Comments (3)
@Kaidy-Boom I meet the same problem,I think the reason is that the mnist is too easy for them to learn.I delete the first two Deformable layers and only keep the last one seems to have a better result.
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@Kaidy-Boom @wushuang01 Thanks for your report, indeed that's not a good demo of the effectiveness. To clarify, my implementation is just a port of the tensorflow version by Felix Lau as mentioned in README, I discussed with him briefly in the past about a similar question, and I did not have time to do more for that. For a complete prove of the effectiveness, I think would be better to check the original paper.
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@oeway Thanks for your reply.appreciate for your work which is helpful for me.
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Related Issues (20)
- Question about implementation HOT 1
- Input image w!=h HOT 5
- Gif image visualization HOT 6
- Offset output channel HOT 1
- What is the size of the offset? HOT 3
- about the implementation.. view instead of permute ? HOT 5
- Confusion about the shape of offset HOT 7
- Why only fine tuning on deformable convnet HOT 1
- Weighting in deformed kernel
- Fixed
- only for test, cannot be used for training
- The deform-conv layers reduce the detection accuracy
- Offset BUG HOT 1
- How to get the sampling points?
- related to TF_version #4
- related to TF_version #4
- related to TF_version #4
- Process finished with exit code 139 (interrupted by signal 11: SIGSEGV) HOT 1
- Deformable Pooling is not included HOT 1
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