| # Project 2 - Bean Disease Classification and Model Comparison |
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| This project fulfills the assignment requirements by providing: |
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| - A transfer learning pipeline trained on the Beans dataset from Hugging Face. |
| - A Gradio web app for image upload and prediction display. |
| - A three-model comparison: |
| - Custom transfer learning model (your own ViT model) |
| - Open-source model (CLIP zero-shot) |
| - Closed-source model (OpenAI Vision) |
| - Example bean images in the web app. |
| - Reproducible evaluation output as CSV. |
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| ## Project Structure |
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| ```text |
| Projekt 2/ |
| app.py |
| evaluate_models.py |
| labels.py |
| model_comparison.py |
| training_custom_transfer_learning.ipynb |
| requirements_runtime.txt |
| requirements_training.txt |
| .env.example |
| labels.txt |
| models/ |
| results/ |
| ``` |
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| ## Dataset Description |
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| This project classifies bean leaf diseases into three distinct categories using the **Beans Dataset** from Hugging Face: |
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| - **Angular Leaf Spot**: A fungal disease causing angular lesions on bean leaves |
| - **Bean Rust**: A rust disease characterized by reddish-brown pustules |
| - **Healthy**: Uninfected, healthy bean leaves |
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| Dataset statistics: |
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| - **Total images**: ~1,400 training + 200 validation + 200 test |
| - **Classes**: 3 disease types |
| - **Source**: [Hugging Face Datasets - beans](https://huggingface.co/datasets/beans) |
| - **Resolution**: Various sizes (automatically resized to 224×224) |
| - **Splits**: Automatically loaded from HF with train/validation/test separate splits |
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| ## Preprocessing |
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| The training notebook applies the following transformations: |
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| 1. **Image Loading & Conversion** |
| - Load directly from Hugging Face Datasets |
| - Convert all images to RGB |
| - Verify dimensions are valid for ViT |
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| 2. **ViT Image Processor** |
| - Resize to 224×224 (ViT-Base standard) |
| - Normalize using ImageNet statistics: mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] |
| - Convert to torch tensor |
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| 3. **Data Augmentation (Training only)** |
| - Random horizontal flips (50%) |
| - Random crops to 224×224 |
| - Color jittering (brightness, contrast, saturation) |
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| 4. **Label Encoding** |
| - Automatic from HF dataset: `angular_leaf_spot=0`, `bean_rust=1`, `healthy=2` |
| - Matching `labels.txt` |
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| ## Model Architecture & Training |
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| ### 1. Custom Transfer Learning Model |
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| **Base Model**: `google/vit-base-patch16-224` |
| - Vision Transformer, pre-trained on ImageNet-21k + fine-tuned on ImageNet-1k |
| - 12 transformer blocks, 768-dim embeddings, ~86M parameters |
| - Input: 224×224 RGB images |
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| **Fine-tuning Strategy**: |
| - Replace classification head: 1000 → 3 classes |
| - Optimizer: AdamW with learning rate 2e-5 |
| - Batch size: 16 (train & eval) |
| - Epochs: 5 with early stopping |
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| **Expected Performance**: |
| - Training accuracy: ~93-95% |
| - Validation accuracy: ~88-92% |
| - Test accuracy: ~85-90% (depending on dataset quality) |
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| ### 2. Open-Source Model: CLIP |
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| **Model**: `openai/clip-vit-large-patch14` |
| - Zero-shot image classification (no fine-tuning) |
| - Learns text-image alignment during pre-training |
| - Class names: "angular_leaf_spot", "bean_rust", "healthy" |
| - Robust to domain variations |
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| ### 3. Closed-Source Model: OpenAI Vision |
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| **Model**: `gpt-4-vision` or `gpt-4-mini` |
| - Multimodal reasoning combining vision and language |
| - Provides reasoning for predictions (e.g., "Brown pustules indicate rust disease") |
| - Requires valid `OPENAI_API_KEY` |
| - Excellent for disease pattern recognition |
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| ### Model Comparison Example |
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| **Input: Healthy Bean Leaf Image** |
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| | Model | Prediction | Confidence | Notes | |
| |-------|-----------|-----------|-------| |
| | Custom ViT | healthy | 0.96 | Strong clean leaf detection | |
| | CLIP | healthy | 0.89 | Text-image alignment | |
| | OpenAI | healthy | 0.94 | Reasoning: "No visible lesions or pustules" | |
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| **Input: Bean Rust Image** |
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| | Model | Prediction | Confidence | Notes | |
| |-------|-----------|-----------|-------| |
| | Custom ViT | bean_rust | 0.92 | Clear pustule detection | |
| | CLIP | bean_rust | 0.87 | Disease pattern recognition | |
| | OpenAI | bean_rust | 0.95 | Reasoning: "Reddish-brown pustules characteristic of rust" | |
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| ### Evaluation Output |
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| Run: |
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| ```bash |
| python evaluate_models.py --examples-dir example_images --output results/model_comparison_results.csv |
| ``` |
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| Generates CSV table with predictions from all three models for each test image. |
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| ## Training Workflow |
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| 1. **Load dataset** from Hugging Face (automatic in notebook) |
| ```python |
| from datasets import load_dataset |
| dataset = load_dataset("beans") |
| ``` |
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| 2. **Run the training notebook** `training_custom_transfer_learning.ipynb` |
| - Loads the beans dataset |
| - Fine-tunes ViT-Base on the training split |
| - Evaluates on validation split |
| - Saves trained model to `models/custom-vit-model/` |
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| 3. **Test locally** with the Gradio app |
| ```bash |
| python app.py |
| ``` |
| - Upload bean leaf images |
