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--- |
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title: GenVidBench - Video Action Recognition |
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emoji: 🎬 |
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colorFrom: blue |
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colorTo: purple |
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sdk: gradio |
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sdk_version: 5.47.2 |
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app_file: app.py |
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pinned: false |
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license: apache-2.0 |
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short_description: State-of-the-art video action recognition using MMAction2 |
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--- |
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# GenVidBench - Video Action Recognition |
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A powerful video analysis tool that uses state-of-the-art deep learning models to recognize actions and activities in videos. Built on top of MMAction2 framework with a user-friendly Gradio interface. |
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## 🚀 Features |
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- **Action Recognition**: Identify actions and activities in videos using TSN (Temporal Segment Networks) |
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- **Top-5 Predictions**: Get the most likely actions with confidence scores |
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- **Multiple Formats**: Support for MP4, AVI, MOV, and other video formats |
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- **Real-time Processing**: Fast inference optimized for web deployment |
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- **User-friendly Interface**: Clean and intuitive Gradio web interface |
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## 🎯 Model Details |
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This demo uses: |
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- **Model**: TSN (Temporal Segment Networks) with ResNet-50 backbone |
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- **Dataset**: Trained on Kinetics-400 dataset (400 action classes) |
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- **Framework**: MMAction2 (OpenMMLab) |
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- **Input**: RGB video frames |
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- **Output**: Top-5 action predictions with confidence scores |
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## 🛠️ Technical Stack |
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- **Backend**: Python, PyTorch, MMAction2 |
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- **Frontend**: Gradio |
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- **Video Processing**: OpenCV, Decord |
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- **Deployment**: Hugging Face Spaces |
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## 📖 How to Use |
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1. **Upload Video**: Click the upload area or drag and drop your video file |
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2. **Wait for Processing**: The model will analyze your video (usually takes a few seconds) |
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3. **View Results**: See the top 5 predicted actions with confidence scores |
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## 💡 Tips for Best Results |
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- **Video Length**: Shorter videos (under 30 seconds) process faster |
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- **Video Quality**: Clear, well-lit videos work best |
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- **Action Clarity**: Videos with clear, distinct actions yield better results |
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- **Supported Formats**: MP4, AVI, MOV, and other common video formats |
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## 🔬 Supported Actions |
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The model can recognize 400 different action classes from the Kinetics-400 dataset, including: |
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- Sports activities (basketball, soccer, tennis, etc.) |
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- Daily activities (cooking, cleaning, reading, etc.) |
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- Physical exercises (push-ups, jumping jacks, etc.) |
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- Musical activities (playing instruments, singing, etc.) |
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- And many more! |
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## 🏗️ Architecture |
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``` |
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Video Input → Frame Sampling → Feature Extraction → Classification → Top-5 Predictions |
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``` |
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## 📊 Performance |
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- **Accuracy**: State-of-the-art performance on Kinetics-400 |
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- **Speed**: Optimized for real-time inference |
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- **Memory**: Efficient GPU/CPU utilization |
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## 🤝 Contributing |
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This project is part of the GenVidBench framework. Contributions are welcome! |
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## 📄 License |
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This project is licensed under the Apache License 2.0 - see the LICENSE file for details. |
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## 🙏 Acknowledgments |
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- [MMAction2](https://github.com/open-mmlab/mmaction2) - The underlying framework |
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- [OpenMMLab](https://openmmlab.com/) - For the excellent computer vision tools |
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- [Hugging Face](https://huggingface.co/) - For the deployment platform |
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--- |
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**Note**: This is a demonstration of video action recognition capabilities. For production use, consider additional validation and error handling. |
