---
license: mit
datasets:
- JayRay5/cyprus-fish-dataset
model-index:
- name: ConvNeXT-Tiny for Cyprus Fish Recognition
  results:
  - task:
      type: classification
    dataset:
      type: fish_classification
      name: Cyprus Fish Dataset
    metrics:
    - name: accuracy
      type: accuracy
      value: 0.95
      verified: true
base_model:
- facebook/convnext-tiny-224
tags:
- image-classification
- fish
- cyprus-fish
- vision
- biology
metrics:
- accuracy
library_name: transformers
---
# 🐟 Cyprus Fish Classifier (ConvNeXT Tiny)



This model is a fine-tuned version of **[facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224)** on the **[Cyprus Fish Dataset](https://huggingface.co/datasets/JayRay5/cyprus-fish-dataset)**. It is designed to classify fish species commonly found in the waters around Cyprus.

This model is part of a complete **End-to-End MLOps project** including CI/CD, Docker containerization, and a deployed FastAPI application.

* **💻 GitHub Repository:** [cyprus-fish-classifier](https://github.com/JayRay5/cyprus-fish-classifier)
* **🚀 Live Demo:** [Hugging Face Space](https://huggingface.co/spaces/JayRay5/Cyprus-Fish-Recognition-App)

## 📊 Model Details

* **Model Architecture:** ConvNext Tiny (28M parameters)
* **Task:** Image Classification (Multi-class)
* **Input Image Size:** 224x224 pixels
* **Training Framework:** PyTorch & Hugging Face Transformers
* **Finetuning Strategy:** Full finetuning of the classification head, backbone frozen


## 🚀 Get Started with the Model

Make sure your transformers>=4.57.3

```bash
from transformers import ConvNextImageProcessor,AutoImageProcessor,AutoModelForImageClassification
from PIL import Image

processor = AutoImageProcessor.from_pretrained("JayRay5/convnext-tiny-224-cyprus-fish-cls")
model = AutoModelForImageClassification.from_pretrained("JayRay5/convnext-tiny-224-cyprus-fish-cls")

image = Image.open("path_to_your_image/image.png").convert("RGB")
inputs = (
        processor(images=image, return_tensors="pt").to(model.device).to(model.dtype)
    )

with torch.inference_mode():
        outputs = model(**inputs)

id2label = model.config.id2label
results = {}

for idx, prob in enumerate(probs):
    idx_int = idx
    label_name = id2label[idx_int]

    results[label_name] = float(prob)

print(results)
```

## Training Details

### Training Data

Training data can be found at [Cyprus Fish Dataset](https://huggingface.co/datasets/JayRay5/cyprus-fish-dataset).

### Training Procedure

The hyperparameters have been validated using a k-fold validation strategy. <br>
For more details about the training process, refer to the [GitHub repository](https://github.com/JayRay5/cyprus-fish-classifier).

#### Preprocessing 

- **Random Horizontal Flip** (p=0.5)
- **Random Rotation** (±15 degrees)
- **Color Jitter** (Brightness & Contrast ±20%)


#### Training Hyperparameters

- k_folds: 5
- batch_size: 32   
- grad_acc: 1
- epochs: 50
- lr: 3e-4
- scheduler: "constant"
- device: "cuda"
- fp16: True
- freeze_backbone: True


## Evaluation

#### Factors

As the test set is small, the evaluation may be biased.


#### Metrics

Accuracy: 0.95


### Out-of-Scope Use

The [Cyprus Fish Dataset](https://huggingface.co/datasets/JayRay5/cyprus-fish-dataset) is a non-exhaustive dataset. 

Thus, the model will not be able to classify species that are not in the dataset.

## Contact

rayane.bencharef.1@ens.etsmtl.ca