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Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.

Python 99.38% Shell 0.62%

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image_segmentation's Issues

how to solve ?

my label is background to 0 and others to 1 .but i donot know how to make it work, the code may not have a test function?

if img_ch =3,RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0

If img_ch =3 ，sometimes this error occurs.

more detail information：
batchsize = 1，the code can work
batchsize = 4，it causes the problem ，RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0

Thanks for you sharing.
I want to know shouldn't the input of the image be of any size?And I modified the file of data_loader,but I got this error:
d4 = torch.cat((x3,d4),dim=1)
RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 1. Got 24 and 25 in dimension 2 at /opt/conda/conda-bld/pytorch_1556653099582/work/aten/src/THC/generic/THCTensorMath.cu:71
Please help me.

Low performance when i train

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.

lr is confusing

When I run python main.py --lr=0.001
I saw this

Namespace(augmentation_prob=0.05992219405072973, batch_size=4, beta1=0.5, beta2=0.999, cuda_idx=2, image_size=224, img_ch=3, log_step=2, lr=0.0002124676041200755, mode='train', model_path='./models', model_type='R2AttU_Net', num_epochs=100, num_epochs_decay=24, num_workers=8, output_ch=1, result_path='./result/R2AttU_Net', t=3, test_path='./dataset/test/', train_path='./dataset/train/', val_step=2, valid_path='./dataset/valid/')

as you can see: lr=0.0002124676041200755
The learning rate is not equal as I set in the config, why?
How can I deal with it?
Thank you!

size problem

Does this model have a requirement for image size?I have uesd 2 medical dataset in this model,but the results are strange.
这个模型对输入图片大小有要求吗？并且我的dataset是单通道图片，我把输入图片调整为三通道，但结果出来异常，有些指标很低

About the results

Dear @LeeJunHyun ,
Thank you very much for your time and your help.
I want to ask you about the results image. Firstly my dataset used in model look like
--- for input and GT respectivly.
the getting result look like
Named : U_Net_valid_1_GT
Named : U_Net_valid_1_image
Named : U_Net_valid_1_SR
Can you please explain what's this images refer to ?!!!,, Where is the resulted segmented image?

where i can find the GroundTruth data.i just find train GT while test and valid don't find

run main.py.it show haven't it

Could you add the support of torchhub?

just like the title said

FileNotFoundError: [Errno 2] No such file or directory: '.

hello i have some problems about this code
Traceback (most recent call last):
File "/home/panpan/wenjian/pytorchImage_Segmentation-master/dataset.py", line 115, in
main(config)
File "/home/panpan/wenjian/pytorchImage_Segmentation-master/dataset.py", line 55, in main
copyfile(src, dst)
File "/home/panpan/anaconda3/lib/python3.6/shutil.py", line 120, in copyfile
with open(src, 'rb') as fsrc:
FileNotFoundError: [Errno 2] No such file or directory: '../ISIC/dataset/train_data/ISIC_33.png'

i dont know how to deal with ,can you help me ?thanks a lot

if img_ch =1 ，output with shape doesn't match the broadcast shape

if img_ch =1 ，output with shape doesn't match the broadcast shape
Input is grayscale image.
Thanks!

Regarding dataset

How do I prepare my dataset ?

inputs of evaluation for training and validation

@LeeJunHyun
Thanks for sharing the code
I found some differences in the evaluation of training and validation in solver.py.
Is this correct? Because I am a beginner, so a little confused!

Train ### ### valid

SR = self.unet(images) SR = F.sigmoid(self.unet(images))
SR_probs = F.sigmoid(SR)
...... ......
acc += get_accuracy(SR,GT) acc += get_accuracy(SR,GT)
SE += get_sensitivity(SR,GT) SE += get_sensitivity(SR,GT)
SP += get_specificity(SR,GT) SP += get_specificity(SR,GT)
PC += get_precision(SR,GT) PC += get_precision(SR,GT)
...... ......

may i have a baseline level pre-trained model?

