Visualization of detection

[DoSquared]

Search before asking

• I have searched the YOLOv5 issues and discussions and found no similar questions.

Question

Hello

I used the argument "--visualize" with detect.py and got output feature maps from YOLOv5.

I was wondering how can I visualize other outputs from the detection like for example class prediction maps or whatever there is to visualize from the detection!

Additional

No response

[DoSquared]

How to get outputs from detection visualization similar to the image below.

[github-actions]

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

Access additional YOLOv5 🚀 resources:

• Wiki – https://github.com/ultralytics/yolov5/wiki
• Tutorials – https://docs.ultralytics.com/yolov5
• Docs – https://docs.ultralytics.com

Access additional Ultralytics ⚡ resources:

• Ultralytics HUB – https://ultralytics.com/hub
• Vision API – https://ultralytics.com/yolov5
• About Us – https://ultralytics.com/about
• Join Our Team – https://ultralytics.com/work
• Contact Us – https://ultralytics.com/contact

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐!

[glenn-jocher]

@DoSquared hi there,

Glad to see you're interested in visualizing YOLOv5 detection outputs! You can visualize various detection outputs using --visual. This will generate images with bounding boxes, but if you'd like more detailed visualizations such as class prediction maps, you can consider post-processing the detection outputs and creating custom visualizations using the provided bounding box coordinates and confidence scores.

For more specific visualizations, you may need to leverage external libraries like OpenCV or Matplotlib to render custom visualizations using the bounding boxes and class predictions from the YOLOv5 detections.

Hope this helps! If you have further questions, feel free to ask.