---
title: Fish Freshness Classifier
emoji: 🐟
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.19.2
app_file: fish_freshness_app.py
pinned: false
---

# 🐟 Food Freshness Classification Using Deep Learning

This project aims to classify food images as **Fresh** or **Not Fresh** using deep learning and transfer learning with PyTorch. We explore various CNN architectures and hybrid ensemble models to achieve state-of-the-art accuracy.

---

## 📁 Project Structure

├── models/
│ ├── efficientnet_b0.py
│ ├── efficientnet_b4.py
│ ├── mobilenetv2.py
│ ├── resnet50.py
│ └── hybrid_fusion.py
│
├── train/
│ ├── train_efficientnet.py
│ ├── train_resnet50.py
│ ├── train_mobilenetv2.py
│ ├── train_efficientnet_b4.py
│ └── train_hybrid_fusion.py
│
├── evaluate/
│ ├── evaluate_efficientnet.py
│ ├── evaluate_resnet50.py
│ ├── evaluate_mobilenetv2.py
│ ├── evaluate_efficientnet_b4.py
│ ├── evaluate_hybrid_fusion.py
│ └── compare_fish_models.py
│
├── utils/
│ └── dataset_loader.py
│
├── data/
│ ├── train_paths.npy
│ ├── train_labels.npy
│ ├── val_paths.npy
│ ├── val_labels.npy
│ ├── test_paths.npy
│ └── test_labels.npy
│
├── results/
│ ├── eval_metrics_.png
│ ├── confusion_matrix_.png
│ ├── model_metrics_summary.csv
│ └── *.pth (saved models)
│
├── requirements.txt
└── README.md

---

## 🧠 Model Architectures

### ✅ Individual Models:
- **EfficientNetB0**
- **EfficientNetB4**
- **MobileNetV2**
- **ResNet50**

Each uses transfer learning:
- Initial training with `train_base=False` (frozen feature extractor)
- Followed by fine-tuning the deeper layers

### 🔁 Hybrid Model:
**EnhancedHybridFusionClassifier**
- Combines EfficientNetB0, ResNet50, and MobileNetV2
- Fuses feature embeddings via concatenation
- Classifier head with multiple dense layers
- Fine-tuned after head training

---

## 🧪 Evaluation and Comparison

Each model is evaluated on the same test set.

### Metrics:
- Accuracy
- Precision
- Recall
- F1 Score
- AUC (ROC)

### Visual Outputs:
- `confusion_matrix_*.png`
- `eval_metrics_*.png`
- ROC curves and metric bar charts (`compare_fish_models.py`)

Results are also exported to:
results/model_metrics_summary.csv

---

## 🏗️ Training Details

- Optimizer: `Adam`
- Loss: `BCELoss`
- Scheduler: `ReduceLROnPlateau` (monitors val accuracy)
- Early stopping via logic inside training script
- Data augmentation during training:
  - Random horizontal flip
  - Rotation
  - Color jitter
- `num_workers` optimized based on CPU
- `pin_memory=True` for faster GPU transfer

---

## 💾 Setup & Requirements

1. Clone the repository:
```bash
git clone <your-repo-url>
cd <repository-name>
```

2. Create and activate virtual environment:
```bash
# Windows
setup.bat

# Or manually:
python -m venv venv
venv\Scripts\activate  # Windows
source venv/bin/activate  # Linux/Mac
pip install -r requirements.txt
```

Requirements:
- Python 3.8+
- PyTorch 2.0+
- CUDA-compatible GPU (recommended)
- See requirements.txt for full list

## 🚀 How to Run

1. Train a Model:
```bash
python train/train_efficientnet.py
```

2. Evaluate:
```bash
python evaluate/evaluate_efficientnet.py
```

3. Compare All Models:
```bash
python evaluate/compare_fish_models.py
```

## 🏁 Results Summary

| Model | Accuracy | Precision | Recall | F1 |
|-------|----------|-----------|--------|-------|
| EfficientNetB0 | 0.9819 | 0.9652 | 0.9978 | 0.9812 |
| EfficientNetB4 | 0.9797 | 0.9733 | 0.9843 | 0.9788 |
| MobileNetV2 | 0.9562 | 0.9410 | 0.9685 | 0.9546 |
| ResNet50 | 0.9765 | 0.9648 | 0.9865 | 0.9756 |
| HybridFusion | 0.9755 | 0.9627 | 0.9865 | 0.9745 |

## 📌 Notes

- Training logs are printed per epoch with loss/val accuracy
- Best models are saved as *_best.pth
- Hybrid model training uses transfer learning and feature fusion
- Comparison across architectures and training strategy is fair (same data, same pipeline)

## 🏆 Final Thoughts

This project shows the power of transfer learning and hybrid deep learning models in food quality assessment. The modular pipeline supports extension (e.g., Grad-CAM, more ensembling) and can serve as a template for similar classification tasks.

## Acknowledgments

- [Segment Anything Model (SAM)](https://github.com/facebookresearch/segment-anything)
- [EfficientNet Implementation](https://github.com/lukemelas/EfficientNet-PyTorch)