Recurrent Unit for Sequences

Hi,
I just wonder how the recurrent network learns from the information of the temporal previous images of a sequence.
The for loop for the t steps is implemented in the Recurrent_block.

def forward(self,x):
        for i in range(self.t):
            if i==0:
                x1 = self.conv(x)        
            x1 = self.conv(x+x1)
        return x1

I assumed that somewhere the hidden state would be returned to use in the next frame.

batch_size about R2AttU_Net?

I am using 1080ti
I set batch_size = 2 of R2AttU_Net, it return
cuda runtime error (2) : out of memory
Can I set batch_size > 1 when i training R2AttU_Net?

conv size of up-conv2x2

Hi, the paper says that "Every step in the expansive path consists of an upsampling of the
feature map followed by a 2x2 convolution ('up-convolution') that halves the
number of feature channels"

In your code, however, I notice that you use a conv2d of size3x3, would you explain the reason to that?

class up_conv(nn.Module):
    def __init__(self,ch_in,ch_out):
        super(up_conv,self).__init__()
        self.up = nn.Sequential(
            nn.Upsample(scale_factor=2),
            nn.Conv2d(ch_in,ch_out,kernel_size=3,stride=1,padding=1,bias=True),
		    nn.BatchNorm2d(ch_out),
			nn.ReLU(inplace=True)
        )

    def forward(self,x):
        x = self.up(x)
        return x

And also, let's say I want to stick to the paper and use 2x2 conv, how would I choose the padding and stride to make output size the same as the input size?

Thanks for sharing your work.

about the dataset

Hello, could you upload the ISIC 2018 dataset? I downloaded from the web but always failed in about 50%.

where i can find the GroundTruth data.i just find train GT while test and valid don't find

run main.py.it show haven't it

Is it a bug in RRCNN

I check this function and see the x has been changed by x = self.Conv_1x1(x). So "return x+x1" maybe a bug.
def forward(self,x):
x = self.Conv_1x1(x)
x1 = self.RCNN(x)
return x+x1

Custom dataset

Hello,

First of all thank you for your contribution.
Could you please point us what is to be changed in your code in order
to apply those networks to a custom dataset?

Thanks!!
Joris

Issue with inference.

predict image

how to get predicted segmented image from test data

RuntimeError: Given groups=1, weight of size 64 3 3 3, expected input[1, 512, 512, 3] to have 3 channels, but got 512 channels instead

thanks for your share,i am trying to use your program to run my dataset,the train picture's size is 512,512,its channel is 3,but when i run this program ,it appear this error, i don't know how to solve it,you know?thanks!
RuntimeError: Given groups=1, weight of size 64 3 3 3, expected input[1, 512, 512, 3] to have 3 channels, but got 512 channels instead

evalution problem

Hi, @LeeJunHyun ,
First of all thank you very much for sharing your code!

i try with my data, but evalution score become 0.
i don't konw how to to solve this problem. Is there something wrong with my data?

