Comments (2)
Generally, if inputting grayscale images into RGB networks, you would just copy the grayscale channel three times. However, it is unclear how FID behaves in this case, as the original Inception network was probably not trained on grayscale images.
Also, I would advise against computing a FID score on Mnist. FID score was designed to work on larger images of natural scenes, not on 28x28 images of handwritten digits. I am guessing that the numbers you would get from that are pretty meaningless and do not reflect human judgement of quality.
from pytorch-fid.
I added more info here :- https://stackoverflow.com/questions/57183647/
FYI: Thanks for your repo. Helped me finish this task fast.
from pytorch-fid.
Related Issues (20)
- Imaginary component 3.1913775165377e+114 HOT 4
- How to calculate fid score with label HOT 1
- Can I implement the code with video data? HOT 1
- ValueError: Imaginary component 4.082076360939105e+125 HOT 12
- Batch-size Error HOT 5
- FID is a negative value HOT 2
- ValueError: batch_size should be a positive integer value, but got batch_size=0 HOT 8
- Invalid path error HELP PLS
- CUDNN_STATUS_NOT_SUPPORTED
- OSError: image file is truncated HOT 1
- python: symbol lookup error: /home/xxx/miniconda3/envs/torch/lib/python3.7/site-packages/mk│ l/../../../libmkl_intel_thread.so.1: undefined symbol: __kmpc_global_thread_num HOT 1
- RuntimeError: unexpected EOF, expected 877244 more bytes. The file might be corrupted. HOT 3
- Query: WGAN-GP FID SCORE (PyTorch) HOT 1
- No module named pytorch_fid
- RuntimeError: cuDNN error: CUDNN_STATUS_NOT_INITIALIZED HOT 1
- batch_size error HOT 7
- A better way to compute the FID
- Faster computation for FID
- Dataset sizes
- Error while loading weights from url HOT 1
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from pytorch-fid.