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README.md

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-# 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.
-
-<img src="output.gif" alt="demo of the dashboard" width="800"/>
-
-### [Live Demo](https://moaaztaha-yolo-interface-using-streamlit-app-ioset2.streamlit.app/)
-
-
-## 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
-
-
-## 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
-```
-
-### 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.
-
-## References
-https://discuss.streamlit.io/t/deploy-yolov5-object-detection-on-streamlit/27675