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
In the summation of residual and identity, there is a dimensional mismatch, and after debugging, it is found that the dimensions will be different after transformer, how to solve this problem?
The text was updated successfully, but these errors were encountered:
To add to the above question, it is well known that the fasterrcnn task is not required to specify the resolution of the input images (he has default intervals), but after referring to the code in the article, I think of him more as a paradigm for a classification task based on 224x224 input images, so I would like to know how you applied ConTNet to the fasterrcnn detection task? How did you handle it for fasterrcnn input images with different resolutions?
In the summation of residual and identity, there is a dimensional mismatch, and after debugging, it is found that the dimensions will be different after transformer, how to solve this problem?
The text was updated successfully, but these errors were encountered: