This repository contains a Gradio app that allows users to upload a video and detects if there was an accident in the video. The model behind this application is based on the ResNet-50 architecture and has undergone several optimization processes, to ensure swift and accurate detections.
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Initial Training with ResNet-50:
- Trained on ResNet-50 architecture for 5 epochs.
- Utilized resources available on Intel Dev Cloud.
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Optimization with IPEX:
- Optimized the model using Intel's PyTorch extension, IPEX, to improve the performance on Intel hardware.
- Continued training the IPEX-optimized model for another 15 epochs.
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Conversion to ONNX:
- Converted the PyTorch model to ONNX format to make it compatible with a variety of platforms.
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Optimization with OpenVINO:
- Used Intel's OpenVINO toolkit to further optimize the ONNX model for faster inferencing.
The Gradio app provides an intuitive interface for users to:
- Upload a video.
- Process the video through the optimized model.
- Get a feedback on whether an accident was detected in the uploaded video.
- If over 10% of the video frames consisted of an accident, "Accident" will be declared, else "No Accident".
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Clone the Repository:
git clone git@github.com:SSKlearns/IntelOneAPI.git cd IntelOneAPI -
Run the Gradio App:
python app.py
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Use the App:
- Open the provided link in your browser.
- Upload a video and wait for the model to process it.
- Review the results to see if an accident was detected.
Feel free to submit issues, fork the repository and send pull requests!
This project is licensed under the terms of the MIT license.