https://github.com/sinarck/tessera
Fabric and textile classifier developed for the 2025 GYEC.
https://github.com/sinarck/tessera
ai competition deep-learning python
Last synced: 5 months ago
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Fabric and textile classifier developed for the 2025 GYEC.
- Host: GitHub
- URL: https://github.com/sinarck/tessera
- Owner: sinarck
- Created: 2025-04-13T00:28:29.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-13T18:58:13.000Z (over 1 year ago)
- Last Synced: 2025-07-07T04:08:05.198Z (about 1 year ago)
- Topics: ai, competition, deep-learning, python
- Language: Python
- Homepage:
- Size: 8.57 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Fabric Classifier
A deep learning-based fabric classification system that can identify and classify different types of fabrics from images. This project uses PyTorch and computer vision techniques to analyze fabric textures and patterns.
## Features
- Fabric classification using deep learning
- Texture and pattern analysis
- Color analysis
- Pre-trained model support
- Training and inference capabilities
- Checkpoint saving and loading
## Requirements
- Python 3.x
- PyTorch >= 2.0.0
- torchvision >= 0.15.0
- Pillow >= 9.0.0
- numpy >= 1.21.0
- opencv-python >= 4.5.0
- scikit-learn >= 1.0.0
## Installation
1. Clone this repository:
```bash
git clone git@github.com:sinarck/tessera.git
cd tessera
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Project Structure
```
.
├── Fabrics/ # Directory containing fabric images
├── checkpoints/ # Directory for saving model checkpoints
├── fabric_classifier.py # Main training and model definition
├── predict_fabric.py # Script for making predictions
├── requirements.txt # Project dependencies
└── best_fabric_classifier.pth # Pre-trained model weights
```
## Usage
### Training
To train the model:
```bash
python fabric_classifier.py
```
The script will:
- Load and preprocess the fabric images
- Train the model
- Save checkpoints during training
- Evaluate the model on validation data
### Prediction
To make predictions on new fabric images:
```bash
python predict_fabric.py --image path/to/your/image.jpg
```
## Model Architecture
The project uses a custom neural network architecture based on PyTorch, with the following features:
- Convolutional layers for feature extraction
- Fully connected layers for classification
- Support for transfer learning
- Custom data augmentation