26:55 epoch=60/80 step=100/1275 loss=27.85424 lr=0.00000565
+2026-06-28 04:27:05 epoch=60/80 step=150/1275 loss=28.12696 lr=0.00000563
+2026-06-28 04:27:15 epoch=60/80 step=200/1275 loss=28.04060 lr=0.00000561
+2026-06-28 04:27:24 epoch=60/80 step=250/1275 loss=28.06073 lr=0.00000559
+2026-06-28 04:27:35 epoch=60/80 step=300/1275 loss=28.23069 lr=0.00000557
+2026-06-28 04:27:46 epoch=60/80 step=350/1275 loss=27.85976 lr=0.00000555
+2026-06-28 04:27:56 epoch=60/80 step=400/1275 loss=27.54279 lr=0.00000553
+2026-06-28 04:28:07 epoch=60/80 step=450/1275 loss=27.45099 lr=0.00000552
+2026-06-28 04:28:18 epoch=60/80 step=500/1275 loss=27.40012 lr=0.00000550
+2026-06-28 04:28:28 epoch=60/80 step=550/1275 loss=27.51362 lr=0.00000548
+2026-06-28 04:28:40 epoch=60/80 step=600/1275 loss=27.39356 lr=0.00000546
+2026-06-28 04:28:51 epoch=60/80 step=650/1275 loss=27.23596 lr=0.00000544
+2026-06-28 04:29:01 epoch=60/80 step=700/1275 loss=27.14714 lr=0.00000542
+2026-06-28 04:29:10 epoch=60/80 step=750/1275 loss=27.10022 lr=0.00000541
+2026-06-28 04:29:20 epoch=60/80 step=800/1275 loss=27.21388 lr=0.00000539
+2026-06-28 04:29:30 epoch=60/80 step=850/1275 loss=27.17162 lr=0.00000537
+2026-06-28 04:29:40 epoch=60/80 step=900/1275 loss=27.18340 lr=0.00000535
+2026-06-28 04:29:51 epoch=60/80 step=950/1275 loss=27.13028 lr=0.00000533
+2026-06-28 04:30:01 epoch=60/80 step=1000/1275 loss=27.14518 lr=0.00000531
+2026-06-28 04:30:11 epoch=60/80 step=1050/1275 loss=27.11308 lr=0.00000530
+2026-06-28 04:30:21 epoch=60/80 step=1100/1275 loss=27.11056 lr=0.00000528
+2026-06-28 04:30:32 epoch=60/80 step=1150/1275 loss=27.06500 lr=0.00000526
+2026-06-28 04:30:42 epoch=60/80 step=1200/1275 loss=27.19207 lr=0.00000524
+2026-06-28 04:30:53 epoch=60/80 step=1250/1275 loss=27.17343 lr=0.00000522
+2026-06-28 04:31:10 mask2former val mIoU=0.55609 background=0.62096 live_coral=0.69015 dead_coral=0.42425 bleached_coral=0.48901
+2026-06-28 04:31:21 epoch=61/80 step=50/1275 loss=28.94748 lr=0.00000520
+2026-06-28 04:31:30 epoch=61/80 step=100/1275 loss=27.44210 lr=0.00000518
+2026-06-28 04:31:40 epoch=61/80 step=150/1275 loss=27.39492 lr=0.00000516
+2026-06-28 04:31:49 epoch=61/80 step=200/1275 loss=27.35898 lr=0.00000514
+2026-06-28 04:31:58 epoch=61/80 step=250/1275 loss=27.14698 lr=0.00000512
+2026-06-28 04:32:08 epoch=61/80 step=300/1275 loss=27.13178 lr=0.00000511
+2026-06-28 04:32:18 epoch=61/80 step=350/1275 loss=27.10150 lr=0.00000509
+2026-06-28 04:32:29 epoch=61/80 step=400/1275 loss=27.18451 lr=0.00000507
+2026-06-28 04:32:39 epoch=61/80 step=450/1275 loss=27.16406 lr=0.00000505
+2026-06-28 04:32:50 epoch=61/80 step=500/1275 loss=27.03374 lr=0.00000503
+2026-06-28 04:33:00 epoch=61/80 step=550/1275 loss=27.20088 lr=0.00000502
+2026-06-28 04:33:10 epoch=61/80 step=600/1275 loss=27.28974 lr=0.00000500
+2026-06-28 04:33:21 epoch=61/80 step=650/1275 loss=27.30420 lr=0.00000498
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+2026-06-28 04:33:43 epoch=61/80 step=750/1275 loss=27.16322 lr=0.00000494
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+2026-06-28 04:34:05 epoch=61/80 step=850/1275 loss=26.85749 lr=0.00000491
+2026-06-28 04:34:16 epoch=61/80 step=900/1275 loss=26.91987 lr=0.00000489
+2026-06-28 04:34:27 epoch=61/80 step=950/1275 loss=26.90087 lr=0.00000487
+2026-06-28 04:34:38 epoch=61/80 step=1000/1275 loss=26.88363 lr=0.00000486
+2026-06-28 04:34:49 epoch=61/80 step=1050/1275 loss=26.99723 lr=0.00000484
+2026-06-28 04:34:59 epoch=61/80 step=1100/1275 loss=27.04136 lr=0.00000482
+2026-06-28 04:35:10 epoch=61/80 step=1150/1275 loss=27.03647 lr=0.00000480
+2026-06-28 04:35:21 epoch=61/80 step=1200/1275 loss=27.03989 lr=0.00000479
+2026-06-28 04:35:32 epoch=61/80 step=1250/1275 loss=27.01875 lr=0.00000477
+2026-06-28 04:35:49 mask2former val mIoU=0.55662 background=0.63278 live_coral=0.68621 dead_coral=0.43957 bleached_coral=0.46789
+2026-06-28 04:36:00 epoch=62/80 step=50/1275 loss=26.93365 lr=0.00000474
+2026-06-28 04:36:12 epoch=62/80 step=100/1275 loss=26.18678 lr=0.00000473
+2026-06-28 04:36:23 epoch=62/80 step=150/1275 loss=27.89045 lr=0.00000471
+2026-06-28 04:36:34 epoch=62/80 step=200/1275 loss=26.97056 lr=0.00000469
+2026-06-28 04:36:45 epoch=62/80 step=250/1275 loss=27.34599 lr=0.00000467
+2026-06-28 04:36:56 epoch=62/80 step=300/1275 loss=27.16090 lr=0.00000466
+2026-06-28 04:37:07 epoch=62/80 step=350/1275 loss=27.42698 lr=0.00000464
+2026-06-28 04:37:18 epoch=62/80 step=400/1275 loss=27.44727 lr=0.00000462
+2026-06-28 04:37:28 epoch=62/80 step=450/1275 loss=27.43676 lr=0.00000460
+2026-06-28 04:37:38 epoch=62/80 step=500/1275 loss=27.44283 lr=0.00000459
+2026-06-28 04:37:49 epoch=62/80 step=550/1275 loss=27.30456 lr=0.00000457
+2026-06-28 04:38:00 epoch=62/80 step=600/1275 loss=27.17239 lr=0.00000455
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+2026-06-28 04:38:20 epoch=62/80 step=700/1275 loss=26.99229 lr=0.00000452
+2026-06-28 04:38:30 epoch=62/80 step=750/1275 loss=26.85081 lr=0.00000450
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+2026-06-28 04:38:50 epoch=62/80 step=850/1275 loss=26.97337 lr=0.00000447
+2026-06-28 04:39:01 epoch=62/80 step=900/1275 loss=26.83314 lr=0.00000445
+2026-06-28 04:39:11 epoch=62/80 step=950/1275 loss=26.87261 lr=0.00000443
+2026-06-28 04:39:22 epoch=62/80 step=1000/1275 loss=26.79403 lr=0.00000442
+2026-06-28 04:39:33 epoch=62/80 step=1050/1275 loss=26.86734 lr=0.00000440
+2026-06-28 04:39:44 epoch=62/80 step=1100/1275 loss=26.74839 lr=0.00000438
+2026-06-28 04:39:55 epoch=62/80 step=1150/1275 loss=26.70049 lr=0.00000437
+2026-06-28 04:40:06 epoch=62/80 step=1200/1275 loss=26.68551 lr=0.00000435
+2026-06-28 04:40:16 epoch=62/80 step=1250/1275 loss=26.62095 lr=0.00000433
+2026-06-28 04:40:33 mask2former val mIoU=0.55858 background=0.60815 live_coral=0.68439 dead_coral=0.45273 bleached_coral=0.48903
+2026-06-28 04:40:44 epoch=63/80 step=50/1275 loss=26.97521 lr=0.00000431
+2026-06-28 04:40:55 epoch=63/80 step=100/1275 loss=26.64323 lr=0.00000429
+2026-06-28 04:41:06 epoch=63/80 step=150/1275 loss=26.96990 lr=0.00000427
+2026-06-28 04:41:17 epoch=63/80 step=200/1275 loss=27.15813 lr=0.00000426
+2026-06-28 04:41:28 epoch=63/80 step=250/1275 loss=27.26712 lr=0.00000424
+2026-06-28 04:41:39 epoch=63/80 step=300/1275 loss=26.90543 lr=0.00000423
+2026-06-28 04:41:50 epoch=63/80 step=350/1275 loss=27.05783 lr=0.00000421
+2026-06-28 04:42:00 epoch=63/80 step=400/1275 loss=27.07824 lr=0.00000419
+2026-06-28 04:42:10 epoch=63/80 step=450/1275 loss=27.00202 lr=0.00000418
+2026-06-28 04:42:21 epoch=63/80 step=500/1275 loss=27.03145 lr=0.00000416
+2026-06-28 04:42:31 epoch=63/80 step=550/1275 loss=26.87487 lr=0.00000414
+2026-06-28 04:42:41 epoch=63/80 step=600/1275 loss=27.07218 lr=0.00000413
+2026-06-28 04:42:50 epoch=63/80 step=650/1275 loss=26.98517 lr=0.00000411
+2026-06-28 04:43:00 epoch=63/80 step=700/1275 loss=26.89698 lr=0.00000409
+2026-06-28 04:43:10 epoch=63/80 step=750/1275 loss=26.94067 lr=0.00000408
+2026-06-28 04:43:20 epoch=63/80 step=800/1275 loss=26.89788 lr=0.00000406
+2026-06-28 04:43:30 epoch=63/80 step=850/1275 loss=26.90855 lr=0.00000405
+2026-06-28 04:43:40 epoch=63/80 step=900/1275 loss=26.78674 lr=0.00000403
+2026-06-28 04:43:52 epoch=63/80 step=950/1275 loss=26.70027 lr=0.00000401
+2026-06-28 04:44:02 epoch=63/80 step=1000/1275 loss=26.64147 lr=0.00000400
+2026-06-28 04:44:12 epoch=63/80 step=1050/1275 loss=26.55560 lr=0.00000398
+2026-06-28 04:44:23 epoch=63/80 step=1100/1275 loss=26.56019 lr=0.00000396
+2026-06-28 04:44:34 epoch=63/80 step=1150/1275 loss=26.54804 lr=0.00000395
+2026-06-28 04:44:44 epoch=63/80 step=1200/1275 loss=26.60259 lr=0.00000393
+2026-06-28 04:44:55 epoch=63/80 step=1250/1275 loss=26.61977 lr=0.00000392
+2026-06-28 04:45:11 mask2former val mIoU=0.56084 background=0.61624 live_coral=0.68191 dead_coral=0.45542 bleached_coral=0.48979
+2026-06-28 04:45:22 epoch=64/80 step=50/1275 loss=26.01999 lr=0.00000389
+2026-06-28 04:45:32 epoch=64/80 step=100/1275 loss=27.01622 lr=0.00000388
+2026-06-28 04:45:42 epoch=64/80 step=150/1275 loss=26.20276 lr=0.00000386
+2026-06-28 04:45:52 epoch=64/80 step=200/1275 loss=26.24779 lr=0.00000385
+2026-06-28 04:46:02 epoch=64/80 step=250/1275 loss=26.31118 lr=0.00000383
+2026-06-28 04:46:12 epoch=64/80 step=300/1275 loss=26.08410 lr=0.00000381
+2026-06-28 04:46:22 epoch=64/80 step=350/1275 loss=26.34962 lr=0.00000380
+2026-06-28 04:46:33 epoch=64/80 step=400/1275 loss=26.27457 lr=0.00000378
+2026-06-28 04:46:43 epoch=64/80 step=450/1275 loss=26.34767 lr=0.00000377
+2026-06-28 04:46:53 epoch=64/80 step=500/1275 loss=26.55370 lr=0.00000375
+2026-06-28 04:47:04 epoch=64/80 step=550/1275 loss=26.53244 lr=0.00000374
+2026-06-28 04:47:15 epoch=64/80 step=600/1275 loss=26.56384 lr=0.00000372
+2026-06-28 04:47:25 epoch=64/80 step=650/1275 loss=26.71014 lr=0.00000370
+2026-06-28 04:47:35 epoch=64/80 step=700/1275 loss=26.58302 lr=0.00000369
+2026-06-28 04:47:46 epoch=64/80 step=750/1275 loss=26.71074 lr=0.00000367