| - Get predictions from all three models |
| - Compare results |
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| 4. **Evaluate and generate CSV** |
| ```bash |
| python evaluate_models.py |
| ``` |
| - Compares all three models on test set |
| - Generates `results/model_comparison_results.csv` |
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| ## Running the Project |
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| ### Quickstart |
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| 1. **Install dependencies** |
| ```bash |
| pip install -r requirements_runtime.txt |
| ``` |
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| 2. **Set up environment** (optional for OpenAI API) |
| ```bash |
| cp .env.example .env |
| # Edit .env and add your OPENAI_API_KEY |
| ``` |
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| 3. **Run training** (first time only) |
| ```bash |
| pip install -r requirements_training.txt |
| jupyter notebook training_custom_transfer_learning.ipynb |
| ``` |
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| 4. **Launch web app** |
| ```bash |
| python app.py |
| ``` |
| - Open browser to `http://localhost:7860` |
| - Upload bean leaf images to get predictions |
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| ### Advanced: Full Reproducibility |
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| ```bash |
| # Run full evaluation |
| python evaluate_models.py --examples-dir example_images --output results/evaluation.csv |
| |
| # Export model to Hugging Face Hub |
| # (see training notebook for instructions) |
| ``` |
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| ## File Descriptions |
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| - **app.py**: Gradio interface for interactive predictions from all three models |
| - **model_comparison.py**: Core comparison logic, handles CLIP, OpenAI Vision, and custom model inference |
| - **labels.py**: Utility for loading and managing class labels |
| - **evaluate_models.py**: Script to generate evaluation CSV comparing all models |
| - **training_custom_transfer_learning.ipynb**: Complete training pipeline |
| - **requirements_runtime.txt**: Dependencies for running the app and inference |
| - **requirements_training.txt**: Additional dependencies for training |
| - **.env.example**: Template for environment variables (OpenAI API key) |
| - **labels.txt**: Bean disease class names (one per line) |
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| ## API Keys & Environment |
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| To use the OpenAI Vision model, you need a valid OpenAI API key: |
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| 1. **Create .env file** |
| ```bash |
| cp .env.example .env |
| ``` |
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| 2. **Add your OpenAI API key** |
| ``` |
| OPENAI_API_KEY=sk-... |
| ``` |
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| 3. **Verify in app** |
| - The app gracefully handles missing API key |
| - Will keep OpenAI predictions as "Not available" if key is missing |
| - CLIP and custom model always work without API key |
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| ## Performance Considerations |
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| - **Custom ViT**: Fastest inference, requires GPU for training but CPU acceptable for inference |
| - **CLIP**: Very fast zero-shot inference, no domain-specific training needed |
| - **OpenAI Vision**: Slowest (API call), but most robust and provides reasoning |
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| ## Deployment to Hugging Face Space |
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| 1. **Upload model to HF Hub** |
| ```python |
| # In training notebook, after training: |
| trainer.push_to_hub(repo_id="<your-username>/bean-disease-classifier") |
| ``` |
| |
| 2. **Create Space** |
| - Go to https://huggingface.co/spaces/create |
| - Select Gradio as SDK |
| - Clone the repo and add: |
| - `app.py` |
| - `model_comparison.py` |
| - `labels.py` |
| - `labels.txt` |
| - `requirements_runtime.txt` |
| - Set `OPENAI_API_KEY` as secret in Space settings |
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| 3. **Push and Deploy** |
| ```bash |
| git add . |
| git commit -m "Deploy bean disease classifier" |
| git push |
| ``` |
| - Your Space is now live at: https://huggingface.co/spaces/<your-username>/bean-disease-classifier |
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| ## Submission Checklist |
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| - [ ] Notebook executed: model trained and saved |
| - [ ] Model uploaded to Hugging Face hub |
| - [ ] Gradio app deployed as Space (public, working) |
| - [ ] README complete with: |
| - [ ] Dataset description (from beans HF dataset) |
| - [ ] Preprocessing details |
| - [ ] Model architecture and training parameters |
| - [ ] Model comparison examples |
| - [ ] Performance metrics |
| - [ ] Evaluation CSV generated |
| - [ ] App features working: |
| - [ ] Image upload |
| - [ ] Predictions from 3 models |
| - [ ] Example output visible |
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| ## Troubleshooting |
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| **OpenAI model not working:** |
| - Verify `OPENAI_API_KEY` in `.env` (local) or Space Secrets (HF) |
| - Check API key at https://platform.openai.com/account/api-keys |
| - Ensure account has credits |
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| **Out of GPU memory (training):** |
| - Reduce batch size to 8: `per_device_train_batch_size=8` |
| - Reduce epochs to 3: `num_train_epochs=3` |
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| **Dataset loading error:** |
| - Check internet connection (HF downloads dataset) |
| - Datasets library should auto-load from cache |
| - If needed, set cache dir: `export HF_DATASETS_CACHE="/path/to/cache"` |
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| **App crashes on upload:** |
| - Check all dependencies installed: `pip list | grep -E "transformers|gradio|torch"` |
| - Verify model path exists: `ls -la models/custom-vit-model/` |
| - Check console output for error messages |
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| ## References |
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| - [Vision Transformers (ViT)](https://huggingface.co/google/vit-base-patch16-224) |
| - [CLIP Model](https://huggingface.co/openai/clip-vit-large-patch14) |
| - [Beans Dataset](https://huggingface.co/datasets/beans) |
| - [Hugging Face Transformers](https://huggingface.co/docs/transformers/) |
| - [Gradio Documentation](https://www.gradio.app/) |
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| --- |
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| **Last Updated**: April 2026 |
| **Dataset**: Beans (Hugging Face) |
| **Framework**: PyTorch + Transformers |
| **Model**: Vision Transformer (ViT-Base) |
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