Epoch [1/50], Loss: 21.7333,
[Training] Acc: 0.9116, SE: 0.0035, SP: 0.9902, PC: 0.0123, F1: 0.0053, JS: 0.0029, DC: 0.0053
[Validation] Acc: 0.9193, SE: 0.0064, SP: 0.9651, PC: 0.0075, F1: 0.0062, JS: 0.0031, DC: 0.0062
Best U_Net model score : 0.0094
Epoch [2/50], Loss: 18.3222,
[Training] Acc: 0.9222, SE: 0.0000, SP: 0.9978, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9511, SE: 0.0003, SP: 0.9993, PC: 0.0053, F1: 0.0005, JS: 0.0003, DC: 0.0005
Epoch [3/50], Loss: 17.2257,
[Training] Acc: 0.9309, SE: 0.0000, SP: 0.9988, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9452, SE: 0.0000, SP: 0.9931, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [4/50], Loss: 16.3815,
[Training] Acc: 0.9266, SE: 0.0000, SP: 0.9991, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9514, SE: 0.0000, SP: 0.9997, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [5/50], Loss: 15.9624,
[Training] Acc: 0.9243, SE: 0.0000, SP: 0.9996, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [6/50], Loss: 14.9182,
[Training] Acc: 0.9232, SE: 0.0000, SP: 0.9998, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9517, SE: 0.0000, SP: 0.9999, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [7/50], Loss: 14.1974,
[Training] Acc: 0.9235, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [8/50], Loss: 13.5342,
[Training] Acc: 0.9233, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [9/50], Loss: 12.9099,
[Training] Acc: 0.9241, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [10/50], Loss: 12.3084,
[Training] Acc: 0.9281, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9516, SE: 0.0000, SP: 0.9998, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [11/50], Loss: 11.7281,
[Training] Acc: 0.9264, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9519, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [12/50], Loss: 11.1690,
[Training] Acc: 0.9278, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [13/50], Loss: 10.6329,
[Training] Acc: 0.9204, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9519, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [14/50], Loss: 10.1158,
[Training] Acc: 0.9211, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [15/50], Loss: 9.6117,
[Training] Acc: 0.9295, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [16/50], Loss: 9.1243,
[Training] Acc: 0.9213, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [17/50], Loss: 8.6579,
[Training] Acc: 0.9218, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [18/50], Loss: 8.2152,
[Training] Acc: 0.9244, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [19/50], Loss: 7.7921,
[Training] Acc: 0.9160, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [20/50], Loss: 7.3884,
[Training] Acc: 0.9207, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [21/50], Loss: 7.0041,
[Training] Acc: 0.9297, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
[Validation] Acc: 0.9519, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [22/50], Loss: 6.6437,
[Training] Acc: 0.9232, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 0.00010942264264529794.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [23/50], Loss: 6.3045,
[Training] Acc: 0.9282, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 0.00010551469112225158.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [24/50], Loss: 5.9960,
[Training] Acc: 0.9249, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 0.00010160673959920523.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [25/50], Loss: 5.7120,
[Training] Acc: 0.9274, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 9.769878807615888e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [26/50], Loss: 5.4525,
[Training] Acc: 0.9254, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 9.379083655311253e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [27/50], Loss: 5.2159,
[Training] Acc: 0.9208, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 8.988288503006618e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [28/50], Loss: 4.9962,
[Training] Acc: 0.9220, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 8.597493350701983e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [29/50], Loss: 4.7708,
[Training] Acc: 0.9227, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 8.206698198397348e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [30/50], Loss: 4.5739,
[Training] Acc: 0.9244, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 7.815903046092712e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [31/50], Loss: 4.4050,
[Training] Acc: 0.9203, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 7.425107893788077e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [32/50], Loss: 4.2477,
[Training] Acc: 0.9261, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 7.034312741483442e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [33/50], Loss: 4.1065,
[Training] Acc: 0.9239, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 6.643517589178807e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [34/50], Loss: 3.9750,
[Training] Acc: 0.9262, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 6.252722436874172e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [35/50], Loss: 3.8508,
[Training] Acc: 0.9244, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 5.861927284569537e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [36/50], Loss: 3.7409,
[Training] Acc: 0.9259, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 5.4711321322649015e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [37/50], Loss: 3.6415,
[Training] Acc: 0.9278, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 5.0803369799602664e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [38/50], Loss: 3.5523,
[Training] Acc: 0.9285, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 4.689541827655631e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [39/50], Loss: 3.4731,
[Training] Acc: 0.9208, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 4.298746675350996e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [40/50], Loss: 3.3990,
[Training] Acc: 0.9242, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 3.907951523046361e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [41/50], Loss: 3.3331,
[Training] Acc: 0.9274, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 3.517156370741726e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [42/50], Loss: 3.2744,
[Training] Acc: 0.9280, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 3.1263612184370907e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [43/50], Loss: 3.2240,
[Training] Acc: 0.9264, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 2.735566066132455e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [44/50], Loss: 3.1804,
[Training] Acc: 0.9221, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 2.3447709138278197e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [45/50], Loss: 3.1397,
[Training] Acc: 0.9278, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 1.9539757615231842e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [46/50], Loss: 3.1074,
[Training] Acc: 0.9281, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 1.5631806092185487e-05.
[Validation] Acc: 0.9519, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [47/50], Loss: 3.0812,
[Training] Acc: 0.9264, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 1.1723854569139132e-05.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [48/50], Loss: 3.0632,
[Training] Acc: 0.9187, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 7.815903046092777e-06.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [49/50], Loss: 3.0484,
[Training] Acc: 0.9197, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 3.907951523046423e-06.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Epoch [50/50], Loss: 3.0376,
[Training] Acc: 0.9241, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000
Decay learning rate to lr: 6.776263578034403e-20.
[Validation] Acc: 0.9518, SE: 0.0000, SP: 1.0000, PC: 0.0000, F1: 0.0000, JS: 0.0000, DC: 0.0000

