Comments (5)
Hi, welcome up to DexiNed, just a question, did you read the paper related to this repo? Have you checked closed issues about the evaluation? If you did, I'll be happy to help you.
Cheers
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Yes, I have read your article.
How can I use these variables to calculate the metric?
I am working on improving the LDC architecture, but the precision metric is giving very low results(0.1). Why aren't you sharing the eval.py file?
from dexined.
Hi, sorry you are right.
Have been a little bit busy. Let me know if this issue can help you:
#63
Cheers.
from dexined.
I want to add these metrics to the train code. So it has to be python code.
https://github.com/Walstruzz/edge_eval_python
There is a repo for Python, but it's too complicated.
I want to measure the performance of GhostNet architecture using your repo. Can you help with this?
My Work: https://github.com/kadirnar/Pytorch-LDC/blob/main/backbone/mymodel.py
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Related Issues (20)
- Metric Evaluation HOT 1
- The code of the fifth block seems to be inconsistent with the network structure of the fifth block HOT 3
- DexiNed model converted to ONNX not returning Results Using CUDA - C++ HOT 1
- Run Time error : CUDA out of memory
- val_loss
- How can we export its weights for Mobile
- ValueError in TF2 model
- 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
- About Bsds and Biped HOT 2
- 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
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