Comments (3)
Hiya @yassineAlouini , what you suggested should work! It might be a bit slow, and the learning rate might need to be adjusted.
LCFCN does not work natively with batch sizes larger than one, mainly because the watershed algorithm does not support more than one image. If there is a Pytorch-based watershed method, then we could incorporate it and make LCFCN support larger batches.
Thanks for the nice, concise code in making the code support batches larger than 1.
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Indeed, watershed is probably the limiting part for now. I will have a look at a pytorch implementation and update with more details. Thanks for the quick feedback.
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I have found this: https://paperswithcode.com/paper/deep-watershed-transform-for-instance. Not sure how useful it could be, I probably need some exploration to see if we can extract the watershed part of it.
Here is a Pytorch implementation: https://github.com/timothyn617/watershed-transform
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Related Issues (20)
- Reproducing results in paper
- Inference problem HOT 3
- The prediction was incorrect where use best_model_trancos_ResFCN.pth HOT 1
- How to annotate fro custom data training? HOT 4
- Where are the path_model and path_opt files when training from scratch? HOT 8
- Error in losses.py HOT 2
- ValueError: exp_list is empty... HOT 8
- Can you kindly provide the scripts for testing and visualization of blobs? HOT 2
- Error in loading .pth HOT 2
- Can you provide a model that you trained on trancos data?
- Display results HOT 1
- Wrong output with other backbone networks HOT 4
- How to use the multiclass version?How to organize the dataset?Pascal VOC for example... HOT 1
- How to create files for folder images?
- Inference script HOT 2
- How to plot loss during the training ?
- Dear author, can you give me a test.py HOT 1
- sorry,I am a green hand HOT 1
- ERROR: Command errored out with exit status 128 HOT 4
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