Comments (3)
Hello, the main focus of our model is to design an extremely small model for skin lesion segmentation, as indicated by the title of the paper. Therefore, we did not train or test the model on other datasets. Furthermore, in future work, we may improve and expand the modules proposed in EGE-UNet to adapt them to other broader medical image datasets.
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Hi, I also found this issue while using my own dataset, and I found all my conv-layer's weight became NaN after one loss backward.
Then with torch.autograd.detect_anomaly()
said groupConvBackward output NaN.
After some useless debugging from my guesses, I found the dataset isic*
used in the paper is kinda special. All its samples include positive area, which means there are always two classes in each mask.
So I tentatively deleted all full-positive & full-negative samples of my dataset, then it works!
But it's truly an issue that we will meet full-positive or full-negative sample in practical usage, I don't know if it can solve this situaltion.
Thanks :)
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I filtered my dataset again after, and I found that conclusion above is not quite correct.
It's dirty data leading to this issue. My dataset include some edge clips from each complete samples on a black canvas with angles, and these clips are full-zero matrix.
I took back to my dataset the rest of those samples I deleted before, after deleting full-zero input samples, and it also works.
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Related Issues (20)
- gt_ds layer
- 训练耗时 HOT 2
- 对比实验数据偏差大
- The mask in the released dataset is Not binary value.
- model_cfg ------ 'gt_ds': False----------error
- 数据结果不对
- 请问能将对比的模型的代码也开源一下吗?
- gt_ds设置为False报错
- myNormalize class
- 论文提到DW深度可分离卷积,但在代码中却使用普通卷积
- Input size
- 参数数据不对 HOT 6
- the best checkpoint
- ValueError: height and width must be > 0 HOT 1
- 模型转换和优化的错误
- IndexError: index 1 is out of bounds for axis 1 with size 1 HOT 1
- hello, can you upload both the trained weights or the predicted results of EGE-UNet and MALUNet ? It may not cost your extra time. Becasue we will compare your results at qualitative and quantitative aspects. Thanks!
- About Dataset
- Can you improve the code for the test? Thank you so much! HOT 7
- Trainning in other datasets and deploy HOT 13
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