Comments (4)
khdlr
commented on July 20, 2024
Hi Malaika,
you'll need to implement a torch.utils.data.Dataset that returns pairs of image and ground truth annotations.
Then you can change the get_dataset function in data_loading.py to use your custom dataset instead of the pre-configured InriaDataset.
Let me know if that helps!
Cheers,
Konrad
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Malaika68
commented on July 20, 2024
Thank you so much for the help. Can you please tell what type of annotations it should be?
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khdlr
commented on July 20, 2024
No problem! The annotations should be of the same shape as the imagery (i.e. dense annotations) and contain a 1 for each foreground pixel and a 0 for each background pixel.
In the examples shown in the README, Glacier/Building pixels are the "foreground"
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Malaika68
commented on July 20, 2024
Okay great,,
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Related Issues (18)
- How could I get the complete images on result HOT 1
- Is the image input during the training process the original image and the binary image? HOT 17
- Testing data HOT 7
- Whether this program can handle .png files. If you can’t handle it, can you convert the .tif file to a .png file? HOT 1
- AttributeError: 'Array' object has no attribute 'numpy' HOT 1
- RuntimeError: Given groups=1, weight of size [16, 3, 1, 1], expected input[8, 4, 256, 256] to have 3 channels, but got 4 channels instead HOT 10
- Hello, my training is successful, then I would like to ask how to test my own data? HOT 1
- About training HOT 21
- About Loss HOT 5
- About mutil classes HOT 19
- Hed HOT 7
- Weight Map HOT 2
- I have some questions about testing. HOT 6
- About the differences between predictions and queries. HOT 3
- Does the edge detection task have an impact on the semantic segmentation task in this model? HOT 1
- Can you please share the pre-trained weights for interence HOT 1
- Can the author provide code for prediction
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