+2026-06-28 04:47:57 epoch=64/80 step=800/1275 loss=26.71590 lr=0.00000366
+2026-06-28 04:48:08 epoch=64/80 step=850/1275 loss=26.70431 lr=0.00000364
+2026-06-28 04:48:18 epoch=64/80 step=900/1275 loss=26.65803 lr=0.00000363
+2026-06-28 04:48:29 epoch=64/80 step=950/1275 loss=26.66489 lr=0.00000361
+2026-06-28 04:48:40 epoch=64/80 step=1000/1275 loss=26.71045 lr=0.00000360
+2026-06-28 04:48:51 epoch=64/80 step=1050/1275 loss=26.74815 lr=0.00000358
+2026-06-28 04:49:02 epoch=64/80 step=1100/1275 loss=26.75446 lr=0.00000357
+2026-06-28 04:49:13 epoch=64/80 step=1150/1275 loss=26.73173 lr=0.00000355
+2026-06-28 04:49:24 epoch=64/80 step=1200/1275 loss=26.78382 lr=0.00000353
+2026-06-28 04:49:35 epoch=64/80 step=1250/1275 loss=26.69306 lr=0.00000352
+2026-06-28 04:49:52 mask2former val mIoU=0.55838 background=0.63848 live_coral=0.68966 dead_coral=0.41970 bleached_coral=0.48568
+2026-06-28 04:50:03 epoch=65/80 step=50/1275 loss=26.13161 lr=0.00000350
+2026-06-28 04:50:14 epoch=65/80 step=100/1275 loss=26.14772 lr=0.00000348
+2026-06-28 04:50:25 epoch=65/80 step=150/1275 loss=25.82311 lr=0.00000347
+2026-06-28 04:50:36 epoch=65/80 step=200/1275 loss=25.87998 lr=0.00000345
+2026-06-28 04:50:46 epoch=65/80 step=250/1275 loss=25.89756 lr=0.00000344
+2026-06-28 04:50:57 epoch=65/80 step=300/1275 loss=26.28227 lr=0.00000342
+2026-06-28 04:51:08 epoch=65/80 step=350/1275 loss=26.60477 lr=0.00000341
+2026-06-28 04:51:17 epoch=65/80 step=400/1275 loss=26.51379 lr=0.00000339
+2026-06-28 04:51:27 epoch=65/80 step=450/1275 loss=26.59052 lr=0.00000338
+2026-06-28 04:51:39 epoch=65/80 step=500/1275 loss=26.90203 lr=0.00000336
+2026-06-28 04:51:49 epoch=65/80 step=550/1275 loss=26.69380 lr=0.00000335
+2026-06-28 04:51:59 epoch=65/80 step=600/1275 loss=26.55802 lr=0.00000333
+2026-06-28 04:52:10 epoch=65/80 step=650/1275 loss=26.64386 lr=0.00000332
+2026-06-28 04:52:21 epoch=65/80 step=700/1275 loss=26.54805 lr=0.00000330
+2026-06-28 04:52:31 epoch=65/80 step=750/1275 loss=26.36398 lr=0.00000329
+2026-06-28 04:52:42 epoch=65/80 step=800/1275 loss=26.23903 lr=0.00000327
+2026-06-28 04:52:51 epoch=65/80 step=850/1275 loss=26.19763 lr=0.00000326
+2026-06-28 04:53:01 epoch=65/80 step=900/1275 loss=26.18926 lr=0.00000324
+2026-06-28 04:53:11 epoch=65/80 step=950/1275 loss=26.12405 lr=0.00000323
+2026-06-28 04:53:21 epoch=65/80 step=1000/1275 loss=26.13948 lr=0.00000322
+2026-06-28 04:53:32 epoch=65/80 step=1050/1275 loss=26.16774 lr=0.00000320
+2026-06-28 04:53:43 epoch=65/80 step=1100/1275 loss=26.25694 lr=0.00000319
+2026-06-28 04:53:53 epoch=65/80 step=1150/1275 loss=26.28835 lr=0.00000317
+2026-06-28 04:54:04 epoch=65/80 step=1200/1275 loss=26.25389 lr=0.00000316
+2026-06-28 04:54:15 epoch=65/80 step=1250/1275 loss=26.22393 lr=0.00000314
+2026-06-28 04:54:33 mask2former val mIoU=0.55479 background=0.61899 live_coral=0.68126 dead_coral=0.43230 bleached_coral=0.48659
+2026-06-28 04:54:45 epoch=66/80 step=50/1275 loss=26.13368 lr=0.00000312
+2026-06-28 04:54:55 epoch=66/80 step=100/1275 loss=24.46755 lr=0.00000311
+2026-06-28 04:55:05 epoch=66/80 step=150/1275 loss=25.58198 lr=0.00000309
+2026-06-28 04:55:15 epoch=66/80 step=200/1275 loss=25.53457 lr=0.00000308
+2026-06-28 04:55:25 epoch=66/80 step=250/1275 loss=25.56038 lr=0.00000306
+2026-06-28 04:55:36 epoch=66/80 step=300/1275 loss=25.83414 lr=0.00000305
+2026-06-28 04:55:47 epoch=66/80 step=350/1275 loss=25.91262 lr=0.00000304
+2026-06-28 04:55:58 epoch=66/80 step=400/1275 loss=26.20054 lr=0.00000302
+2026-06-28 04:56:07 epoch=66/80 step=450/1275 loss=26.44952 lr=0.00000301
+2026-06-28 04:56:18 epoch=66/80 step=500/1275 loss=26.14906 lr=0.00000299
+2026-06-28 04:56:28 epoch=66/80 step=550/1275 loss=26.25921 lr=0.00000298
+2026-06-28 04:56:38 epoch=66/80 step=600/1275 loss=26.20368 lr=0.00000297
+2026-06-28 04:56:50 epoch=66/80 step=650/1275 loss=26.28950 lr=0.00000295
+2026-06-28 04:57:01 epoch=66/80 step=700/1275 loss=26.08211 lr=0.00000294
+2026-06-28 04:57:11 epoch=66/80 step=750/1275 loss=26.00833 lr=0.00000292
+2026-06-28 04:57:22 epoch=66/80 step=800/1275 loss=25.93040 lr=0.00000291
+2026-06-28 04:57:32 epoch=66/80 step=850/1275 loss=26.03402 lr=0.00000290
+2026-06-28 04:57:43 epoch=66/80 step=900/1275 loss=25.92928 lr=0.00000288
+2026-06-28 04:57:54 epoch=66/80 step=950/1275 loss=26.00641 lr=0.00000287
+2026-06-28 04:58:05 epoch=66/80 step=1000/1275 loss=26.02331 lr=0.00000286
+2026-06-28 04:58:16 epoch=66/80 step=1050/1275 loss=26.05733 lr=0.00000284
+2026-06-28 04:58:27 epoch=66/80 step=1100/1275 loss=25.97913 lr=0.00000283
+2026-06-28 04:58:37 epoch=66/80 step=1150/1275 loss=25.97675 lr=0.00000281
+2026-06-28 04:58:48 epoch=66/80 step=1200/1275 loss=25.96045 lr=0.00000280
+2026-06-28 04:58:59 epoch=66/80 step=1250/1275 loss=25.96287 lr=0.00000279
+2026-06-28 04:59:16 mask2former val mIoU=0.56540 background=0.62830 live_coral=0.69169 dead_coral=0.45614 bleached_coral=0.48549
+2026-06-28 04:59:18 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 04:59:29 epoch=67/80 step=50/1275 loss=26.83525 lr=0.00000277
+2026-06-28 04:59:40 epoch=67/80 step=100/1275 loss=27.23552 lr=0.00000275
+2026-06-28 04:59:51 epoch=67/80 step=150/1275 loss=27.09880 lr=0.00000274
+2026-06-28 05:00:02 epoch=67/80 step=200/1275 loss=26.85562 lr=0.00000273
+2026-06-28 05:00:13 epoch=67/80 step=250/1275 loss=27.03487 lr=0.00000271
+2026-06-28 05:00:23 epoch=67/80 step=300/1275 loss=26.94850 lr=0.00000270
+2026-06-28 05:00:34 epoch=67/80 step=350/1275 loss=26.92718 lr=0.00000269
+2026-06-28 05:00:44 epoch=67/80 step=400/1275 loss=26.81934 lr=0.00000267
+2026-06-28 05:00:54 epoch=67/80 step=450/1275 loss=26.83823 lr=0.00000266
+2026-06-28 05:01:04 epoch=67/80 step=500/1275 loss=26.75040 lr=0.00000265
+2026-06-28 05:01:14 epoch=67/80 step=550/1275 loss=26.82045 lr=0.00000263
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+2026-06-28 05:01:35 epoch=67/80 step=650/1275 loss=26.75462 lr=0.00000261
+2026-06-28 05:01:46 epoch=67/80 step=700/1275 loss=26.85556 lr=0.00000260
+2026-06-28 05:01:57 epoch=67/80 step=750/1275 loss=26.79286 lr=0.00000258
+2026-06-28 05:02:08 epoch=67/80 step=800/1275 loss=26.59744 lr=0.00000257
+2026-06-28 05:02:19 epoch=67/80 step=850/1275 loss=26.59332 lr=0.00000256
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+2026-06-28 05:02:40 epoch=67/80 step=950/1275 loss=26.31167 lr=0.00000253
+2026-06-28 05:02:50 epoch=67/80 step=1000/1275 loss=26.27792 lr=0.00000252
+2026-06-28 05:03:00 epoch=67/80 step=1050/1275 loss=26.24226 lr=0.00000251
+2026-06-28 05:03:11 epoch=67/80 step=1100/1275 loss=26.29022 lr=0.00000249
+2026-06-28 05:03:22 epoch=67/80 step=1150/1275 loss=26.29073 lr=0.00000248
+2026-06-28 05:03:32 epoch=67/80 step=1200/1275 loss=26.31635 lr=0.00000247
+2026-06-28 05:03:43 epoch=67/80 step=1250/1275 loss=26.28966 lr=0.00000245
+2026-06-28 05:04:00 mask2former val mIoU=0.56487 background=0.62529 live_coral=0.68738 dead_coral=0.43431 bleached_coral=0.51248
+2026-06-28 05:04:12 epoch=68/80 step=50/1275 loss=25.14435 lr=0.00000244
+2026-06-28 05:04:22 epoch=68/80 step=100/1275 loss=26.41532 lr=0.00000242
+2026-06-28 05:04:32 epoch=68/80 step=150/1275 loss=26.70888 lr=0.00000241
+2026-06-28 05:04:43 epoch=68/80 step=200/1275 loss=26.82261 lr=0.00000240
+2026-06-28 05:04:53 epoch=68/80 step=250/1275 loss=26.64058 lr=0.00000239
+2026-06-28 05:05:03 epoch=68/80 step=300/1275 loss=26.60632 lr=0.00000237
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+2026-06-28 05:05:25 epoch=68/80 step=400/1275 loss=26.67662 lr=0.00000235
+2026-06-28 05:05:36 epoch=68/80 step=450/1275 loss=26.57179 lr=0.00000234
+2026-06-28 05:05:46 epoch=68/80 step=500/1275 loss=26.55049 lr=0.00000232
+2026-06-28 05:05:56 epoch=68/80 step=550/1275 loss=26.33256 lr=0.00000231
+2026-06-28 05:06:06 epoch=68/80 step=600/1275 loss=26.37984 lr=0.00000230
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+2026-06-28 05:06:27 epoch=68/80 step=700/1275 loss=26.50913 lr=0.00000227
+2026-06-28 05:06:37 epoch=68/80 step=750/1275 loss=26.40200 lr=0.00000226
+2026-06-28 05:06:47 epoch=68/80 step=800/1275 loss=26.47281 lr=0.00000225
+2026-06-28 05:06:57 epoch=68/80 step=850/1275 loss=26.43313 lr=0.00000224
+2026-06-28 05:07:08 epoch=68/80 step=900/1275 loss=26.42412 lr=0.00000223
+2026-06-28 05:07:18 epoch=68/80 step=950/1275 loss=26.38797 lr=0.00000221
+2026-06-28 05:07:28 epoch=68/80 step=1000/1275 loss=26.27798 lr=0.00000220
+2026-06-28 05:07:38 epoch=68/80 step=1050/1275 loss=26.30017 lr=0.00000219
+2026-06-28 05:07:49 epoch=68/80 step=1100/1275 loss=26.29929 lr=0.00000218
+2026-06-28 05:07:59 epoch=68/80 step=1150/1275 loss=26.34521 lr=0.00000217
+2026-06-28 05:08:09 epoch=68/80 step=1200/1275 loss=26.23392 lr=0.00000215
+2026-06-28 05:08:19 epoch=68/80 step=1250/1275 loss=26.25264 lr=0.00000214
+2026-06-28 05:08:35 mask2former val mIoU=0.55594 background=0.62100 live_coral=0.68660 dead_coral=0.42183 bleached_coral=0.49432
+2026-06-28 05:08:46 epoch=69/80 step=50/1275 loss=25.93621 lr=0.00000213
+2026-06-28 05:08:56 epoch=69/80 step=100/1275 loss=26.58946 lr=0.00000211
