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This project uses PyTorch and computer vision techniques to analyze fabric textures and patterns.\n\n## Features\n\n- Fabric classification using deep learning\n- Texture and pattern analysis\n- Color analysis\n- Pre-trained model support\n- Training and inference capabilities\n- Checkpoint saving and loading\n\n## Requirements\n\n- Python 3.x\n- PyTorch \u003e= 2.0.0\n- torchvision \u003e= 0.15.0\n- Pillow \u003e= 9.0.0\n- numpy \u003e= 1.21.0\n- opencv-python \u003e= 4.5.0\n- scikit-learn \u003e= 1.0.0\n\n## Installation\n\n1. Clone this repository:\n\n```bash\ngit clone git@github.com:sinarck/tessera.git\ncd tessera\n```\n\n2. Install the required dependencies:\n\n```bash\npip install -r requirements.txt\n```\n\n## Project Structure\n\n```\n.\n├── Fabrics/              # Directory containing fabric images\n├── checkpoints/         # Directory for saving model checkpoints\n├── fabric_classifier.py # Main training and model definition\n├── predict_fabric.py    # Script for making predictions\n├── requirements.txt     # Project dependencies\n└── best_fabric_classifier.pth # Pre-trained model weights\n```\n\n## Usage\n\n### Training\n\nTo train the model:\n\n```bash\npython fabric_classifier.py\n```\n\nThe script will:\n\n- Load and preprocess the fabric images\n- Train the model\n- Save checkpoints during training\n- Evaluate the model on validation data\n\n### Prediction\n\nTo make predictions on new fabric images:\n\n```bash\npython predict_fabric.py --image path/to/your/image.jpg\n```\n\n## Model Architecture\n\nThe project uses a custom neural network architecture based on PyTorch, with the following features:\n\n- Convolutional layers for feature extraction\n- Fully connected layers for classification\n- Support for transfer learning\n- Custom data augmentation\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsinarck%2Ftessera","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsinarck%2Ftessera","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsinarck%2Ftessera/lists"}