{"id":20199107,"url":"https://github.com/csoren66/industrial-defect-detection","last_synced_at":"2025-03-03T08:26:12.700Z","repository":{"id":262407751,"uuid":"885802148","full_name":"csoren66/Industrial-Defect-Detection","owner":"csoren66","description":"This project implements a deep learning model to automatically detect defects in industrial equipment through image classification. 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The model classifies equipment images into 'defective' and 'non-defective' categories.\n\n## Project Structure\n```\nindustrial-defect-detection/\n├── dataset/\n│   ├── casting_data/casting_data/train/                  # Training image dataset\n│   └── /casting_data/casting_data/test/                  # Testing image dataset\n├── models/                   # Saved model checkpoints\n├── notebooks/               \n│   ├── Industrial_Image_Detection.ipynb\n├── requirements.txt\n└── README.md\n```\n\n## Requirements\n- opencv-python\n- pandas\n- numpy\n- seaborn\n- matplotlib\n- holoviews\n- tensorflow\n- scikit-learn\n- shap\n- json\n\nInstall dependencies:\n```bash\npip install -r requirements.txt\n```\n\n## Dataset\nThe dataset consists of industrial equipment images divided into:\n- Defective\n- Non-defective\n\n\n## Model Architecture\nThe project uses a CNN-based architecture with:\n```\nModel: \"sequential\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n conv2d (Conv2D)             (None, 150, 150, 16)      800       \n                                                                 \n max_pooling2d (MaxPooling2  (None, 75, 75, 16)        0         \n D)                                                              \n                                                                 \n conv2d_1 (Conv2D)           (None, 75, 75, 32)        4640      \n                                                                 \n max_pooling2d_1 (MaxPoolin  (None, 37, 37, 32)        0         \n g2D)                                                            \n                                                                 \n conv2d_2 (Conv2D)           (None, 37, 37, 64)        18496     \n                                                                 \n max_pooling2d_2 (MaxPoolin  (None, 18, 18, 64)        0         \n g2D)                                                            \n                                                                 \n flatten (Flatten)           (None, 20736)             0         \n                                                                 \n dense (Dense)               (None, 224)               4645088   \n                                                                 \n dropout (Dropout)           (None, 224)               0         \n                                                                 \n dense_1 (Dense)             (None, 1)                 225       \n                                                                 \n=================================================================\nTotal params: 4669249 (17.81 MB)\nTrainable params: 4669249 (17.81 MB)\nNon-trainable params: 0 (0.00 Byte)\n_________________________________________________________________\n```\n\n## Contributing\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/AmazingFeature`)\n3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)\n4. Push to the branch (`git push origin feature/AmazingFeature`)\n5. Open a Pull Request\n\n## License\nThis project is licensed under the MIT License - see the LICENSE file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcsoren66%2Findustrial-defect-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcsoren66%2Findustrial-defect-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcsoren66%2Findustrial-defect-detection/lists"}