+2026-06-28 05:09:06 epoch=69/80 step=150/1275 loss=26.18641 lr=0.00000210
+2026-06-28 05:09:15 epoch=69/80 step=200/1275 loss=26.06404 lr=0.00000209
+2026-06-28 05:09:25 epoch=69/80 step=250/1275 loss=25.96299 lr=0.00000208
+2026-06-28 05:09:36 epoch=69/80 step=300/1275 loss=25.96714 lr=0.00000207
+2026-06-28 05:09:47 epoch=69/80 step=350/1275 loss=25.94576 lr=0.00000206
+2026-06-28 05:09:57 epoch=69/80 step=400/1275 loss=25.65665 lr=0.00000204
+2026-06-28 05:10:09 epoch=69/80 step=450/1275 loss=25.74706 lr=0.00000203
+2026-06-28 05:10:19 epoch=69/80 step=500/1275 loss=25.71918 lr=0.00000202
+2026-06-28 05:10:30 epoch=69/80 step=550/1275 loss=25.72019 lr=0.00000201
+2026-06-28 05:10:41 epoch=69/80 step=600/1275 loss=25.88428 lr=0.00000200
+2026-06-28 05:10:52 epoch=69/80 step=650/1275 loss=25.97260 lr=0.00000199
+2026-06-28 05:11:03 epoch=69/80 step=700/1275 loss=26.06515 lr=0.00000198
+2026-06-28 05:11:13 epoch=69/80 step=750/1275 loss=26.13268 lr=0.00000196
+2026-06-28 05:11:24 epoch=69/80 step=800/1275 loss=26.00141 lr=0.00000195
+2026-06-28 05:11:34 epoch=69/80 step=850/1275 loss=25.97040 lr=0.00000194
+2026-06-28 05:11:45 epoch=69/80 step=900/1275 loss=25.86967 lr=0.00000193
+2026-06-28 05:11:56 epoch=69/80 step=950/1275 loss=25.77311 lr=0.00000192
+2026-06-28 05:12:06 epoch=69/80 step=1000/1275 loss=25.75587 lr=0.00000191
+2026-06-28 05:12:16 epoch=69/80 step=1050/1275 loss=25.71259 lr=0.00000190
+2026-06-28 05:12:27 epoch=69/80 step=1100/1275 loss=25.74764 lr=0.00000189
+2026-06-28 05:12:37 epoch=69/80 step=1150/1275 loss=25.77619 lr=0.00000188
+2026-06-28 05:12:47 epoch=69/80 step=1200/1275 loss=25.80424 lr=0.00000186
+2026-06-28 05:12:57 epoch=69/80 step=1250/1275 loss=25.85737 lr=0.00000185
+2026-06-28 05:13:14 mask2former val mIoU=0.56161 background=0.62219 live_coral=0.69747 dead_coral=0.43883 bleached_coral=0.48796
+2026-06-28 05:13:24 epoch=70/80 step=50/1275 loss=27.52465 lr=0.00000184
+2026-06-28 05:13:34 epoch=70/80 step=100/1275 loss=27.42632 lr=0.00000183
+2026-06-28 05:13:44 epoch=70/80 step=150/1275 loss=26.58751 lr=0.00000182
+2026-06-28 05:13:54 epoch=70/80 step=200/1275 loss=26.53255 lr=0.00000181
+2026-06-28 05:14:04 epoch=70/80 step=250/1275 loss=26.24541 lr=0.00000179
+2026-06-28 05:14:14 epoch=70/80 step=300/1275 loss=25.99818 lr=0.00000178
+2026-06-28 05:14:24 epoch=70/80 step=350/1275 loss=25.84208 lr=0.00000177
+2026-06-28 05:14:35 epoch=70/80 step=400/1275 loss=25.85336 lr=0.00000176
+2026-06-28 05:14:45 epoch=70/80 step=450/1275 loss=26.10499 lr=0.00000175
+2026-06-28 05:14:55 epoch=70/80 step=500/1275 loss=25.97469 lr=0.00000174
+2026-06-28 05:15:06 epoch=70/80 step=550/1275 loss=25.72734 lr=0.00000173
+2026-06-28 05:15:16 epoch=70/80 step=600/1275 loss=25.79572 lr=0.00000172
+2026-06-28 05:15:26 epoch=70/80 step=650/1275 loss=25.70022 lr=0.00000171
+2026-06-28 05:15:37 epoch=70/80 step=700/1275 loss=25.71203 lr=0.00000170
+2026-06-28 05:15:47 epoch=70/80 step=750/1275 loss=25.58746 lr=0.00000169
+2026-06-28 05:15:57 epoch=70/80 step=800/1275 loss=25.51510 lr=0.00000168
+2026-06-28 05:16:06 epoch=70/80 step=850/1275 loss=25.65573 lr=0.00000167
+2026-06-28 05:16:17 epoch=70/80 step=900/1275 loss=25.64346 lr=0.00000166
+2026-06-28 05:16:27 epoch=70/80 step=950/1275 loss=25.79502 lr=0.00000165
+2026-06-28 05:16:36 epoch=70/80 step=1000/1275 loss=25.78711 lr=0.00000164
+2026-06-28 05:16:48 epoch=70/80 step=1050/1275 loss=25.90108 lr=0.00000163
+2026-06-28 05:16:58 epoch=70/80 step=1100/1275 loss=25.89897 lr=0.00000162
+2026-06-28 05:17:09 epoch=70/80 step=1150/1275 loss=25.84459 lr=0.00000161
+2026-06-28 05:17:20 epoch=70/80 step=1200/1275 loss=25.79378 lr=0.00000160
+2026-06-28 05:17:31 epoch=70/80 step=1250/1275 loss=25.82293 lr=0.00000159
+2026-06-28 05:17:49 mask2former val mIoU=0.56317 background=0.62443 live_coral=0.68493 dead_coral=0.45701 bleached_coral=0.48632
+2026-06-28 05:18:00 epoch=71/80 step=50/1275 loss=23.50296 lr=0.00000157
+2026-06-28 05:18:10 epoch=71/80 step=100/1275 loss=25.37713 lr=0.00000156
+2026-06-28 05:18:20 epoch=71/80 step=150/1275 loss=25.76420 lr=0.00000155
+2026-06-28 05:18:31 epoch=71/80 step=200/1275 loss=25.56243 lr=0.00000154
+2026-06-28 05:18:41 epoch=71/80 step=250/1275 loss=25.52933 lr=0.00000153
+2026-06-28 05:18:51 epoch=71/80 step=300/1275 loss=25.21607 lr=0.00000152
+2026-06-28 05:19:01 epoch=71/80 step=350/1275 loss=25.47605 lr=0.00000152
+2026-06-28 05:19:11 epoch=71/80 step=400/1275 loss=25.60967 lr=0.00000151
+2026-06-28 05:19:21 epoch=71/80 step=450/1275 loss=25.78084 lr=0.00000150
+2026-06-28 05:19:32 epoch=71/80 step=500/1275 loss=25.87081 lr=0.00000149
+2026-06-28 05:19:42 epoch=71/80 step=550/1275 loss=25.73369 lr=0.00000148
+2026-06-28 05:19:52 epoch=71/80 step=600/1275 loss=25.70449 lr=0.00000147
+2026-06-28 05:20:02 epoch=71/80 step=650/1275 loss=25.67429 lr=0.00000146
+2026-06-28 05:20:13 epoch=71/80 step=700/1275 loss=25.88127 lr=0.00000145
+2026-06-28 05:20:23 epoch=71/80 step=750/1275 loss=25.96937 lr=0.00000144
+2026-06-28 05:20:34 epoch=71/80 step=800/1275 loss=25.98941 lr=0.00000143
+2026-06-28 05:20:44 epoch=71/80 step=850/1275 loss=25.97773 lr=0.00000142
+2026-06-28 05:20:54 epoch=71/80 step=900/1275 loss=25.95736 lr=0.00000141
+2026-06-28 05:21:04 epoch=71/80 step=950/1275 loss=25.94483 lr=0.00000140
+2026-06-28 05:21:15 epoch=71/80 step=1000/1275 loss=26.02217 lr=0.00000139
+2026-06-28 05:21:25 epoch=71/80 step=1050/1275 loss=26.05067 lr=0.00000138
+2026-06-28 05:21:35 epoch=71/80 step=1100/1275 loss=25.88876 lr=0.00000137
+2026-06-28 05:21:45 epoch=71/80 step=1150/1275 loss=25.86860 lr=0.00000137
+2026-06-28 05:21:55 epoch=71/80 step=1200/1275 loss=25.77075 lr=0.00000136
+2026-06-28 05:22:04 epoch=71/80 step=1250/1275 loss=25.81550 lr=0.00000135
+2026-06-28 05:22:20 mask2former val mIoU=0.55652 background=0.62318 live_coral=0.68413 dead_coral=0.43642 bleached_coral=0.48233
+2026-06-28 05:22:31 epoch=72/80 step=50/1275 loss=24.71575 lr=0.00000133
+2026-06-28 05:22:41 epoch=72/80 step=100/1275 loss=26.58570 lr=0.00000132
+2026-06-28 05:22:52 epoch=72/80 step=150/1275 loss=25.68381 lr=0.00000132
+2026-06-28 05:23:02 epoch=72/80 step=200/1275 loss=25.72127 lr=0.00000131
+2026-06-28 05:23:13 epoch=72/80 step=250/1275 loss=25.57525 lr=0.00000130
+2026-06-28 05:23:24 epoch=72/80 step=300/1275 loss=25.28072 lr=0.00000129
+2026-06-28 05:23:34 epoch=72/80 step=350/1275 loss=25.34542 lr=0.00000128
+2026-06-28 05:23:43 epoch=72/80 step=400/1275 loss=25.32486 lr=0.00000127
+2026-06-28 05:23:53 epoch=72/80 step=450/1275 loss=25.38439 lr=0.00000126
+2026-06-28 05:24:03 epoch=72/80 step=500/1275 loss=25.29364 lr=0.00000125
+2026-06-28 05:24:13 epoch=72/80 step=550/1275 loss=25.41817 lr=0.00000125
+2026-06-28 05:24:23 epoch=72/80 step=600/1275 loss=25.36339 lr=0.00000124
+2026-06-28 05:24:34 epoch=72/80 step=650/1275 loss=25.51804 lr=0.00000123
+2026-06-28 05:24:43 epoch=72/80 step=700/1275 loss=25.62440 lr=0.00000122
+2026-06-28 05:24:54 epoch=72/80 step=750/1275 loss=25.62127 lr=0.00000121
+2026-06-28 05:25:04 epoch=72/80 step=800/1275 loss=25.63215 lr=0.00000120
+2026-06-28 05:25:14 epoch=72/80 step=850/1275 loss=25.61773 lr=0.00000120
+2026-06-28 05:25:24 epoch=72/80 step=900/1275 loss=25.47175 lr=0.00000119
+2026-06-28 05:25:35 epoch=72/80 step=950/1275 loss=25.48915 lr=0.00000118
+2026-06-28 05:25:46 epoch=72/80 step=1000/1275 loss=25.38421 lr=0.00000117
+2026-06-28 05:25:57 epoch=72/80 step=1050/1275 loss=25.34992 lr=0.00000116
+2026-06-28 05:26:06 epoch=72/80 step=1100/1275 loss=25.37465 lr=0.00000115
+2026-06-28 05:26:16 epoch=72/80 step=1150/1275 loss=25.34359 lr=0.00000115
+2026-06-28 05:26:25 epoch=72/80 step=1200/1275 loss=25.31380 lr=0.00000114
+2026-06-28 05:26:36 epoch=72/80 step=1250/1275 loss=25.35201 lr=0.00000113
+2026-06-28 05:26:52 mask2former val mIoU=0.56236 background=0.62742 live_coral=0.68767 dead_coral=0.43949 bleached_coral=0.49486
+2026-06-28 05:27:03 epoch=73/80 step=50/1275 loss=24.47892 lr=0.00000112
+2026-06-28 05:27:15 epoch=73/80 step=100/1275 loss=25.41455 lr=0.00000111
+2026-06-28 05:27:25 epoch=73/80 step=150/1275 loss=25.48600 lr=0.00000110
+2026-06-28 05:27:36 epoch=73/80 step=200/1275 loss=25.19438 lr=0.00000109
+2026-06-28 05:27:47 epoch=73/80 step=250/1275 loss=24.92836 lr=0.00000109
+2026-06-28 05:27:57 epoch=73/80 step=300/1275 loss=25.00481 lr=0.00000108
+2026-06-28 05:28:08 epoch=73/80 step=350/1275 loss=25.36987 lr=0.00000107
+2026-06-28 05:28:19 epoch=73/80 step=400/1275 loss=25.25951 lr=0.00000106
+2026-06-28 05:28:30 epoch=73/80 step=450/1275 loss=25.27800 lr=0.00000106
+2026-06-28 05:28:39 epoch=73/80 step=500/1275 loss=25.41822 lr=0.00000105
+2026-06-28 05:28:50 epoch=73/80 step=550/1275 loss=25.40029 lr=0.00000104
+2026-06-28 05:29:01 epoch=73/80 step=600/1275 loss=25.33881 lr=0.00000103
+2026-06-28 05:29:12 epoch=73/80 step=650/1275 loss=25.36658 lr=0.00000102
+2026-06-28 05:29:22 epoch=73/80 step=700/1275 loss=25.28719 lr=0.00000102
+2026-06-28 05:29:33 epoch=73/80 step=750/1275 loss=25.25586 lr=0.00000101
