You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
{{ message }}
This repository has been archived by the owner on Mar 2, 2022. It is now read-only.
outputs = exec_net.infer(inputs={input_blob: prepimg})
I got two outputs and it's shape like (1,18,13,13) and (1,18,26,26). I want to know each number means. I also want to know how EntryIndex function work. I will appreciate your help with this question.
The text was updated successfully, but these errors were encountered:
I can help you with the first part, the second part is a problem for me too!
The results are divided between these 2 layers, the feature map dimensions of which, you have mentioned above. But I got the following for v3-tiny
Layer
Feature map shape
detector/yolo-v3-tiny/Conv_12/BiasAdd/YoloRegion
(1, 255, 26, 26)
detector/yolo-v3-tiny/Conv_9/BiasAdd/YoloRegion
(1, 255, 13, 13)
If you can justify why you don't get 255 and get 18, I can help you understanding the 255 value.
Sign up for freeto subscribe to this conversation on GitHub.
Already have an account?
Sign in.
Hello @PINTO0309,
outputs = exec_net.infer(inputs={input_blob: prepimg})
I got two outputs and it's shape like (1,18,13,13) and (1,18,26,26). I want to know each number means. I also want to know how EntryIndex function work. I will appreciate your help with this question.
The text was updated successfully, but these errors were encountered: