Comments (4)
The network implementation relies on Pytorch, so its performance is on par with the framework's. There shouldn't be anything special if you use the last version of pytorch. If you use bilinear=True
for the upsampling, things are faster and need less memory.
About the data loading and overall pipeline, it's Python so you get what you expect.
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This implementation is very different from the origin U-Net, I think its depend on your task.
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@xiaoxingzeng Can you tell me what's different from the original architecture ? I think my implementation matches the Fig. 1 from https://arxiv.org/pdf/1505.04597.pdf
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@xiaoxingzeng Can you tell me what's different from the original architecture ? I think my implementation matches the Fig. 1 from https://arxiv.org/pdf/1505.04597.pdf
Hi milesial, I check the paper and your code.
Sorry,it's my mistake, I reference the paper not the origin paper you referenced. Your reimplementation is correct.
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Related Issues (20)
- Refer the datasets to download from Kaggle
- Windows .bat setup script HOT 1
- Latest Python versions have deprecated `distutils` module
- How to accurately calculate Model Parameters and GFLOPs?
- thanks a lot, your model help me lot, I make a grad-cam to mark what place the U-Net look, also named hot-map HOT 1
- The training loss is always nan. HOT 3
- dice = ? 0.5
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- 为什么预测出来的图片全黑,能不能回复一下 HOT 3
- can't change batch_size
- IndexError: list index out of range
- Python version HOT 1
- My own dataset reported an error
- Did you use "crop" in the skip connection? or you just connect them? HOT 1
- train error
- wandb issue HOT 3
- Trained Check Points not Working HOT 1
- about export pth to onnx
- what does n_classes=1 mean?
- A confusion in hubconf.py
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