+2026-06-28 05:29:43 epoch=73/80 step=800/1275 loss=25.23200 lr=0.00000100
+2026-06-28 05:29:53 epoch=73/80 step=850/1275 loss=25.26726 lr=0.00000100
+2026-06-28 05:30:04 epoch=73/80 step=900/1275 loss=25.33309 lr=0.00000099
+2026-06-28 05:30:14 epoch=73/80 step=950/1275 loss=25.43524 lr=0.00000098
+2026-06-28 05:30:25 epoch=73/80 step=1000/1275 loss=25.45366 lr=0.00000097
+2026-06-28 05:30:36 epoch=73/80 step=1050/1275 loss=25.47114 lr=0.00000097
+2026-06-28 05:30:47 epoch=73/80 step=1100/1275 loss=25.56131 lr=0.00000096
+2026-06-28 05:30:57 epoch=73/80 step=1150/1275 loss=25.48277 lr=0.00000095
+2026-06-28 05:31:08 epoch=73/80 step=1200/1275 loss=25.44869 lr=0.00000094
+2026-06-28 05:31:18 epoch=73/80 step=1250/1275 loss=25.40143 lr=0.00000094
+2026-06-28 05:31:36 mask2former val mIoU=0.56388 background=0.62530 live_coral=0.68622 dead_coral=0.44184 bleached_coral=0.50214
+2026-06-28 05:31:46 epoch=74/80 step=50/1275 loss=23.74450 lr=0.00000093
+2026-06-28 05:31:56 epoch=74/80 step=100/1275 loss=24.46583 lr=0.00000092
+2026-06-28 05:32:07 epoch=74/80 step=150/1275 loss=24.14931 lr=0.00000091
+2026-06-28 05:32:18 epoch=74/80 step=200/1275 loss=24.90827 lr=0.00000091
+2026-06-28 05:32:29 epoch=74/80 step=250/1275 loss=24.57261 lr=0.00000090
+2026-06-28 05:32:40 epoch=74/80 step=300/1275 loss=24.69510 lr=0.00000089
+2026-06-28 05:32:50 epoch=74/80 step=350/1275 loss=24.55924 lr=0.00000089
+2026-06-28 05:33:00 epoch=74/80 step=400/1275 loss=24.59374 lr=0.00000088
+2026-06-28 05:33:10 epoch=74/80 step=450/1275 loss=24.64681 lr=0.00000087
+2026-06-28 05:33:21 epoch=74/80 step=500/1275 loss=24.50124 lr=0.00000087
+2026-06-28 05:33:31 epoch=74/80 step=550/1275 loss=24.81331 lr=0.00000086
+2026-06-28 05:33:42 epoch=74/80 step=600/1275 loss=25.13550 lr=0.00000085
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+2026-06-28 05:34:37 epoch=74/80 step=850/1275 loss=25.14005 lr=0.00000082
+2026-06-28 05:34:48 epoch=74/80 step=900/1275 loss=25.19518 lr=0.00000081
+2026-06-28 05:34:59 epoch=74/80 step=950/1275 loss=25.26269 lr=0.00000081
+2026-06-28 05:35:10 epoch=74/80 step=1000/1275 loss=25.46459 lr=0.00000080
+2026-06-28 05:35:21 epoch=74/80 step=1050/1275 loss=25.54579 lr=0.00000079
+2026-06-28 05:35:31 epoch=74/80 step=1100/1275 loss=25.63615 lr=0.00000079
+2026-06-28 05:35:42 epoch=74/80 step=1150/1275 loss=25.69459 lr=0.00000078
+2026-06-28 05:35:53 epoch=74/80 step=1200/1275 loss=25.67892 lr=0.00000078
+2026-06-28 05:36:03 epoch=74/80 step=1250/1275 loss=25.56847 lr=0.00000077
+2026-06-28 05:36:21 mask2former val mIoU=0.56661 background=0.62405 live_coral=0.68727 dead_coral=0.44527 bleached_coral=0.50984
+2026-06-28 05:36:22 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 05:36:34 epoch=75/80 step=50/1275 loss=24.35501 lr=0.00000076
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+2026-06-28 05:36:55 epoch=75/80 step=150/1275 loss=25.31890 lr=0.00000075
+2026-06-28 05:37:06 epoch=75/80 step=200/1275 loss=25.56003 lr=0.00000074
+2026-06-28 05:37:17 epoch=75/80 step=250/1275 loss=25.81712 lr=0.00000074
+2026-06-28 05:37:28 epoch=75/80 step=300/1275 loss=25.88178 lr=0.00000073
+2026-06-28 05:37:39 epoch=75/80 step=350/1275 loss=26.00782 lr=0.00000073
+2026-06-28 05:37:49 epoch=75/80 step=400/1275 loss=25.90231 lr=0.00000072
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+2026-06-28 05:39:54 epoch=75/80 step=1000/1275 loss=25.71348 lr=0.00000065
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+2026-06-28 05:41:03 mask2former val mIoU=0.56763 background=0.62534 live_coral=0.68637 dead_coral=0.46158 bleached_coral=0.49724
+2026-06-28 05:41:05 saved /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best.pth and /root/autodl-tmp/coral/coral_seg/work_dirs/mask2former_swin_b/best_hf
+2026-06-28 05:41:16 epoch=76/80 step=50/1275 loss=25.53409 lr=0.00000062
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+2026-06-28 05:45:20 epoch=76/80 step=1250/1275 loss=25.48456 lr=0.00000051
+2026-06-28 05:45:36 mask2former val mIoU=0.56088 background=0.62599 live_coral=0.68756 dead_coral=0.44460 bleached_coral=0.48536
+2026-06-28 05:45:47 epoch=77/80 step=50/1275 loss=24.18464 lr=0.00000050
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+2026-06-28 05:49:30 epoch=77/80 step=1100/1275 loss=25.50533 lr=0.00000043
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+2026-06-28 05:50:02 epoch=77/80 step=1250/1275 loss=25.51228 lr=0.00000042
+2026-06-28 05:50:18 mask2former val mIoU=0.56700 background=0.62910 live_coral=0.69338 dead_coral=0.45808 bleached_coral=0.48744
+2026-06-28 05:50:29 epoch=78/80 step=50/1275 loss=23.76864 lr=0.00000041
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diff --git a/coral_seg/pretrained/mask2former-swin-base-ade-semantic/model.safetensors b/coral_seg/pretrained/mask2former-swin-base-ade-semantic/model.safetensors
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diff --git a/coral_seg/pretrained/mask2former-swin-base-ade-semantic/preprocessor_config.json b/coral_seg/pretrained/mask2former-swin-base-ade-semantic/preprocessor_config.json
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diff --git a/coral_seg/requirements.txt b/coral_seg/requirements.txt
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diff --git a/coral_seg/splits/train_full.txt b/coral_seg/splits/train_full.txt
new file mode 100644
index 0000000000000000000000000000000000000000..64017a983bfaa151c52b51fceaf63c61caf839c3
--- /dev/null
+++ b/coral_seg/splits/train_full.txt
@@ -0,0 +1,3000 @@
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diff --git a/coral_seg/splits/val.txt b/coral_seg/splits/val.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1472ae7085e2df5d3b70e0c3591fc241778ded27
--- /dev/null
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diff --git a/coral_seg/submissions/results.zip b/coral_seg/submissions/results.zip
new file mode 100644
index 0000000000000000000000000000000000000000..10cc8f64156ba7f845d288da422a50868b5c2afd
--- /dev/null
+++ b/coral_seg/submissions/results.zip
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+version https://git-lfs.github.com/spec/v1
+oid sha256:19326a534acc1de1c4b4a901d94f11e92699cf347d3e13901f8da343a65c9502
+size 950074
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diff --git a/coral_seg/tools/check_data.py b/coral_seg/tools/check_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb0ecfbc54ceb6743fa3ba9bb1da3a6c445b5cde
--- /dev/null
+++ b/coral_seg/tools/check_data.py
@@ -0,0 +1,35 @@
+import argparse
+import sys
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from coral_seg.utils import list_pngs, load_config
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", default=str(ROOT / "configs/final_dual.yaml"))
+    args = parser.parse_args()
+    cfg = load_config(args.config)
+    for key in ("train_images", "train_masks", "test_images"):
+        paths = list_pngs(cfg["data"][key])
+        print(key, len(paths), paths[0].name if paths else None, paths[-1].name if paths else None)
+    values = set()
+    bad = []
+    for path in list_pngs(cfg["data"]["train_masks"]):
+        arr = np.array(Image.open(path))
+        unique = set(np.unique(arr).tolist())
+        values |= unique
+        if not unique <= {0, 1, 2, 3}:
+            bad.append((path.name, sorted(unique)))
+    print("mask values:", sorted(values))
+    print("bad masks:", len(bad), bad[:5])
+
+
+if __name__ == "__main__":
+    main()
diff --git a/coral_seg/tools/create_splits.py b/coral_seg/tools/create_splits.py
new file mode 100644
index 0000000000000000000000000000000000000000..90f3777463a1793d1585ce882b14b1f3ee652315
--- /dev/null
+++ b/coral_seg/tools/create_splits.py
@@ -0,0 +1,31 @@
+import argparse
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from coral_seg.splits import create_split_files
+from coral_seg.utils import load_config
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", default=str(ROOT / "configs/final_dual.yaml"))
+    args = parser.parse_args()
+
+    cfg = load_config(args.config)
+    train_file, val_file, full_file = create_split_files(
+        cfg["data"]["train_images"],
+        cfg["data"]["train_masks"],
+        cfg["data"]["split_dir"],
+        val_ratio=float(cfg["data"].get("val_ratio", 0.15)),
+        seed=int(cfg.get("seed", 2026)),
+    )
+    print(f"train split: {train_file}")
+    print(f"val split: {val_file}")
+    print(f"full split: {full_file}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/coral_seg/tools/download_pretrained.py b/coral_seg/tools/download_pretrained.py
new file mode 100644
index 0000000000000000000000000000000000000000..c998fd536797436a6865a36544e120a11e3a74f2
--- /dev/null
+++ b/coral_seg/tools/download_pretrained.py
@@ -0,0 +1,54 @@
+import argparse
+import shutil
+import sys
+from pathlib import Path
+
+import torch
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from huggingface_hub import snapshot_download
+from torchvision.models import ConvNeXt_Large_Weights, convnext_large
+
