Comments (1)
Hi, thanks for the question. Actually, this is not a bug. We follow the normalization manner as the paper "The Contextual Loss for Image Transformation with Non-Aligned Data" (footnote of page 6) and empirically find this gives better matching performance.
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Related Issues (20)
- Wrong output resolution HOT 9
- ModuleNotFoundError: No module named 'cv2' HOT 2
- It seems not correct of the code in TestTransforms.py line 341
- why the light channel data is normalize to 50 HOT 2
- Questions about the test phase
- training command is wrong. HOT 1
- Training data problem HOT 1
- Training has little effect HOT 1
- Test result problem
- CUDA device error "module 'torch._C' has no attribute '_cuda_setDevice'" when running test.py HOT 4
- Runtime error
- Error 404 - Important files missing HOT 1
- the pretrained models HOT 1
- Documentation for starting the library
- Dataset for Training
- error when colorizing the video 04.jpg module 'cv2' has no attribute 'ximgproc' HOT 1
- Colorization result was different if changing the scale_factor=0.5 to 1.0 in test.py . Not sure why?
- The colab demo version is DEAD
- Update code
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