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Project 2 - Bean Disease Classification and Model Comparison

This project fulfills the assignment requirements by providing:

  • 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.

Project Structure 

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/

Dataset Description

This project classifies bean leaf diseases into three distinct categories using the Beans Dataset from Hugging Face:

  • 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

Dataset statistics:

  • Total images: ~1,400 training + 200 validation + 200 test
  • Classes: 3 disease types
  • Source: Hugging Face Datasets - beans
  • Resolution: Various sizes (automatically resized to 224×224)
  • Splits: Automatically loaded from HF with train/validation/test separate splits

Preprocessing

The training notebook applies the following transformations:

  1. Image Loading & Conversion

    • Load directly from Hugging Face Datasets
    • Convert all images to RGB
    • Verify dimensions are valid for ViT

  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

  3. Data Augmentation (Training only)

    • Random horizontal flips (50%)
    • Random crops to 224×224
    • Color jittering (brightness, contrast, saturation)

  4. Label Encoding

    • Automatic from HF dataset: angular_leaf_spot=0, bean_rust=1, healthy=2
    • Matching labels.txt

Model Architecture & Training

1. Custom Transfer Learning Model

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

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

Expected Performance:

  • Training accuracy: ~93-95%
  • Validation accuracy: ~88-92%
  • Test accuracy: ~85-90% (depending on dataset quality)

2. Open-Source Model: CLIP

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

3. Closed-Source Model: OpenAI Vision

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

Model Comparison Example

Input: Healthy Bean Leaf Image

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"

Input: Bean Rust Image

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"

Evaluation Output

Run:

python evaluate_models.py --examples-dir example_images --output results/model_comparison_results.csv

Generates CSV table with predictions from all three models for each test image.

Training Workflow

  1. Load dataset from Hugging Face (automatic in notebook)

    from datasets import load_dataset
dataset = load_dataset("beans")

  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/

  3. Test locally with the Gradio app

    python app.py
    • Upload bean leaf images
    • Get predictions from all three models
    • Compare results

  4. Evaluate and generate CSV

    python evaluate_models.py
    • Compares all three models on test set
    • Generates results/model_comparison_results.csv

Running the Project

Quickstart

  1. Install dependencies

    pip install -r requirements_runtime.txt

  2. Set up environment (optional for OpenAI API)

    cp .env.example .env
# Edit .env and add your OPENAI_API_KEY

  3. Run training (first time only)

    pip install -r requirements_training.txt
jupyter notebook training_custom_transfer_learning.ipynb

  4. Launch web app

    python app.py
    • Open browser to http://localhost:7860
    • Upload bean leaf images to get predictions

Advanced: Full Reproducibility 

# 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)

File Descriptions

  • 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)

API Keys & Environment

To use the OpenAI Vision model, you need a valid OpenAI API key:

  1. Create .env file

    cp .env.example .env

  2. Add your OpenAI API key

    OPENAI_API_KEY=sk-...

  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

Performance Considerations

  • 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

Deployment to Hugging Face Space

  1. Upload model to HF Hub

    # 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

  3. Push and Deploy

    git add .
git commit -m "Deploy bean disease classifier"
git push

Submission Checklist

  • 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

Troubleshooting

OpenAI model not working:

Out of GPU memory (training):

  • Reduce batch size to 8: per_device_train_batch_size=8
  • Reduce epochs to 3: num_train_epochs=3

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"

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

References

Last Updated: April 2026
Dataset: Beans (Hugging Face)
Framework: PyTorch + Transformers
Model: Vision Transformer (ViT-Base)