+from coral_seg.utils import ensure_dir, load_config
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", default=str(ROOT / "configs/final_dual.yaml"))
+    args = parser.parse_args()
+
+    cfg = load_config(args.config)
+    mask2former_dir = Path(cfg["mask2former"]["pretrained_dir"])
+    ensure_dir(mask2former_dir.parent)
+    has_mask2former_weight = (mask2former_dir / "model.safetensors").is_file() or (mask2former_dir / "pytorch_model.bin").is_file()
+    has_mask2former_config = (mask2former_dir / "config.json").is_file() and (mask2former_dir / "preprocessor_config.json").is_file()
+    if has_mask2former_weight and has_mask2former_config:
+        print(f"Mask2Former local files already exist: {mask2former_dir}")
+    else:
+        print("Downloading Mask2Former-Swin-B from HuggingFace cache/source...")
+        snapshot_download(
+            repo_id=cfg["mask2former"]["pretrained_name"],
+            local_dir=mask2former_dir,
+            local_dir_use_symlinks=False,
+        )
+        print(f"Mask2Former saved to {mask2former_dir}")
+
+    convnext_path = Path(cfg["convnext"].get("pretrained_path", ROOT / "pretrained/convnext_large-ea097f82.pth"))
+    ensure_dir(convnext_path.parent)
+    if convnext_path.is_file():
+        print(f"ConvNeXt-L local weight already exists: {convnext_path}")
+    else:
+        print("Downloading torchvision ConvNeXt-L ImageNet-1K weights...")
+        _ = convnext_large(weights=ConvNeXt_Large_Weights.IMAGENET1K_V1)
+        cache_name = Path(ConvNeXt_Large_Weights.IMAGENET1K_V1.url).name
+        cache_path = Path(torch.hub.get_dir()) / "checkpoints" / cache_name
+        if not cache_path.is_file():
+            raise FileNotFoundError(f"torchvision cache weight not found: {cache_path}")
+        shutil.copy2(cache_path, convnext_path)
+        print(f"ConvNeXt-L local weight saved to {convnext_path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/coral_seg/tools/eval_ensemble_val.py b/coral_seg/tools/eval_ensemble_val.py
new file mode 100644
index 0000000000000000000000000000000000000000..c19384f251129422747e137b5308eb13172f228b
--- /dev/null
+++ b/coral_seg/tools/eval_ensemble_val.py
@@ -0,0 +1,90 @@
+import argparse
+import sys
+from pathlib import Path
+
+import numpy as np
+import torch
+from PIL import Image
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from coral_seg.utils import compute_iou, fast_hist, format_metrics, load_config, read_image, set_seed
+from coral_seg.splits import read_ids
+from tools.infer_ensemble import load_convnext, load_mask2former, predict_convnext, predict_mask2former
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--config', default=str(ROOT / 'configs/final_dual.yaml'))
+    parser.add_argument('--convnext-checkpoint', default='')
+    parser.add_argument('--mask2former-checkpoint', default='')
+    parser.add_argument('--split-file', default='')
+    parser.add_argument('--max-samples', type=int, default=0)
+    args = parser.parse_args()
+
+    cfg = load_config(args.config)
+    set_seed(int(cfg.get('seed', 2026)))
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    infer_cfg = cfg['infer']
+    num_classes = int(cfg['num_classes'])
+
+    conv_model = load_convnext(args.convnext_checkpoint, cfg, device) if args.convnext_checkpoint else None
+    mask_model = load_mask2former(args.mask2former_checkpoint, cfg, device) if args.mask2former_checkpoint else None
+    if conv_model is None and mask_model is None:
+        raise ValueError('Provide --convnext-checkpoint and/or --mask2former-checkpoint')
+
+    conv_w = float(infer_cfg.get('convnext_weight', 0.45)) if conv_model is not None else 0.0
+    mask_w = float(infer_cfg.get('mask2former_weight', 0.55)) if mask_model is not None else 0.0
+    norm = max(1e-6, conv_w + mask_w)
+    conv_w /= norm
+    mask_w /= norm
+    flips = list(infer_cfg.get('tta_flips', ['none']))
+
+    split_file = args.split_file or str(Path(cfg['data']['split_dir']) / 'val.txt')
+    ids = read_ids(split_file)
+    if args.max_samples > 0:
+        ids = ids[:args.max_samples]
+
+    hist = np.zeros((num_classes, num_classes), dtype=np.int64)
+    image_dir = Path(cfg['data']['train_images'])
+    mask_dir = Path(cfg['data']['train_masks'])
+
+    for idx, sample_id in enumerate(ids, start=1):
+        image_path = image_dir / f'{sample_id}.png'
+        mask_path = mask_dir / f'{sample_id}.png'
+        image = read_image(image_path)
+        target = np.array(Image.open(mask_path), dtype=np.uint8)
+        total = None
+        if conv_model is not None:
+            probs = predict_convnext(
+                conv_model,
+                image,
+                infer_cfg.get('convnext_sizes', [cfg['convnext']['image_size']]),
+                flips,
+                num_classes,
+                device,
+            )
+            total = conv_w * probs if total is None else total + conv_w * probs
+        if mask_model is not None:
+            probs = predict_mask2former(
+                mask_model,
+                image,
+                infer_cfg.get('mask2former_sizes', [cfg['mask2former']['image_size']]),
+                flips,
+                num_classes,
+                device,
+            )
+            total = mask_w * probs if total is None else total + mask_w * probs
+        pred = total.argmax(dim=1).squeeze(0).cpu().numpy().astype(np.uint8)
+        hist += fast_hist(pred, target, num_classes)
+        if idx % 50 == 0 or idx == len(ids):
+            iou, miou = compute_iou(hist)
+            print(f'evaluated {idx}/{len(ids)} {format_metrics(iou, miou, cfg["class_names"])}')
+
+    iou, miou = compute_iou(hist)
+    print('final', format_metrics(iou, miou, cfg['class_names']))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/coral_seg/tools/eval_predictions.py b/coral_seg/tools/eval_predictions.py
new file mode 100644
index 0000000000000000000000000000000000000000..21cbb503d7da3f191c887bacf5899fac8cd69be1
--- /dev/null
+++ b/coral_seg/tools/eval_predictions.py
@@ -0,0 +1,99 @@
+import argparse
+from pathlib import Path
+from typing import Dict, Tuple
+
+import numpy as np
+from PIL import Image
+
+CLASS_NAMES = ['background', 'live_coral', 'dead_coral', 'bleached_coral']
+
+
+def parse_color_map(text: str) -> Dict[Tuple[int, int, int], int]:
+    mapping = {}
+    if not text:
+        return mapping
+    for item in text.split(';'):
+        item = item.strip()
+        if not item:
+            continue
+        color, label = item.split(':')
+        rgb = tuple(int(x) for x in color.split(','))
+        if len(rgb) != 3:
+            raise ValueError(f'Bad RGB color: {color}')
+        mapping[rgb] = int(label)
+    return mapping
+
+
+def read_pred(path: Path) -> np.ndarray:
+    arr = np.array(Image.open(path))
+    if arr.ndim == 3:
+        arr = arr[:, :, 0]
+    return arr.astype(np.uint8)
+
+
+def read_gt(path: Path, color_map: Dict[Tuple[int, int, int], int]) -> np.ndarray:
+    arr = np.array(Image.open(path))
+    if arr.ndim == 2:
+        return arr.astype(np.uint8)
+    if not color_map:
+        colors = np.unique(arr.reshape(-1, arr.shape[-1])[:, :3], axis=0)
+        raise ValueError(
+            'GT is RGB. Provide --color-map, for example '
+            '"0,0,0:0;0,162,232:1;240,110,170:2". '
+            f'Found colors: {colors.tolist()}'
+        )
+    rgb = arr[:, :, :3]
+    out = np.full(rgb.shape[:2], 255, dtype=np.uint8)
+    for color, label in color_map.items():
+        out[np.all(rgb == np.array(color, dtype=np.uint8), axis=-1)] = label
+    unknown = out == 255
+    if unknown.any():
+        colors = np.unique(rgb[unknown].reshape(-1, 3), axis=0)
+        raise ValueError(f'Unknown GT colors: {colors.tolist()}')
+    return out
+
+
+def fast_hist(pred: np.ndarray, target: np.ndarray, num_classes: int) -> np.ndarray:
+    pred = pred.reshape(-1)
+    target = target.reshape(-1)
+    valid = (target >= 0) & (target < num_classes)
+    hist = np.bincount(num_classes * target[valid].astype(np.int64) + pred[valid].astype(np.int64), minlength=num_classes**2)
+    return hist.reshape(num_classes, num_classes)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--pred-dir', default='/root/autodl-tmp/coral/coral_seg/submissions/results')
+    parser.add_argument('--gt-dir', required=True)
+    parser.add_argument('--color-map', default='')
+    parser.add_argument('--num-classes', type=int, default=4)
+    args = parser.parse_args()
+
+    pred_dir = Path(args.pred_dir)
+    gt_dir = Path(args.gt_dir)
+    color_map = parse_color_map(args.color_map)
+    hist = np.zeros((args.num_classes, args.num_classes), dtype=np.int64)
+    count = 0
+    for pred_path in sorted(pred_dir.glob('*.png')):
+        gt_path = gt_dir / pred_path.name
+        if not gt_path.exists():
+            continue
+        pred = read_pred(pred_path)
+        gt = read_gt(gt_path, color_map)
+        if pred.shape != gt.shape:
+            raise ValueError(f'Shape mismatch for {pred_path.name}: pred={pred.shape}, gt={gt.shape}')
