https://github.com/xixu-me/fashion-mnist-cnn
CNN to classify Fashion-MNIST dataset images
https://github.com/xixu-me/fashion-mnist-cnn
cnn-classification fashion-mnist
Last synced: 7 months ago
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CNN to classify Fashion-MNIST dataset images
- Host: GitHub
- URL: https://github.com/xixu-me/fashion-mnist-cnn
- Owner: xixu-me
- License: gpl-3.0
- Created: 2025-01-12T11:14:39.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-01-12T11:27:41.000Z (9 months ago)
- Last Synced: 2025-01-12T12:29:45.195Z (9 months ago)
- Topics: cnn-classification, fashion-mnist
- Language: Python
- Homepage:
- Size: 86.9 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Fashion-MNIST CNN
A deep learning project implementing a Convolutional Neural Network (CNN) to classify Fashion-MNIST dataset images using PyTorch.
## Overview
This project implements a CNN model to classify fashion items from the Fashion-MNIST dataset. The model architecture includes three convolutional layers with batch normalization and dropout, followed by two fully connected layers.
## Features
- Custom CNN architecture optimized for Fashion-MNIST classification
- Training with early stopping mechanism
- Real-time training progress visualization
- Model checkpointing
- Prediction visualization with comparison to ground truth
- Comprehensive training history plots
## Project Structure
```plaintext
.
├── main.py # Main execution script
├── src/
│ ├── dataset.py # Data loading and preprocessing
│ ├── model.py # CNN model architecture
│ ├── train.py # Training loop implementation
│ ├── utils.py # Utility functions
│ └── visualize.py # Visualization functions
├── models/ # Saved model checkpoints
└── results/ # Training plots and visualizations
```
## Installation
1. Clone the repository:
```bash
git clone https://github.com/yourusername/fashion-mnist-classification.git
cd fashion-mnist-classification
```
2. Install dependencies:
```bash
pip install torch torchvision tqdm matplotlib
```
## Usage
Run the training script:
```bash
python main.py
```
The script will:
1. Download and prepare the Fashion-MNIST dataset
2. Train the CNN model
3. Generate training history plots
4. Save model checkpoints
5. Visualize predictions
## Model Architecture
The CNN architecture consists of:
- 3 Convolutional layers with increasing filter sizes (32, 64, 128)
- Batch Normalization after each convolution
- Dropout for regularization
- MaxPooling layers for dimensionality reduction
- 2 Fully connected layers (512 units, 10 output classes)
## Dataset
The Fashion-MNIST dataset includes 10 categories:
```python
class_labels = [
"T-shirt/top",
"Trouser",
"Pullover",
"Dress",
"Coat",
"Sandal",
"Shirt",
"Sneaker",
"Bag",
"Ankle boot",
]
```
## Results
Training results will be saved in the `results/` directory:
- `training_history.png`: Loss and accuracy curves
- `predictions.png`: Sample predictions with ground truth comparison
The following are the example results:
## License
Copyright © [Xi Xu](https://xi-xu.me). All rights reserved.
Licensed under the [GPL-3.0](LICENSE) license.