https://github.com/prithivsakthiur/yolox-t4

Ultralytics, YOLO v8 - Computer Vision
https://github.com/prithivsakthiur/yolox-t4

computer-vision engine inference ultralytics video yolo yolov8

Last synced: 7 days ago
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Ultralytics, YOLO v8 - Computer Vision

• Host: GitHub
• URL: https://github.com/prithivsakthiur/yolox-t4
• Owner: PRITHIVSAKTHIUR
• Created: 2024-07-09T02:27:59.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-07-11T04:23:51.000Z (over 1 year ago)
• Last Synced: 2025-03-14T11:51:13.853Z (7 months ago)
• Topics: computer-vision, engine, inference, ultralytics, video, yolo, yolov8
• Language: Jupyter Notebook
• Homepage: https://colab.research.google.com/drive/1BKgFUfk2Me1cSPFmbtZSVCn_4cYImPO-?au
• Size: 15 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
## Ultralytics 8.2.51 YOLOv8 in Colab Demo 🍺

    Ultralytics dropped the YOLOv8 - #Ultralytics 8.2.51 🔥, YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.

![alt text](assets/3.png)

☑️ Seamless Real-Time Object Detection: Streamlit combined with YOLOv8 enables real-time object detection directly from your webcam feed. This allows for immediate analysis and insights, making it ideal for applications requiring instant feedback.

☑️Efficient Resource Utilization: YOLOv8 optimized algorithm ensure high-speed processing with minimal computational resources.

    from ultralytics import solutions

    solutions.inference()

    ### Make sure to run the file using command `streamlit run `

👉🏻Official Documentation:

Ultralytics YOLOv8 Documentation: Refer to the official YOLOv8 documentation for comprehensive guides and insights on various computer vision tasks and projects. 🔗 https://docs.ultralytics.com/ .

## Make Sure that Runtime Type is T4 GPU

![alt text](assets/t41.png)

## Ensure to all requirement types

![alt text](assets/t42.png)

## After running all the moudules the Gradio app is live

![alt text](assets/t43.png)

## Upload the Video to Infer

![alt text](assets/t444.png)

## The resultant Video Processing

![alt text](assets/t44.png)

## The Outcome of Video Processing

![alt text](assets/t4OUT.png)

## Demo Video 

![alt text](docs/YOLO.gif)

.

.

.@prithivsakthiur