+        hist += fast_hist(pred, gt, args.num_classes)
+        count += 1
+    if count == 0:
+        raise RuntimeError('No matched prediction/GT PNG files found')
+    denom = hist.sum(1) + hist.sum(0) - np.diag(hist)
+    iou = np.divide(np.diag(hist), denom, out=np.full(args.num_classes, np.nan, dtype=np.float64), where=denom != 0)
+    print('matched_files:', count)
+    print('mIoU:', float(np.nanmean(iou)))
+    for idx, value in enumerate(iou):
+        name = CLASS_NAMES[idx] if idx < len(CLASS_NAMES) else f'class_{idx}'
+        print(f'{idx} {name}: {value}')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/coral_seg/tools/infer_ensemble.py b/coral_seg/tools/infer_ensemble.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f2bbc625e635264cb36e894c909bdfdd7c9e2a6
--- /dev/null
+++ b/coral_seg/tools/infer_ensemble.py
@@ -0,0 +1,189 @@
+import argparse
+import sys
+from pathlib import Path
+from typing import Dict, Iterable, List, Optional
+
+import cv2
+import numpy as np
+import torch
+import torch.nn.functional as F
+from PIL import Image
+from transformers import Mask2FormerForUniversalSegmentation
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from coral_seg.model_convnext_fpn import ConvNeXtFPN
+from coral_seg.utils import (
+    ensure_dir,
+    list_pngs,
+    load_config,
+    make_results_zip,
+    normalize_tensor,
+    read_image,
+    save_mask,
+    set_seed,
+    timestamp,
+)
+from tools.train_mask2former import semantic_logits_from_mask2former
+
+
+def apply_flip_tensor(x: torch.Tensor, flip: str) -> torch.Tensor:
+    if flip == "h":
+        return torch.flip(x, dims=[-1])
+    if flip == "v":
+        return torch.flip(x, dims=[-2])
+    if flip == "hv":
+        return torch.flip(x, dims=[-2, -1])
+    return x
+
+
+def resize_normalize(image: np.ndarray, size: int) -> torch.Tensor:
+    resized = cv2.resize(image, (size, size), interpolation=cv2.INTER_LINEAR)
+    return normalize_tensor(resized).unsqueeze(0)
+
+
+def load_convnext(checkpoint: str, cfg: Dict, device: torch.device) -> ConvNeXtFPN:
+    model = ConvNeXtFPN(num_classes=int(cfg["num_classes"]), pretrained=False)
+    ckpt = torch.load(checkpoint, map_location="cpu")
+    model.load_state_dict(ckpt["model"], strict=True)
+    return model.to(device).eval()
+
+
+def load_mask2former(path_or_ckpt: str, cfg: Dict, device: torch.device) -> Mask2FormerForUniversalSegmentation:
+    path = Path(path_or_ckpt)
+    if path.is_dir():
+        model = Mask2FormerForUniversalSegmentation.from_pretrained(path)
+    else:
+        mcfg = cfg["mask2former"]
+        base = mcfg.get("pretrained_dir") if Path(str(mcfg.get("pretrained_dir", ""))).exists() else mcfg["pretrained_name"]
+        model = Mask2FormerForUniversalSegmentation.from_pretrained(
+            base,
+            num_labels=int(cfg["num_classes"]),
+            ignore_mismatched_sizes=True,
+        )
+        ckpt = torch.load(path_or_ckpt, map_location="cpu")
+        model.load_state_dict(ckpt["model"], strict=True)
+    return model.to(device).eval()
+
+
+@torch.no_grad()
+def predict_convnext(
+    model: ConvNeXtFPN,
+    image: np.ndarray,
+    sizes: Iterable[int],
+    flips: Iterable[str],
+    num_classes: int,
+    device: torch.device,
+) -> torch.Tensor:
+    h, w = image.shape[:2]
+    probs_sum = torch.zeros((1, num_classes, h, w), device=device)
+    count = 0
+    for size in sizes:
+        x = resize_normalize(image, int(size)).to(device)
+        for flip in flips:
+            xf = apply_flip_tensor(x, flip)
+            logits = model(xf)
+            logits = apply_flip_tensor(logits, flip)
+            logits = F.interpolate(logits, size=(h, w), mode="bilinear", align_corners=False)
+            probs_sum += torch.softmax(logits, dim=1)
+            count += 1
+    return probs_sum / max(1, count)
+
+
+@torch.no_grad()
+def predict_mask2former(
+    model: Mask2FormerForUniversalSegmentation,
+    image: np.ndarray,
+    sizes: Iterable[int],
+    flips: Iterable[str],
+    num_classes: int,
+    device: torch.device,
+) -> torch.Tensor:
+    h, w = image.shape[:2]
+    probs_sum = torch.zeros((1, num_classes, h, w), device=device)
+    count = 0
+    for size in sizes:
+        x = resize_normalize(image, int(size)).to(device)
+        for flip in flips:
+            xf = apply_flip_tensor(x, flip)
+            outputs = model(pixel_values=xf)
+            logits = semantic_logits_from_mask2former(outputs, target_size=(h, w))
+            logits = apply_flip_tensor(logits, flip)
+            probs_sum += torch.softmax(logits, dim=1)
+            count += 1
+    return probs_sum / max(1, count)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", default=str(ROOT / "configs/final_dual.yaml"))
+    parser.add_argument("--convnext-checkpoint", default="")
+    parser.add_argument("--mask2former-checkpoint", default="")
+    parser.add_argument("--test-dir", default="")
+    parser.add_argument("--out-dir", default="")
+    parser.add_argument("--zip-path", default="")
+    parser.add_argument("--no-zip", action="store_true")
+    args = parser.parse_args()
+
+    cfg = load_config(args.config)
+    set_seed(int(cfg.get("seed", 2026)))
+    infer_cfg = cfg["infer"]
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    test_dir = args.test_dir or cfg["data"]["test_images"]
+    out_dir = ensure_dir(args.out_dir or infer_cfg["out_dir"])
+    num_classes = int(cfg["num_classes"])
+
+    conv_model = None
+    mask_model = None
+    if args.convnext_checkpoint:
+        conv_model = load_convnext(args.convnext_checkpoint, cfg, device)
+    if args.mask2former_checkpoint:
+        mask_model = load_mask2former(args.mask2former_checkpoint, cfg, device)
+    if conv_model is None and mask_model is None:
+        raise ValueError("Provide at least one checkpoint: --convnext-checkpoint or --mask2former-checkpoint")
+
+    conv_w = float(infer_cfg.get("convnext_weight", 0.45)) if conv_model is not None else 0.0
+    mask_w = float(infer_cfg.get("mask2former_weight", 0.55)) if mask_model is not None else 0.0
+    norm = max(1e-6, conv_w + mask_w)
+    conv_w /= norm
+    mask_w /= norm
+    flips = list(infer_cfg.get("tta_flips", ["none"]))
+
+    image_paths = list_pngs(test_dir)
+    for idx, image_path in enumerate(image_paths, start=1):
+        image = read_image(image_path)
+        total = None
+        if conv_model is not None:
+            probs = predict_convnext(
+                conv_model,
+                image,
+                infer_cfg.get("convnext_sizes", [cfg["convnext"]["image_size"]]),
+                flips,
+                num_classes,
+                device,
+            )
+            total = conv_w * probs if total is None else total + conv_w * probs
+        if mask_model is not None:
+            probs = predict_mask2former(
+                mask_model,
+                image,
+                infer_cfg.get("mask2former_sizes", [cfg["mask2former"]["image_size"]]),
+                flips,
+                num_classes,
+                device,
+            )
+            total = mask_w * probs if total is None else total + mask_w * probs
+        pred = total.argmax(dim=1).squeeze(0).cpu().numpy().astype(np.uint8)
+        save_mask(out_dir / image_path.name, pred)
+        if idx % 50 == 0 or idx == len(image_paths):
+            print(f"{timestamp()} inferred {idx}/{len(image_paths)}")
+
+    if not args.no_zip:
+        zip_path = args.zip_path or infer_cfg["zip_path"]
+        make_results_zip(out_dir, zip_path)
+        print(f"saved zip: {zip_path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/coral_seg/tools/train_convnext.py b/coral_seg/tools/train_convnext.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb8f35951405eded6710fb192b4808fb1fbeaea7
--- /dev/null
+++ b/coral_seg/tools/train_convnext.py
@@ -0,0 +1,212 @@
+import argparse
+import functools
+import sys
+from pathlib import Path
+from typing import Dict
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch.cuda.amp import GradScaler, autocast
+from torch.optim import AdamW
+from torch.optim.lr_scheduler import LambdaLR
+from torch.utils.data import DataLoader
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from coral_seg.dataset import CoralSegDataset, build_train_transform, build_val_transform, segmentation_collate
+from coral_seg.losses import ComboSegLoss
+from coral_seg.model_convnext_fpn import ConvNeXtFPN
+from coral_seg.splits import create_split_files
+from coral_seg.utils import (
+    AverageMeter,
+    compute_iou,
+    cosine_warmup_lambda,
+    ensure_dir,
+    fast_hist,
+    format_metrics,
+    load_config,
+    save_json,
+    set_seed,
+    timestamp,
+)
+
+
+def build_loaders(cfg: Dict, image_size: int, batch_size: int, full_train: bool) -> tuple[DataLoader, DataLoader | None]:
+    data_cfg = cfg["data"]
+    train_file, val_file, full_file = create_split_files(
+        data_cfg["train_images"],