Process finished with exit code 0

Could you help me?

Thank you very much!

Question about patch size

Hello.
I'm a starter of u-net.
Thank you for sharing your work.

I have a question about it.
Where is the code that control patch size?

About the input of Attention Gate

Thanks for your sharing! But there is a question that I think g=d5 should be written as g=x5 as shown in the following figure. I don't know whether the way I understand is right or not. If not, please point out!

Execution files order

Hi!
First of all thank you very much for sharing your code!
I'd like to try it but I'm a little confused with the order in which I have to run the different files.

Could you help me?

Thank you very much!

poor result on isbi challenge

Hi:
thank you for the code, i have trained the unet with the isbi dataset 100 epoch,batch size is 8(30 gray picture),but the result is very bad, about 11 percent(accuracy). And i wonder is there something wrong that i have done, could you give me some advise?
data_loader .txt
main .txt
I just modify the data_loader.py and change the input channel to 1(the picture is gray)

about the test

hello ,thank you very much for your contribution

could you tell me how to test the code ,because

Epoch [15/15], Loss: 11.5097,
[Training] Acc: 0.9289, SE: 0.8480, SP: 0.9742, PC: 0.8885, F1: 0.8537, JS: 0.7518, DC: 0.8537
Decay learning rate to lr: 0.0.
[Validation] Acc: 0.8809, SE: 0.9680, SP: 0.8651, PC: 0.6196, F1: 0.7209, JS: 0.5988, DC: 0.7209

Process finished with exit code 0

the test function ,doesn't work , and i only can see the test result

error when set batch to 4

Namespace(augmentation_prob=0.16924156139739033, batch_size=4, beta1=0.5, beta2=0.999, cuda_idx=3, image_size=224, img_ch=1, log_step=2, lr=0.00017304986946574825, mode='train', model_path='./models', model_type='R2U_Net', num_epochs=250, num_epochs_decay=186, num_workers=8, output_ch=1, result_path='./result/R2U_Net', t=3, test_path='/home/Data/DC_disk2/tsachi_dataset/dataset/test/', train_path='/home/Data/DC_disk2/tsachi_dataset/dataset/train/', val_step=2, valid_path='/home/Data/DC_disk2/tsachi_dataset/dataset/valid/')
image count in train path :2400
image count in valid path :211
image count in test path :200
Traceback (most recent call last):
File "main.py", line 101, in
main(config)
File "main.py", line 61, in main
solver.train()
File "/home/imagry/tsachi/solver.py", line 140, in train
for i, (images, GT) in enumerate(self.train_loader):
File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/dataloader.py", line 336, in next
return self._process_next_batch(batch)
File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/dataloader.py", line 357, in _process_next_batch
raise batch.exc_type(batch.exc_msg)
RuntimeError: Traceback (most recent call last):
File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/dataloader.py", line 106, in _worker_loop
samples = collate_fn([dataset[i] for i in batch_indices])
File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/dataloader.py", line 187, in default_collate
return [default_collate(samples) for samples in transposed]
File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/dataloader.py", line 187, in
return [default_collate(samples) for samples in transposed]
File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/dataloader.py", line 164, in default_collate
return torch.stack(batch, 0, out=out)
RuntimeError: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 192 and 320 in dimension 2 at /pytorch/aten/src/TH/generic/THTensorMath.cpp:3616

