https://github.com/headless-start/data-augmentation-impact

This repository contains effect of Data Augmentation of Training Set during Model Training.
https://github.com/headless-start/data-augmentation-impact

augmented-images cuda data gpu keras matplotlib mnist opencv-python python3 tensorflow training-data

Last synced: 3 days ago
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This repository contains effect of Data Augmentation of Training Set during Model Training.

• Host: GitHub
• URL: https://github.com/headless-start/data-augmentation-impact
• Owner: headless-start
• License: mit
• Created: 2025-01-10T14:28:23.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2025-02-01T07:50:30.000Z (11 days ago)
• Last Synced: 2025-02-08T16:17:41.822Z (3 days ago)
• Topics: augmented-images, cuda, data, gpu, keras, matplotlib, mnist, opencv-python, python3, tensorflow, training-data
• Language: Jupyter Notebook
• Homepage:
• Size: 2.37 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Image Augmentation with TensorFlow  

## 📌 Project Overview  

This project demonstrates the impact of **image augmentation techniques** on model performance by training a neural network on the MNIST dataset. Key comparisons include model accuracy and generalization with/without augmentation.  

**Dataset**: MNIST.  

**Goal**: Evaluate how augmentation improves robustness and reduces overfitting in general Image classification tasks.

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## 🚀 Key Features  

1. **Image Augmentation Pipeline**:  

   - Adjustments: Horizontal flipping, grayscale conversion, saturation, brightness, rotation, and cropping.  

   - Real-time augmentation using TensorFlow’s `tf.image` module.  

2. **Optimized Dataset Preparation**:  

   - Normalization (`[0, 255]` → `[0, 1]`), caching, shuffling, and prefetching for GPU efficiency.  

3. **Deep Learning Model**:  

   - Architecture: 2 hidden layers (4096 neurons each, ReLU activation), output layer (10 neurons, softmax).  

   - Trained separately on augmented vs. raw data for performance comparison.  

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## 🔍 Findings  

- **Augmented Model**:  

  - **Accuracy**: 94.2% (train) vs. 95.8% (test)  

  - **Runtime**: 3s/epoch | **Memory**: 4GB (NVIDIA GPU).  

- **Baseline (No Augmentation)**:  

  - **Accuracy**: 99.1% (train) vs. 94.4% (test) 

  - **Runtime**: 3s/epoch | **Memory**: 3.8GB (NVIDIA GPU). 

- **Conclusion**:  

  - Augmentation improved test generalization by 1.4% while adding minimal computational overhead.  

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## 🛠 System Requirements  

### Dependencies  

- Python 3.8+  

- Libraries: `tensorflow`, `tensorflow-datasets`, `matplotlib`, `Pillow`  

- Hardware: GPU with cuDNN support (recommended)

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## 📄 License  

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.