+        data_cfg["train_masks"],
+        data_cfg["split_dir"],
+        val_ratio=float(data_cfg.get("val_ratio", 0.15)),
+        seed=int(cfg.get("seed", 2026)),
+    )
+    split_file = full_file if full_train else train_file
+    train_ds = CoralSegDataset(
+        data_cfg["train_images"],
+        data_cfg["train_masks"],
+        str(split_file),
+        build_train_transform(image_size),
+    )
+    train_loader = DataLoader(
+        train_ds,
+        batch_size=batch_size,
+        shuffle=True,
+        num_workers=int(cfg["train"].get("num_workers", 8)),
+        pin_memory=True,
+        drop_last=True,
+        collate_fn=segmentation_collate,
+        persistent_workers=int(cfg["train"].get("num_workers", 8)) > 0,
+    )
+    val_loader = None
+    if not full_train:
+        val_ds = CoralSegDataset(
+            data_cfg["train_images"],
+            data_cfg["train_masks"],
+            str(val_file),
+            build_val_transform(image_size),
+        )
+        val_loader = DataLoader(
+            val_ds,
+            batch_size=batch_size,
+            shuffle=False,
+            num_workers=int(cfg["train"].get("num_workers", 8)),
+            pin_memory=True,
+            collate_fn=segmentation_collate,
+            persistent_workers=int(cfg["train"].get("num_workers", 8)) > 0,
+        )
+    return train_loader, val_loader
+
+
+@torch.no_grad()
+def validate(model: torch.nn.Module, loader: DataLoader, cfg: Dict, device: torch.device) -> tuple[float, np.ndarray]:
+    model.eval()
+    num_classes = int(cfg["num_classes"])
+    hist = np.zeros((num_classes, num_classes), dtype=np.int64)
+    for batch in loader:
+        images = batch["image"].to(device, non_blocking=True)
+        masks = batch["mask"].numpy()
+        logits = model(images)
+        preds = logits.argmax(dim=1).cpu().numpy().astype(np.uint8)
+        for pred, target in zip(preds, masks):
+            hist += fast_hist(pred, target, num_classes)
+    iou, miou = compute_iou(hist)
+    return miou, iou
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", default=str(ROOT / "configs/final_dual.yaml"))
+    parser.add_argument("--resume", default="")
+    parser.add_argument("--full-train", action="store_true")
+    args = parser.parse_args()
+
+    cfg = load_config(args.config)
+    set_seed(int(cfg.get("seed", 2026)))
+    conv_cfg = cfg["convnext"]
+    work_dir = ensure_dir(conv_cfg["work_dir"])
+    save_json(work_dir / "resolved_config.json", cfg)
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    train_loader, val_loader = build_loaders(
+        cfg,
+        image_size=int(conv_cfg["image_size"]),
+        batch_size=int(conv_cfg["batch_size"]),
+        full_train=args.full_train,
+    )
+
+    model = ConvNeXtFPN(
+        num_classes=int(cfg["num_classes"]),
+        pretrained=bool(conv_cfg.get("pretrained", True)),
+        pretrained_path=conv_cfg.get("pretrained_path"),
+    ).to(device)
+
+    if args.resume:
+        ckpt = torch.load(args.resume, map_location="cpu")
+        model.load_state_dict(ckpt["model"], strict=True)
+
+    loss_cfg = conv_cfg["loss"]
+    criterion = ComboSegLoss(
+        num_classes=int(cfg["num_classes"]),
+        ce_weight=float(loss_cfg.get("ce", 1.0)),
+        dice_weight=float(loss_cfg.get("dice", 1.0)),
+        lovasz_weight=float(loss_cfg.get("lovasz", 0.5)),
+        label_smoothing=float(loss_cfg.get("label_smoothing", 0.05)),
+    )
+
+    backbone_params = []
+    head_params = []
+    for name, param in model.named_parameters():
+        if not param.requires_grad:
+            continue
+        if name.startswith("features"):
+            backbone_params.append(param)
+        else:
+            head_params.append(param)
+
+    optimizer = AdamW(
+        [
+            {"params": backbone_params, "lr": float(conv_cfg["lr"]) * float(conv_cfg.get("backbone_lr_mult", 0.25))},
+            {"params": head_params, "lr": float(conv_cfg["lr"])},
+        ],
+        weight_decay=float(conv_cfg["weight_decay"]),
+    )
+    epochs = int(conv_cfg["epochs"])
+    total_steps = epochs * len(train_loader)
+    warmup_steps = int(conv_cfg.get("warmup_epochs", 5)) * len(train_loader)
+    scheduler = LambdaLR(
+        optimizer,
+        functools.partial(cosine_warmup_lambda, total_steps=total_steps, warmup_steps=warmup_steps),
+    )
+    scaler = GradScaler(enabled=bool(cfg["train"].get("amp", True)))
+    best_miou = -1.0
+
+    for epoch in range(1, epochs + 1):
+        model.train()
+        meter = AverageMeter()
+        for step, batch in enumerate(train_loader, start=1):
+            images = batch["image"].to(device, non_blocking=True)
+            masks = batch["mask"].to(device, non_blocking=True)
+            optimizer.zero_grad(set_to_none=True)
+            with autocast(enabled=bool(cfg["train"].get("amp", True))):
+                logits = model(images)
+                loss = criterion(logits, masks, epoch)
+            scaler.scale(loss).backward()
+            grad_clip = float(cfg["train"].get("grad_clip_norm", 0.0))
+            if grad_clip > 0:
+                scaler.unscale_(optimizer)
+                torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
+            scaler.step(optimizer)
+            scaler.update()
+            scheduler.step()
+            meter.update(loss.item(), images.size(0))
+            if step % int(cfg["train"].get("print_freq", 50)) == 0:
+                lr = optimizer.param_groups[-1]["lr"]
+                print(f"{timestamp()} epoch={epoch}/{epochs} step={step}/{len(train_loader)} loss={meter.avg:.5f} lr={lr:.8f}")
+
+        metrics = {"epoch": epoch, "train_loss": meter.avg}
+        if val_loader is not None:
+            miou, iou = validate(model, val_loader, cfg, device)
+            metrics.update({"val_miou": miou, "val_iou": iou.tolist()})
+            print(f"{timestamp()} convnext val {format_metrics(iou, miou, cfg['class_names'])}")
+            is_best = miou > best_miou
+            best_miou = max(best_miou, miou)
+        else:
+            is_best = True
+
+        state = {
+            "model": model.state_dict(),
+            "epoch": epoch,
+            "best_miou": best_miou,
+            "config": cfg,
+            "metrics": metrics,
+        }
+        torch.save(state, work_dir / "latest.pth")
+        if is_best:
+            torch.save(state, work_dir / "best.pth")
+            print(f"{timestamp()} saved {work_dir / 'best.pth'}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/coral_seg/tools/train_mask2former.py b/coral_seg/tools/train_mask2former.py
new file mode 100644
index 0000000000000000000000000000000000000000..971d53746ce7abb466aa7924edec449463b54cd1
--- /dev/null
+++ b/coral_seg/tools/train_mask2former.py
@@ -0,0 +1,226 @@
+import argparse
+import functools
+import sys
+from pathlib import Path
+from typing import Dict, List
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch.cuda.amp import GradScaler, autocast
+from torch.optim import AdamW
+from torch.optim.lr_scheduler import LambdaLR
+from torch.utils.data import DataLoader
+from transformers import Mask2FormerForUniversalSegmentation
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from coral_seg.dataset import CoralSegDataset, build_train_transform, build_val_transform, mask2former_collate
+from coral_seg.splits import create_split_files
+from coral_seg.utils import (
+    AverageMeter,
+    compute_iou,
+    cosine_warmup_lambda,
+    ensure_dir,
+    fast_hist,
+    format_metrics,
+    load_config,
+    save_json,
+    set_seed,
+    timestamp,
+)
+
+
+def semantic_logits_from_mask2former(outputs, target_size: tuple[int, int]) -> torch.Tensor:
+    class_logits = outputs.class_queries_logits
+    mask_logits = outputs.masks_queries_logits
+    class_probs = torch.softmax(class_logits, dim=-1)[..., :-1]
+    mask_probs = torch.sigmoid(mask_logits)
+    semseg = torch.einsum("bqc,bqhw->bchw", class_probs, mask_probs)
+    return F.interpolate(semseg, size=target_size, mode="bilinear", align_corners=False)
+
+
+def build_model(cfg: Dict) -> Mask2FormerForUniversalSegmentation:
+    mcfg = cfg["mask2former"]
+    id2label = {i: name for i, name in enumerate(cfg["class_names"])}
+    label2id = {name: i for i, name in id2label.items()}
+    model_name_or_path = mcfg.get("pretrained_dir") or mcfg["pretrained_name"]
+    if not Path(str(model_name_or_path)).exists():
+        model_name_or_path = mcfg["pretrained_name"]
+    model = Mask2FormerForUniversalSegmentation.from_pretrained(
+        model_name_or_path,
+        num_labels=int(cfg["num_classes"]),
+        id2label=id2label,
+        label2id=label2id,
+        ignore_mismatched_sizes=True,
+    )
+    if bool(mcfg.get("gradient_checkpointing", True)):
+        try:
+            model.gradient_checkpointing_enable()
+        except ValueError:
+            print("Gradient checkpointing is not supported by this Mask2Former class; continuing without it.")
