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# Fashion MNIST Classifier Zoo 👕🎽👖
## Model Overview
Welcome to the Fashion MNIST Classifier Zoo! This model card showcases a collection of image classification models trained on the [Fashion MNIST dataset](https://github.com/zalandoresearch/fashion-mnist). Each model offers a unique approach to identifying articles of clothing from 28x28 grayscale images. Explore the models below to find the perfect fit for your fashion needs!
## Models
### CNN_Fashion_MNIST
- **Architecture:** A custom Convolutional Neural Network (CNN) designed for efficient feature extraction and classification.
- **Size:** 1.5 MB
- **Use Case:** Ideal for resource-constrained environments or applications requiring fast inference.
### VGG16_Fashion_MNIST
- **Architecture:** Implementation of the classic VGG16 architecture, leveraging its deep layers for robust feature learning.
- **Size:** 184 MB
- **Use Case:** Suitable for applications where high accuracy is paramount, even at the cost of increased computational complexity.
### Xception_Fashion_MNIST
- **Architecture:** Employs the Xception architecture, known for its efficient use of parameters and strong performance.
- **Size:** 279 MB
- **Use Case:** A good balance between accuracy and computational efficiency, making it suitable for a wide range of applications.
## Interactive Demo
Unfortunately, this model card is static, but imagine the possibilities! If this were interactive, you could:
1. **Upload your own fashion images** and see how each model classifies them.
2. **Compare the models' performance** on a held-out test set with interactive visualizations.
3. **Adjust confidence thresholds** to explore the trade-off between precision and recall.
## Intended Use
These models are intended for:
- Educational purposes: Learning about image classification and deep learning architectures.
- Benchmarking: Comparing the performance of different models on the Fashion MNIST dataset.
- Inspiration: Providing a starting point for building more sophisticated fashion recognition systems.
## How to Use
1. **Load the Model:** Use TensorFlow/Keras to load the `.keras` model file of your choice.
```
from tensorflow import keras
model = keras.models.load_model('VGG16_Fashion_MNIST.keras')
```
2. **Prepare Your Data:** Ensure your input data consists of 99x99x3 RGB images, preprocessed to match the model's expected input.
3. **Make Predictions:** Use the loaded model to predict the class of each image.
```
predictions = model.predict(your_test_data)
```
## Training
The `fashion_mnist.ipynb` notebook provides a complete guide to training these models from scratch. Follow the instructions in the notebook to:
1. **Load the Fashion MNIST dataset.**
2. **Preprocess the data.**
3. **Build and train the models.**
4. **Evaluate their performance.**
## Files
- `.gitattributes`: Specifies attributes for files in the repository.
- `CNN_Fashion_MNIST.keras`: Pre-trained CNN model.
- `VGG16_Fashion_MNIST.keras`: Pre-trained VGG16 model.
- `Xception_Fashion_MNIST.keras`: Pre-trained Xception model.
- `fashion_mnist.ipynb`: Jupyter Notebook for training and evaluation.
- `README.md`: This model card.
## Limitations and Future Directions
- **Dataset Bias:** The Fashion MNIST dataset is a simplified representation of real-world fashion images. Models trained on this dataset may not generalize well to more complex scenarios.
- **Limited Architectures:** This collection includes only a few popular architectures. Future work could explore more recent and advanced models.
- **No Interactive Demo:** As mentioned above, an interactive demo would greatly enhance the user experience.
## Author
[Harsh Maniya](https://huggingface.co/harshhmaniya)
[GitHUb](https://github.com/harshhmaniya)
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