| # Yolov5 Real-time Inference using Streamlit |
| A web interface for real-time yolo inference using streamlit. It supports CPU and GPU inference, supports both images and videos and uploading your own custom models. |
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| <img src="output.gif" alt="demo of the dashboard" width="800"/> |
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| ### [Live Demo](https://moaaztaha-yolo-interface-using-streamlit-app-ioset2.streamlit.app/) |
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| ## Features |
| - **Caches** the model for faster inference on both CPU and GPU. |
| - Supports uploading model files (<200MB) and downloading models from URL (any size) |
| - Supports both images and videos. |
| - Supports both CPU and GPU inference. |
| - Supports: |
| - Custom Classes |
| - Changing Confidence |
| - Changing input/frame size for videos |
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| ## How to run |
| After cloning the repo: |
| 1. Install requirements |
| - `pip install -r requirements.txt` |
| 2. Add sample images to `data/sample_images` |
| 3. Add sample video to `data/sample_videos` and call it `sample.mp4` or change name in the code. |
| 4. Add the model file to `models/` and change `cfg_model_path` to its path. |
| ```bash |
| git clone https://github.com/moaaztaha/Yolo-Interface-using-Streamlit |
| cd Yolo-Interface-using-Streamlit |
| streamlit run app.py |
| ``` |
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| ### To-do Next |
| - [x] Allow model upload (file / url). |
| - [x] resizing video frames for faster processing. |
| - [ ] batch processing, processes the whole video and then show the results. |
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| ## References |
| https://discuss.streamlit.io/t/deploy-yolov5-object-detection-on-streamlit/27675 |
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