ultralytics / xview-yolov3

xView 2018 Object Detection Challenge: YOLOv3 Training and Inference.
https://docs.ultralytics.com
GNU Affero General Public License v3.0
256 stars 57 forks source link

How to merge tiled inference results? #24

Closed ok4d4 closed 3 years ago

ok4d4 commented 3 years ago

Question

Hello, how do you merge the inference results in the NMS method?

github-actions[bot] commented 3 years ago

Hello @ok4d4, thank you for your interest in our work! Ultralytics has publicly released YOLOv5 at https://github.com/ultralytics/yolov5, featuring faster, lighter and more accurate object detection. YOLOv5 is recommended for all new projects.

If this is a bug report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you.

If this is a custom model or data training question, please note that Ultralytics does not provide free personal support. As a leader in vision ML and AI, we do offer professional consulting, from simple expert advice up to delivery of fully customized, end-to-end production solutions for our clients, such as:

For more information please visit https://www.ultralytics.com.

glenn-jocher commented 3 years ago

@ok4d4 small objects detected twice in overlapping regions of tiles are automatically merged in NMS. There is nothing special required, simply normal NMS.

ok4d4 commented 3 years ago

Isn't there a possibility that objects may not be detected if they are on the boundary of the grid?

glenn-jocher commented 3 years ago

@ok4d4 there's a possibility that objects may not be detected at all locations in the image.

ok4d4 commented 3 years ago

Of course you're right. When cropping an image, should I overlap parts of it?

glenn-jocher commented 3 years ago

@ok4d4 yes that's what I mean, if you overlap for example 10% each direction on a 640 crop, then all objects smaller than 64 pixels across should be detected at least once without any breaks. Larger objects may be cut in pieces, but then again the standard use case for sliding window inference is for detecting small objects in large images, so large objects should be less of an issue.

github-actions[bot] commented 3 years ago

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.