Skip to content

README.md

Latest commit

 

History

History
214 lines (153 loc) · 6.79 KB

README.md

File metadata and controls

214 lines (153 loc) · 6.79 KB

🥭 Mango Ripeness Classification with GradCam

Python TensorFlow License Accuracy

This project implements deep learning models to classify six stages of mango ripeness using transfer learning and hybrid architectures. Additionally, Grad-CAM visualizations are used to interpret the decision-making process of the models.

📊 Overview

  • Dataset: 6,000 mango images (1,000 per class)
    • Training: 4,800 images
    • Validation: 600 images
    • Testing: 600 images
  • Image Size: 224x224 pixels
  • Batch Size: 64
  • Models Tested: 8 different architectures
  • Best Performance: 98.33% accuracy (XceptionLstm)
  • Tools: TensorFlow, Keras, scikit-learn, Grad-CAM

🎯 Ripeness Stages

The model classifies mangoes into six distinct stages:

  1. Unripe - Green, firm mangoes
  2. Early Ripe - Starting to show color changes
  3. Partially Ripe - Mixed green and yellow/red coloring
  4. Ripe - Optimal for consumption
  5. Overripe - Very soft, past prime
  6. Perished - Spoiled, not suitable for consumption

✨ Features

  • Data Augmentation: Rotation, shifts, zoom, and flips to improve model robustness
  • Smart Callbacks:
    • Model checkpointing for saving best weights
    • Early stopping to prevent overfitting
    • Learning rate reduction on plateau
  • Grad-CAM Visualizations: Interpret what the model "sees" when making predictions
  • Comprehensive Evaluation: Classification reports and confusion matrices

🚀 Installation

Clone the Repository

git clone https://github.com/your-username/mango-ripeness-classification.git
cd mango-ripeness-classification

Install Dependencies

pip install -r requirements.txt

Dataset

The dataset is already included in the repository under the data/ directory. No additional downloads required!

💻 Usage

Training Models

All model notebooks are located in the models/ folder. To train a model:

jupyter notebook models/

Available notebooks:

  • mangodensenet201.ipynb - DenseNet201
  • mangoinceptionv3.ipynb - InceptionV3
  • mangomobilenetv2.ipynb - MobileNetV2
  • mangoxception.ipynb - Xception
  • mangoxceptionlstm.ipynb - XceptionLSTM (Best performer)
  • mangocnn.ipynb - Custom CNN (EfficientNetV2B0)
  • mangovgg19.ipynb - VGG19
  • mangoresnet50.ipynb - ResNet50

Each notebook includes:

  • Model architecture setup
  • Training and validation
  • Grad-CAM visualization
  • Performance evaluation

📈 Model Performance

Accuracy Comparison

Model Accuracy Chart

Rank Model Test Accuracy Training Time Parameters Notes
🥇 XceptionLSTM 98.33% ~45 min 20M Best overall performance,Sequential modeling capability
🥈 DenseNet201 98.02% ~50 min 23M Dense connections
🥉 InceptionV3 98.10% ~40 min 24M Multi-scale feature extraction
4 MobileNetV2 98.00% ~30 min 3.5M Fastest inference, mobile-ready
5 Xception 98.00% ~42 min 23M Depthwise separable convolutions
6 CNN (EfficientNetV2B0) 97.00% ~35 min 7M Efficient compound scaling
7 VGG19 97.30% ~55 min 144M Classic architecture, very deep
8 ResNet50 92.00% ~38 min 26M Good baseline with residual connections

Confusion Matrix - Xception+Lstm (Best Model)

Results on test set (600 images):

Confusion Matrix

Key Observations:

  • Most confusion occurs between Early Ripe and Unripe stages (9 misclassifications)
  • Ripe stage has perfect classification (100%)
  • Overall accuracy: 98.33%
  • Diagonal dominance indicates strong classification performance across all classes

🛠️ Requirements

Create a requirements.txt file with:

tensorflow>=2.8.0
keras>=2.8.0
scikit-learn>=1.0.0
numpy>=1.21.0
pandas>=1.3.0
opencv-python>=4.5.0
matplotlib>=3.4.0
seaborn>=0.11.0

📁 Project Structure

mango-ripeness-classification/
├── data/
│   ├── train/
│   ├── validation/
│   └── test/
├── models/
│   ├── mangodensenet201.ipynb
│   ├── mangoinceptionv3.ipynb
│   ├── mangomobilenetv2.ipynb
│   ├── mangoxception.ipynb
│   ├── mangoxceptionlstm.ipynb
│   ├── mangocnn.ipynb
│   ├── mangovgg19.ipynb
│   └── mangoresnet50.ipynb
├── saved_models/
├── visualizations/
├── requirements.txt
├── LICENSE
└── README.md

🎨 Grad-CAM Visualizations

Grad-CAM (Gradient-weighted Class Activation Mapping) helps visualize which parts of the mango image the model focuses on when making predictions. This provides interpretability and helps validate that the model is learning relevant features.

image image image image image image

🤝 Contributing

Contributions are welcome! Here's how you can help:

  1. Fork the repository
  2. Create a new branch: git checkout -b feature-name
  3. Make your changes and commit: git commit -m "Add feature"
  4. Push to the branch: git push origin feature-name
  5. Submit a pull request

Please ensure your code follows the existing style and includes appropriate tests.

📄 License

This project is licensed under the MIT License - feel free to modify and distribute.

See the LICENSE file for details.

🙏 Acknowledgments

  • Kaggle Community for datasets and resources
  • xAI for inspiration and tools
  • TensorFlow & Keras Teams for excellent deep learning frameworks
  • Transfer Learning research community for pre-trained models

📧 Contact

For questions or feedback, please open an issue or reach out through GitHub.

🔮 Future Work

  • Mobile app deployment
  • Real-time video classification
  • Additional fruit types
  • Model quantization for edge devices
  • Web-based demo interface

⭐ If you find this project useful, please consider giving it a star! ⭐