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https://github.com/bniladridas/deepvision
CIFAR-10 Image Classification with MobileNetV2
https://github.com/bniladridas/deepvision
data-science machine-learning python
Last synced: about 2 months ago
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CIFAR-10 Image Classification with MobileNetV2
- Host: GitHub
- URL: https://github.com/bniladridas/deepvision
- Owner: bniladridas
- License: mit
- Created: 2024-02-04T21:50:29.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-09-09T07:08:26.000Z (4 months ago)
- Last Synced: 2024-10-20T05:40:51.945Z (3 months ago)
- Topics: data-science, machine-learning, python
- Language: Python
- Homepage:
- Size: 2.83 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🚀 **DeepVision**: Cutting-Edge Image Classification with TensorFlow & Keras
## 🖼️ Overview
**DeepVision** represents the pinnacle of image classification, leveraging state-of-the-art deep learning frameworks—**TensorFlow** and **Keras**—to deliver robust and accurate predictions. Built on the powerful **MobileNetV2** architecture and trained on the renowned **CIFAR-10** dataset, this project is engineered to classify a wide spectrum of objects efficiently.
---
## ✨ Key Features
- **Deep Learning Excellence**: Implements TensorFlow and Keras for scalable model deployment.
- **Advanced Architecture**: MobileNetV2 ensures a perfect balance between performance and speed.
- **Diverse Dataset**: Trained on 60,000 32x32 color images from the CIFAR-10 dataset, spanning 10 distinct classes.
- **Customizable Training**: Train over 5 epochs with adjustable hyperparameters to fine-tune performance.
- **Comprehensive Evaluation**: Track metrics like test accuracy and loss for precise performance insights.
---
## 🚀 Getting Started
### 🔧 Prerequisites
- Python 3.x
- TensorFlow
- Matplotlib (optional for visualizations)
### 💻 Installation
1. **Clone the repository:**
```bash
git clone https://github.com/niladridas/deepvision.git
```
2. **Navigate to the project directory:**
```bash
cd deepvision
```
3. **Install dependencies:**
```bash
pip install -r requirements.txt
```
---
## 📂 Usage
1. **Run the classification script:**
```bash
python src.py
```
2. **Monitor training progress and review test accuracy and optional visualizations.**
---
## 🔍 Preview
---
## 📊 Results
- **Training Accuracy**: ~75%
- **Validation Accuracy**: ~73%
- **Test Accuracy**: ~74%
---
## ⚖️ License
This project is open-source and licensed under the [MIT License](https://github.com/niladridas/deepvision?tab=MIT-1-ov-file).
---
## 🙌 Acknowledgments
- Heartfelt thanks to the incredible **TensorFlow** and **Keras** communities.
- CIFAR-10 dataset: [Access Here](https://www.cs.toronto.edu/~kriz/cifar.html)
---
## 🤝 Contributing
Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request. For major changes, please discuss them in an issue first to ensure they align with the project's goals.
1. **Fork the repository**
2. **Create a new branch** (`git checkout -b feature-branch`)
3. **Commit your changes** (`git commit -m 'Add some feature'`)
4. **Push to the branch** (`git push origin feature-branch`)
5. **Open a pull request**
---
🌟 Contributions are welcome! Feel free to report issues, suggest improvements, or fork the repository to take this project to the next level.