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Fast Real-time Object Detection with High-Res Output https://x.com/_akhaliq/status/1840213012818329826 https://x.com/githubprojects/status/1891370506537910724 https://www.threads.net/@githubprojects/post/DGKdoE4zdUX
| Date | Stars |
|---|---|
| 2026-07-24 | 624 |
| 2026-07-25 | 624 |
| 2026-07-28 | 625 |
| 2026-07-30 | 625 |
| 2026-08-06 | 625 |
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# Fast Real-time Object Detection with High-Res Output
[](https://huggingface.co/spaces/Sanshruth/CCTV_YOLO?embed=true)
## Overview
Live stream of Las Vegas sidewalk traffic cam, processed with YOLOv5n6 on low-resolution frames, with results drawn on high-resolution frames.
This project demonstrates **real-time object detection** using the YOLOv5n6 model with **low-resolution inference** for high-speed processing, while drawing the results on **high-resolution frames**. The object detection pipeline is deployed as a Gradio app and streams live data from an external camera feed.
### Features
- **YOLOv5n6 Model**: Pre-trained object detection model optimized for speed and accuracy.
- **Low-resolution Processing**: Efficiently processes frames in low resolution (320x180) while mapping results to high-res images.
- **Gradio Interface**: Interactive Gradio interface with real-time video stream processing.
- **CUDA Support**: Optimized for CUDA-enabled GPUs, ensuring fast inference times.
### Model Details
- **Model**: YOLOv5n6 (`yolov5n6.pt`)
- **Confidence Threshold**: 0.25
- **IOU Threshold**: 0.45
- **Max Detections**: 100 objects per frame
### How It Works
The pipeline processes a live video stream, detecting objects in each frame using YOLOv5n6. Bounding boxes are drawn on the high-resolution frames based on detections from the low-resolution inference.
### Usage
1. Clone the repository and install the dependencies:
```bash
git clone https://github.com/SanshruthR/CCTV_YOLO.git
cd cctv-yolo
pip install -r requirements.txt
```
2. Run the script:
```bash
python app.py
```
3. Access the Gradio interface and view the live stream processed with YOLOv5n6.
### Deployment
This project is deployed on **Hugging Face Spaces**. You can interact with the app via the following link:
[Live Demo on Hugging Face](https://huggingface.co/spaces/Sanshruth/CCTV_YOLO?embed=true)
### License
This project is licensed under the MIT License.
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c7c8f11e1dd90ba3, topic:yolo, desc:object detection, readme:object detection
matched fp:c7c8f11e1dd90ba3, topic:gradio