Exception ignored in: <bound method _DataLoaderIter.del of <torch.utils.data.dataloader._DataLoaderIter object at 0x7fca6e95b7b8>>
Traceback (most recent call last):
File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/dataloader.py", line 399, in del
self._shutdown_workers()
File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/dataloader.py", line 378, in _shutdown_workers
self.worker_result_queue.get()
File "/usr/lib/python3.5/multiprocessing/queues.py", line 345, in get
return ForkingPickler.loads(res)
File "/usr/local/lib/python3.5/dist-packages/torch/multiprocessing/reductions.py", line 151, in rebuild_storage_fd
fd = df.detach()
File "/usr/lib/python3.5/multiprocessing/resource_sharer.py", line 57, in detach
with _resource_sharer.get_connection(self._id) as conn:
File "/usr/lib/python3.5/multiprocessing/resource_sharer.py", line 87, in get_connection
c = Client(address, authkey=process.current_process().authkey)
File "/usr/lib/python3.5/multiprocessing/connection.py", line 487, in Client
c = SocketClient(address)
File "/usr/lib/python3.5/multiprocessing/connection.py", line 614, in SocketClient
s.connect(address)
ConnectionRefusedError: [Errno 111] Connection refused

The result is not good in ISIC 2018

Jaccard and F1 only 0.5-0.7,are you sure? On the official competition website, the result common is 0.80+ in term of jaccard. In addition, the result of Unet in my experiment is 0.78+ in term of jaccard, Yes! ISIC 2018. 2594 images.I resize it to 256*192

about the Atten_block implementation compared with the paper

according to the paper, HgWgDg is matched up with HxWxDx using trilinear interpolation, which is also called "Resampler" in the figure 2.
but in the code of this repo, there is no this step, instead you use the feature map after the Up_conv step. So the code can be runned, But it is clearly different from what proposed in the figure 2.
So I just wonder whether you implemented this way on purpose? And if yes, please enlighten me why it's better this way.
BTW, thank you so much for this repo, it really helps me a lot. :)

some data info

Hi
Thanks for sharing your work

I have a dataset which looks likes this, image of shape (26, 64,64,64),where in the image the 0th channel is the image and the and the 24 channels are labels, where each channel is a different label.

can i use my data to train this network ?
Let me know if you need more info
Thanks in advance

I can't change the batch_size

Whenever I modify the batch_size, the program does not run,
and then I refer to this issue #4 to modify it.
I followed your suggestion "change the code data_loader.py#L82 to Transform.append(T.Resize(224,224))", still got the error message.

my code

Transform.append(T.Resize((int(256*aspect_ratio)-int(256*aspect_ratio)%16,256)))
Transform.append(T.Resize(224,224))
Transform = T.Compose(Transform)

Traceback (most recent call last):
File "main.py", line 101, in
main(config)
File "main.py", line 61, in main
solver.train()
File "/home/en308/pytorch/Image_Segmentation-master/solver.py", line 142, in train
for i, (images, GT) in enumerate(self.train_loader):
File "/home/en308/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 582, in next
return self._process_next_batch(batch)
File "/home/en308/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 608, in _process_next_batch
raise batch.exc_type(batch.exc_msg)
TypeError: Traceback (most recent call last):
File "/home/en308/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 99, in _worker_loop
samples = collate_fn([dataset[i] for i in batch_indices])
File "/home/en308/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/utils/worker.py", line 99, in
samples = collate_fn([dataset[i] for i in batch_indices])
File "/home/en308/pytorch/Image_Segmentation-master/data_loader.py", line 89, in getitem
image = Norm(image)
File "/home/en308/anaconda3/envs/pytorch/lib/python3.7/site-packages/torchvision/transforms/transforms.py", line 164, in call
return F.normalize(tensor, self.mean, self.std, self.inplace)
File "/home/en308/anaconda3/envs/pytorch/lib/python3.7/site-packages/torchvision/transforms/functional.py", line 201, in normalize
raise TypeError('tensor is not a torch image.')
TypeError: tensor is not a torch image.

