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https://github.com/headless-start/fashion-mnist-classifier
This repository contains Fashion MNIST Image Classification.
https://github.com/headless-start/fashion-mnist-classifier
cuda-programming gpu keras mnist-dataset object-detection opencv-python python3 tensorflow tensorflow-models
Last synced: 3 days ago
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This repository contains Fashion MNIST Image Classification.
- Host: GitHub
- URL: https://github.com/headless-start/fashion-mnist-classifier
- Owner: headless-start
- License: mit
- Created: 2024-08-24T18:00:22.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-02-01T06:31:53.000Z (11 days ago)
- Last Synced: 2025-02-08T16:17:42.036Z (3 days ago)
- Topics: cuda-programming, gpu, keras, mnist-dataset, object-detection, opencv-python, python3, tensorflow, tensorflow-models
- Language: Jupyter Notebook
- Homepage:
- Size: 176 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Fashion MNIST Image Classification
## 📌 Project Overview
This project demonstrates the **loading, preprocessing, and visualization** of the **Fashion MNIST dataset** using **TensorFlow** and **Keras**. The dataset consists of grayscale images of fashion items, each labeled with one of ten classes. The project prepares the dataset for training machine learning models to classify fashion items into their respective categories.
**Dataset**: Fashion MNIST
**Images**: 28x28 pixel grayscale images of fashion items (e.g., T-shirts, shoes, dresses).
**Classes**: 10 categories of clothing items:
1. T-shirt/top
2. Trouser
3. Pullover
4. Dress
5. Coat
6. Sandal
7. Shirt
8. Sneaker
9. Bag
10. Ankle boot
---
## 🚀 Key Features
1. **Loading the Dataset**:
- The Fashion MNIST dataset is loaded using the `keras.datasets.fashion_mnist` API.
- Training set: 60,000 images.
- Test set: 10,000 images.
2. **Preprocessing the Data**:
- Pixel values are normalized to the range [0, 1] by dividing by 255.
- Images are prepared for visualization and model input.
3. **Visualization**:
- Display the first image from the training dataset.
- Show a 5x5 grid of the first 25 training images with their corresponding labels.
4. **Labels**:
- Labels represent the type of clothing item in each image (e.g., 0: T-shirt/top, 1: Trouser, 2: Pullover, etc.).
---
## 🔍 How It Works
1. **Loading the Dataset**:
- The dataset is loaded using TensorFlow's built-in API.
2. **Preprocessing**:
- Normalize pixel values to improve model training efficiency.
3. **Visualization**:
- Display individual images and grids of images with labels for better understanding.
4. **First Image in the Dataset**:
- The first image from the training set is displayed with a color bar, showing raw pixel data.
5. **Grid of 25 Training Images**:
- A 5x5 grid of the first 25 training images is displayed, with each image labeled according to its class (e.g., T-shirt, trouser, dress).
---
## 🛠 System Requirements
### Dependencies
- Python 3.8+
- Libraries: `numpy`, `pandas`, `tensorflow`, `keras`
- Hardware: GPU with cuDNN support (recommended)
---
## 📄 License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.