Image Classification
Transformers
Safetensors
English
vit
vision
biology
ecology
phenology
plants
plant-phenology
leaf-phenology
iNaturalist
Eval Results (legacy)
Instructions to use phenobase/phenovisionL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phenobase/phenovisionL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="phenobase/phenovisionL") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("phenobase/phenovisionL") model = AutoModelForImageClassification.from_pretrained("phenobase/phenovisionL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "ViTForImageClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "encoder_stride": 16, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "green_leaves", | |
| "1": "colored_leaves", | |
| "2": "breaking_leaf_buds" | |
| }, | |
| "image_size": 224, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "label2id": { | |
| "green_leaves": 0, | |
| "colored_leaves": 1, | |
| "breaking_leaf_buds": 2 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "model_type": "vit", | |
| "num_attention_heads": 16, | |
| "num_channels": 3, | |
| "num_hidden_layers": 24, | |
| "num_labels": 3, | |
| "patch_size": 16, | |
| "pooler_act": "tanh", | |
| "pooler_output_size": 768, | |
| "problem_type": "multi_label_classification", | |
| "qkv_bias": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.52.4" | |
| } | |