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