README.md

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. 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 GitHUb