+    return model
+
+
+def build_loaders(cfg: Dict, image_size: int, batch_size: int, full_train: bool) -> tuple[DataLoader, DataLoader | None]:
+    data_cfg = cfg["data"]
+    train_file, val_file, full_file = create_split_files(
+        data_cfg["train_images"],
+        data_cfg["train_masks"],
+        data_cfg["split_dir"],
+        val_ratio=float(data_cfg.get("val_ratio", 0.15)),
+        seed=int(cfg.get("seed", 2026)),
+    )
+    split_file = full_file if full_train else train_file
+    collate = functools.partial(mask2former_collate, num_classes=int(cfg["num_classes"]))
+    train_ds = CoralSegDataset(
+        data_cfg["train_images"],
+        data_cfg["train_masks"],
+        str(split_file),
+        build_train_transform(image_size),
+    )
+    train_loader = DataLoader(
+        train_ds,
+        batch_size=batch_size,
+        shuffle=True,
+        num_workers=int(cfg["train"].get("num_workers", 8)),
+        pin_memory=True,
+        drop_last=True,
+        collate_fn=collate,
+        persistent_workers=int(cfg["train"].get("num_workers", 8)) > 0,
+    )
+    val_loader = None
+    if not full_train:
+        val_ds = CoralSegDataset(
+            data_cfg["train_images"],
+            data_cfg["train_masks"],
+            str(val_file),
+            build_val_transform(image_size),
+        )
+        val_loader = DataLoader(
+            val_ds,
+            batch_size=batch_size,
+            shuffle=False,
+            num_workers=int(cfg["train"].get("num_workers", 8)),
+            pin_memory=True,
+            collate_fn=collate,
+            persistent_workers=int(cfg["train"].get("num_workers", 8)) > 0,
+        )
+    return train_loader, val_loader
+
+
+@torch.no_grad()
+def validate(model: torch.nn.Module, loader: DataLoader, cfg: Dict, device: torch.device) -> tuple[float, np.ndarray]:
+    model.eval()
+    num_classes = int(cfg["num_classes"])
+    hist = np.zeros((num_classes, num_classes), dtype=np.int64)
+    for batch in loader:
+        images = batch["image"].to(device, non_blocking=True)
+        masks = batch["raw_mask"].numpy()
+        outputs = model(pixel_values=images)
+        logits = semantic_logits_from_mask2former(outputs, target_size=masks.shape[-2:])
+        preds = logits.argmax(dim=1).cpu().numpy().astype(np.uint8)
+        for pred, target in zip(preds, masks):
+            hist += fast_hist(pred, target, num_classes)
+    iou, miou = compute_iou(hist)
+    return miou, iou
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", default=str(ROOT / "configs/final_dual.yaml"))
+    parser.add_argument("--resume", default="")
+    parser.add_argument("--full-train", action="store_true")
+    args = parser.parse_args()
+
+    cfg = load_config(args.config)
+    set_seed(int(cfg.get("seed", 2026)))
+    mcfg = cfg["mask2former"]
+    work_dir = ensure_dir(mcfg["work_dir"])
+    save_json(work_dir / "resolved_config.json", cfg)
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    train_loader, val_loader = build_loaders(
+        cfg,
+        image_size=int(mcfg["image_size"]),
+        batch_size=int(mcfg["batch_size"]),
+        full_train=args.full_train,
+    )
+    model = build_model(cfg).to(device)
+    if args.resume:
+        ckpt = torch.load(args.resume, map_location="cpu")
+        model.load_state_dict(ckpt["model"], strict=True)
+
+    no_decay = ("bias", "LayerNorm.weight", "layer_norm.weight", "norm.weight")
+    grouped = [
+        {
+            "params": [p for n, p in model.named_parameters() if p.requires_grad and not any(nd in n for nd in no_decay)],
+            "weight_decay": float(mcfg["weight_decay"]),
+        },
+        {
+            "params": [p for n, p in model.named_parameters() if p.requires_grad and any(nd in n for nd in no_decay)],
+            "weight_decay": 0.0,
+        },
+    ]
+    optimizer = AdamW(grouped, lr=float(mcfg["lr"]))
+    epochs = int(mcfg["epochs"])
+    total_steps = epochs * len(train_loader)
+    warmup_steps = int(mcfg.get("warmup_epochs", 5)) * len(train_loader)
+    scheduler = LambdaLR(
+        optimizer,
+        functools.partial(cosine_warmup_lambda, total_steps=total_steps, warmup_steps=warmup_steps),
+    )
+    scaler = GradScaler(enabled=bool(cfg["train"].get("amp", True)))
+    best_miou = -1.0
+
+    for epoch in range(1, epochs + 1):
+        model.train()
+        meter = AverageMeter()
+        for step, batch in enumerate(train_loader, start=1):
+            images = batch["image"].to(device, non_blocking=True)
+            mask_labels = [m.to(device, non_blocking=True) for m in batch["mask_labels"]]
+            class_labels = [c.to(device, non_blocking=True) for c in batch["class_labels"]]
+            optimizer.zero_grad(set_to_none=True)
+            with autocast(enabled=bool(cfg["train"].get("amp", True))):
+                outputs = model(pixel_values=images, mask_labels=mask_labels, class_labels=class_labels)
+                loss = outputs.loss
+            scaler.scale(loss).backward()
+            grad_clip = float(cfg["train"].get("grad_clip_norm", 0.0))
+            if grad_clip > 0:
+                scaler.unscale_(optimizer)
+                torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
+            scaler.step(optimizer)
+            scaler.update()
+            scheduler.step()
+            meter.update(loss.item(), images.size(0))
+            if step % int(cfg["train"].get("print_freq", 50)) == 0:
+                lr = optimizer.param_groups[0]["lr"]
+                print(f"{timestamp()} epoch={epoch}/{epochs} step={step}/{len(train_loader)} loss={meter.avg:.5f} lr={lr:.8f}")
+
+        metrics = {"epoch": epoch, "train_loss": meter.avg}
+        if val_loader is not None:
+            miou, iou = validate(model, val_loader, cfg, device)
+            metrics.update({"val_miou": miou, "val_iou": iou.tolist()})
+            print(f"{timestamp()} mask2former val {format_metrics(iou, miou, cfg['class_names'])}")
+            is_best = miou > best_miou
+            best_miou = max(best_miou, miou)
+        else:
+            is_best = True
+
+        state = {
+            "model": model.state_dict(),
+            "epoch": epoch,
+            "best_miou": best_miou,
+            "config": cfg,
+            "metrics": metrics,
+        }
+        torch.save(state, work_dir / "latest.pth")
+        if is_best:
+            torch.save(state, work_dir / "best.pth")
+            model.save_pretrained(work_dir / "best_hf")
+            print(f"{timestamp()} saved {work_dir / 'best.pth'} and {work_dir / 'best_hf'}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/coral_seg/tools/visualize_results.py b/coral_seg/tools/visualize_results.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab6723ae400b561902dbe4fa6635f8832c8d5cf7
--- /dev/null
+++ b/coral_seg/tools/visualize_results.py
@@ -0,0 +1,102 @@
+import argparse
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+
+PALETTE = np.array(
+    [
+        [0, 0, 0],
+        [0, 162, 232],
+        [240, 110, 170],
+        [255, 242, 0],
+    ],
+    dtype=np.uint8,
+)
+
+
+def label_to_color(mask: np.ndarray) -> np.ndarray:
+    mask = np.asarray(mask)
+    if mask.ndim == 3:
+        return mask.astype(np.uint8)
+    mask = np.clip(mask, 0, len(PALETTE) - 1)
+    return PALETTE[mask]
+
+
+def overlay(image: np.ndarray, color_mask: np.ndarray, alpha: float = 0.45) -> np.ndarray:
+    image = image.astype(np.float32)
+    color_mask = color_mask.astype(np.float32)
+    fg = color_mask.sum(axis=-1) > 0
+    out = image.copy()
+    out[fg] = image[fg] * (1.0 - alpha) + color_mask[fg] * alpha
+    return np.clip(out, 0, 255).astype(np.uint8)
+
+
+def add_title(arr: np.ndarray, title: str) -> np.ndarray:
+    h, w = arr.shape[:2]
+    canvas = np.full((h + 28, w, 3), 255, dtype=np.uint8)
+    canvas[28:] = arr
+    img = Image.fromarray(canvas)
+    draw = ImageDraw.Draw(img)
+    draw.text((8, 7), title, fill=(0, 0, 0))
+    return np.array(img)
+
+
+def read_rgb(path: Path) -> np.ndarray:
+    return np.array(Image.open(path).convert('RGB'))
+
+
+def read_mask(path: Path) -> np.ndarray:
+    arr = np.array(Image.open(path))
+    if arr.ndim == 3:
+        return arr[:, :, :3]
+    return arr.astype(np.uint8)
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--image-dir', default='/root/autodl-tmp/coral/data_fresh/test_raw/PrecisionLabel800/images')
+    parser.add_argument('--pred-dir', default='/root/autodl-tmp/coral/coral_seg/submissions/results')
+    parser.add_argument('--gt-dir', default='')
+    parser.add_argument('--out-dir', default='/root/autodl-tmp/coral/coral_seg/vis_results')
+    parser.add_argument('--num', type=int, default=24)
+    parser.add_argument('--alpha', type=float, default=0.45)
+    args = parser.parse_args()
+
+    image_dir = Path(args.image_dir)
+    pred_dir = Path(args.pred_dir)
+    gt_dir = Path(args.gt_dir) if args.gt_dir else None
+    out_dir = Path(args.out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    pred_paths = sorted(pred_dir.glob('*.png'))[: args.num]
+    if not pred_paths:
+        raise RuntimeError(f'No prediction PNG files found in {pred_dir}')
+
+    for pred_path in pred_paths:
+        image_path = image_dir / pred_path.name
+        if not image_path.exists():
+            raise FileNotFoundError(image_path)
+        image = read_rgb(image_path)
+        pred = read_mask(pred_path)
+        pred_color = label_to_color(pred)
+        panels = [
+            add_title(image, 'image'),
+            add_title(pred_color, 'prediction'),
+            add_title(overlay(image, pred_color, args.alpha), 'pred overlay'),
+        ]
+        if gt_dir is not None:
+            gt_path = gt_dir / pred_path.name
+            if gt_path.exists():
+                gt = read_mask(gt_path)
+                gt_color = label_to_color(gt)
+                panels.append(add_title(gt_color, 'gt'))
+                panels.append(add_title(overlay(image, gt_color, args.alpha), 'gt overlay'))
+        canvas = np.concatenate(panels, axis=1)
+        Image.fromarray(canvas).save(out_dir / pred_path.name)
+    print(f'saved {len(pred_paths)} visualizations to {out_dir}')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/coral_seg/vis_results/image_0001.png b/coral_seg/vis_results/image_0001.png
new file mode 100644
index 0000000000000000000000000000000000000000..b4d1cacb2848a16332399c83d1a248b8488d75fb
--- /dev/null
+++ b/coral_seg/vis_results/image_0001.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5a699870ed3aa0f710b8c4b72c18e42696701cc6f5b70f3b2c5f87dc2e6dbe03
+size 233850
diff --git a/coral_seg/vis_results/image_0002.png b/coral_seg/vis_results/image_0002.png
new file mode 100644
index 0000000000000000000000000000000000000000..564f57502e30967c18cff654049e4d3712c6fe3e
--- /dev/null
+++ b/coral_seg/vis_results/image_0002.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a0a3988af4e382864b8d05622145fb74f3cfc7bcfa24e923bc99a516880c313
+size 225935
diff --git a/coral_seg/vis_results/image_0003.png b/coral_seg/vis_results/image_0003.png
new file mode 100644
index 0000000000000000000000000000000000000000..db84b47ebdc79d54c64e0506ce22343a0260d4f1
--- /dev/null
+++ b/coral_seg/vis_results/image_0003.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:750db3dc808ebf30e3314a031d00c077685444c039451d5cf21987b3ff36122c
+size 159745
diff --git a/coral_seg/vis_results/image_0004.png b/coral_seg/vis_results/image_0004.png
new file mode 100644
index 0000000000000000000000000000000000000000..28579a9139937ab41bc0ce88b5cb402b54c425f6
--- /dev/null
+++ b/coral_seg/vis_results/image_0004.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e4ec5fb7a9c53aadebe644bd11813d49c4480b69510364ed7161708c088246df
+size 206640
diff --git a/coral_seg/vis_results/image_0005.png b/coral_seg/vis_results/image_0005.png
new file mode 100644
index 0000000000000000000000000000000000000000..9851242f9b3bd69b876d636648b20fa64d4ca8ea
--- /dev/null
+++ b/coral_seg/vis_results/image_0005.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:38539a27f204b830bda150a29e283994a4903879ef3bafe865b85460ad72ad63
+size 153569
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