Comments (4)
It should be possible to modify the 'length' variable in the solver.py file, and its accumulation should be length=length+1
from image_segmentation.
Thanks for sharing useful project I have a question about the score in this repo I had prepared the data ISIC same yours.
1815 images were used for training, 259 for validation and 520 for testing models
When I train your model with the data ISIC, the result is very bad.
- U_Net
Epoch [148/150], Loss: 2.7109,
[Training] Acc: 0.0993, SE: 0.0212, SP: 0.0800, PC: 0.0212, F1: 0.0212, JS: 0.1003, DC: 0.1026
Decay learning rate to lr: 9.719919173243618e-06.
[Validation] Acc: 0.0961, SE: 0.0219, SP: 0.0828, PC: 0.0219, F1: 0.0219, JS: 0.1004, DC: 0.1113
Epoch [149/150], Loss: 2.8651,
[Training] Acc: 0.0994, SE: 0.0212, SP: 0.0800, PC: 0.0212, F1: 0.0212, JS: 0.1003, DC: 0.1026
Decay learning rate to lr: 4.8599595866217745e-06.
[Validation] Acc: 0.0961, SE: 0.0219, SP: 0.0827, PC: 0.0219, F1: 0.0219, JS: 0.1004, DC: 0.1114- AttU_Net
Epoch [99/100], Loss: 9.7082,
[Training] Acc: 0.1229, SE: 0.0270, SP: 0.1002, PC: 0.0270, F1: 0.0270, JS: 0.1251, DC: 0.1307
Decay learning rate to lr: 6.125101680409697e-06.
[Validation] Acc: 0.1219, SE: 0.0288, SP: 0.1041, PC: 0.0288, F1: 0.0288, JS: 0.1274, DC: 0.1410
Epoch [100/100], Loss: 10.5515,
[Training] Acc: 0.1227, SE: 0.0271, SP: 0.1003, PC: 0.0271, F1: 0.0271, JS: 0.1251, DC: 0.1309
Decay learning rate to lr: 9.147955830346444e-20.
[Validation] Acc: 0.1221, SE: 0.0286, SP: 0.1042, PC: 0.0286, F1: 0.0286, JS: 0.1274, DC: 0.1406- R2AttU_Net
Epoch [99/100], Loss: 89.6683,
[Training] Acc: 0.4795, SE: 0.1198, SP: 0.4012, PC: 0.1198, F1: 0.1198, JS: 0.5003, DC: 0.5545
[Validation] Acc: 0.4692, SE: 0.1067, SP: 0.4279, PC: 0.1067, F1: 0.1067, JS: 0.5019, DC: 0.5861
Epoch [100/100], Loss: 87.9917,
[Training] Acc: 0.4799, SE: 0.1187, SP: 0.4019, PC: 0.1187, F1: 0.1187, JS: 0.5003, DC: 0.5547
[Validation] Acc: 0.4704, SE: 0.1167, SP: 0.4168, PC: 0.1167, F1: 0.1167, JS: 0.5019, DC: 0.5846@LeeJunHyun can you solve this for me. Thanks.
Hello, I have the same problem. Have you solved it before?
from image_segmentation.
感谢分享有用的项目我有一个关于这个 repo 中的分数的问题,我准备了与你相同的数据 ISIC。
1815 张图像用于训练,259 张用于验证,520 张用于测试模型
当我使用数据 ISIC 训练您的模型时,结果非常糟糕。
- U_Net
Epoch [148/150], Loss: 2.7109,
[Training] Acc: 0.0993, SE: 0.0212, SP: 0.0800, PC: 0.0212, F1: 0.0212, JS: 0.1003, DC: 0.1026
Decay learning rate to lr: 9.16991917324 -06。
[验证] Acc: 0.0961, SE: 0.0219, SP: 0.0828, PC: 0.0219, F1: 0.0219, JS: 0.1004, DC: 0.1113
Epoch [149/150], Loss: 2.8651,
[Training] Acc: 0.0994, SE: 0.0212,SP:0.0800,PC:0.0212,F1:0.0212,JS:0.1003,DC:0.1026
衰减学习率到 lr:4.8599595866217745e-06。
[验证] Acc: 0.0961, SE: 0.0219, SP: 0.0827, PC: 0.0219, F1: 0.0219, JS: 0.1004, DC: 0.1114- AttU_Net
Epoch [99/100], Loss: 9.7082,
[Training] Acc: 0.1229, SE: 0.0270, SP: 0.1002, PC: 0.0270, F1: 0.0270, JS: 0.1251, DC: 0.1307
Decay learning rate to lr: 6.16971016804 -06。
[验证] Acc: 0.1219, SE: 0.0288, SP: 0.1041, PC: 0.0288, F1: 0.0288, JS: 0.1274, DC: 0.1410
Epoch [100/100], Loss: 10.5515,
[Training] Acc: 0.1227, SE: 0.0271,SP:0.1003,PC:0.0271,F1:0.0271,JS:0.1251,DC:0.1309
衰减学习率到 lr:9.147955830346444e-20。
[验证] Acc: 0.1221, SE: 0.0286, SP: 0.1042, PC: 0.0286, F1: 0.0286, JS: 0.1274, DC: 0.1406- R2AttU_Net
Epoch [99/100], Loss: 89.6683,
[Training] Acc: 0.4795, SE: 0.1198, SP: 0.4012, PC: 0.1198, F1: 0.1198, JS: 0.5003, DC: 0.5545
[Validation] Acc: 0.4692, SE : 0.1067, SP: 0.4279, PC: 0.1067, F1: 0.1067, JS: 0.5019, DC: 0.5861
Epoch [100/100], Loss: 87.9917,
[Training] Acc: 0.4799, SE: 0.1187, SP: 0.4019, PC: 0.1187, F1: 0.1187, JS: 0.5003, DC: 0.5547
[验证] Acc: 0.4704, SE: 0.1167, SP: 0.4168, PC: 0.1167, F1: 0.1167, JS: 0.5019, DC: 0.5846@LeeJunHyun你能帮我解决这个问题吗?谢谢。
你好,我也有同样的问题。你以前解决过吗?
Please have you solved this problem?
from image_segmentation.
It should be possible to modify the 'length' variable in the solver.py file, and its accumulation should be length=length+1
Is it because it is stacked once each time, so each time +1 and then averaged?
from image_segmentation.
Related Issues (20)
- some queries HOT 1
- How long will it cost for training on DRIVE dataset?
- saving checkpoints in "model.pth" format and visualizing the segmentation results HOT 1
- JS=1,DS>1and loss>1000 HOT 1
- RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 160 and 384 in dimension 2 at /pytorch/aten/src/TH/generic/THTensor.cpp:689 HOT 8
- Grayscale image HOT 1
- about the .pkl file HOT 2
- > hey guys. use pytorch<=1.2.0 (not confirmed) or change the funtion in "evaluation" to fit the calculation of **bool tensor** will sovle the problem HOT 1
- El
- How to train the multi-class task
- Dice coefficience
- the length variable in the solver.py,
- ValueError: num_samples should be a positive integer value, but got num_samples=0 HOT 1
- License
- the test of solver.py HOT 1
- acc error HOT 1
- Environment details HOT 2
- ValueError: num_samples should be a positive integer value, but got num_samples=0 HOT 1
- Query HOT 1
- 如何使用模型
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from image_segmentation.