Comments (2)
Hi, I have a doubt that Bsds edges predicted by Biped dataset training are better, but Bsds metrics are lower during evaluation. Is this because of the gt value of Bsds?
In our next publication we will try to give this answer. In the meantime you can check our last publication LDC. Sorry, I didn't give you a concrete answer.
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I think it may be the labeling of the BSDS dataset that causes this problem, because BIPED labels all edges as much as possible, while some edges are ignored in the labeling of BSDS, causing the model trained with BIPED to detect this part of the edges, but BSDS does not have this part of the labeling, which leads to a decrease in metrics.
Maybe I'm wrong, just offering a possible thought
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Related Issues (20)
- Testing on a single image (lena_std.tif) HOT 3
- about evaluation HOT 2
- Works once but stops working after testing own image HOT 3
- Can I change the resize value in dataset transform function? HOT 3
- Size of the CLASSIC images
- About args.double_img HOT 5
- Can't parse 'dsize'. Sequence item with index 0 has a wrong type HOT 2
- Should the l_weight value change according to the dataset? HOT 3
- Inference mode is available? HOT 2
- GT HOT 1
- loss does not converge HOT 25
- How can I calculate ODS, OIS, AP metrics? HOT 5
- does DexiNed support multiple GPUs .. if Yes, how to implement it for DexiNed TF2 HOT 4
- high train loss
- About Precision-Recall Curves on Biped
- Getting an NoneType error while testing with my images HOT 1
- Unable to reproduce results HOT 7
- Interpretation of result.png HOT 2
- Hello! Why does the following problem occur after running main.py
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