Comments (4)
The most proper (but perhaps time-consuming) thing would be to retrain AlexNet to do ImageNet classification on grayscale images. This would be a 1-line change to this codebase (add a line in the data loader to force images to be grayscale). Then do a linear calibration steps with our perceptual judgments from the BAPPS dataset.
Replicating to 3 identical channels is a reasonable alternative.
from perceptualsimilarity.
I didn't test specifically on grayscale images, but it should still give some notion of distance.
from perceptualsimilarity.
thank you for the update
from perceptualsimilarity.
Facing the same issue myself. Need to use LPIPS (averaged over the number of frames) as a metric of Perceptual Similarity for the KTH Action Video Dataset (which is grayscale). Is replicating the single channels to 3 identical channels an acceptable way to obtain a similarity measure? Does there exist a better alternative?
from perceptualsimilarity.
Related Issues (20)
- AttributeError: module 'lpips' has no attribute 'LPIPS' HOT 6
- How we can use PerceptualSimilarity loss function for images with different sizes HOT 2
- AttributeError: module 'lpips' has no attribute 'normalize_tensor'
- Why do I get different results when running the same data multiple times? HOT 3
- Distortions HOT 1
- Two warnings from Torchvision
- Has anybody applied this metrics to libtorch(c/c++)?
- torch.device("cuda:0")? HOT 1
- How to ignore some pixels when calculating the loss? HOT 1
- Update pip package
- normalization in BGR HOT 3
- Distance measure after training
- RuntimeError: Function 'SqrtBackward0' returned nan values in its 0th output HOT 4
- How to expand the use?
- Update conda package
- how can I wget the JND Dataset, the old URL was unavailable? HOT 4
- Can be used for 3D data ? HOT 2
- How to apply this function to 1channel image? HOT 4
- Using LPIPS with AMP
- How do I turn off the screen spam? HOT 1
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from perceptualsimilarity.