I see the data_loader.py#L81 process same question

so I modify

Transform.append(T.Resize(224,224))
Transform.append(T.ToTensor())
Transform = T.Compose(Transform)

Traceback (most recent call last):
File "main.py", line 101, in
main(config)
File "main.py", line 61, in main
solver.train()
File "/home/en308/pytorch/Image_Segmentation-master/solver.py", line 142, in train
for i, (images, GT) in enumerate(self.train_loader):
File "/home/en308/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 582, in next
return self._process_next_batch(batch)
File "/home/en308/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 608, in _process_next_batch
raise batch.exc_type(batch.exc_msg)
ValueError: Traceback (most recent call last):
File "/home/en308/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 99, in _worker_loop
samples = collate_fn([dataset[i] for i in batch_indices])
File "/home/en308/anaconda3/envs/pytorch/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 99, in
samples = collate_fn([dataset[i] for i in batch_indices])
File "/home/en308/pytorch/Image_Segmentation-master/data_loader.py", line 85, in getitem
image = Transform(image)
File "/home/en308/anaconda3/envs/pytorch/lib/python3.7/site-packages/torchvision/transforms/transforms.py", line 61, in call
img = t(img)
File "/home/en308/anaconda3/envs/pytorch/lib/python3.7/site-packages/torchvision/transforms/transforms.py", line 196, in call
return F.resize(img, self.size, self.interpolation)
File "/home/en308/anaconda3/envs/pytorch/lib/python3.7/site-packages/torchvision/transforms/functional.py", line 244, in resize
return img.resize((ow, oh), interpolation)
File "/home/en308/anaconda3/envs/pytorch/lib/python3.7/site-packages/PIL/Image.py", line 1865, in resize
message + " Use " + ", ".join(filters[:-1]) + " or " + filters[-1]
ValueError: Unknown resampling filter (224). Use Image.NEAREST (0), Image.LANCZOS (1), Image.BILINEAR (2), Image.BICUBIC (3), Image.BOX (4) or Image.HAMMING (5)

Could you suggest me how to do?

Class-balanced CE loss function leads to worse result?

I find that the class-balanced cross entropy loss function leads to smaller loss, about 0.001 - 0.008 in my dataset, and the performance is worse than using Iou as loss function which has bigger loss value .

Dataset training

Hello, @LeeJunHyun
I followed your instruction by changing the path to my data in dataset.py L99,100 .. but after running main.py i got errors in L152(solver.py) about the unequal size of SR_Flat, GT_Flat !!

ValueError: Target and input must have the same number of elements. target nelement (196608) != input nelement (65536)

about the issue

hello can you help me to solve this problem
i dont know how to deal with

Traceback (most recent call last): File "/home/panpan/RRE/Image_Segmentation-master/main.py", line 101, in <module> main(config) File "/home/panpan/RRE/Image_Segmentation-master/main.py", line 61, in main solver.train() File "/home/panpan/RRE/Image_Segmentation-master/solver.py", line 140, in train for i, (images, GT) in enumerate(self.train_loader): File "/home/panpan/anaconda3/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 637, in __next__ return self._process_next_batch(batch) File "/home/panpan/anaconda3/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 658, in _process_next_batch raise batch.exc_type(batch.exc_msg) RuntimeError: Traceback (most recent call last): File "/home/panpan/anaconda3/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 138, in _worker_loop samples = collate_fn([dataset[i] for i in batch_indices]) File "/home/panpan/anaconda3/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 138, in <listcomp> samples = collate_fn([dataset[i] for i in batch_indices]) File "/home/panpan/RRE/Image_Segmentation-master/data_loader.py", line 89, in __getitem__ image = Norm_(image) File "/home/panpan/anaconda3/lib/python3.6/site-packages/torchvision/transforms/transforms.py", line 163, in __call__ return F.normalize(tensor, self.mean, self.std, self.inplace) File "/home/panpan/anaconda3/lib/python3.6/site-packages/torchvision/transforms/functional.py", line 208, in normalize tensor.sub_(mean[:, None, None]).div_(std[:, None, None]) RuntimeError: output with shape [1, 256, 256] doesn't match the broadcast shape [3, 256, 256]

Order of the files

hi, What is the order of files that we need to run?

If I input size is 512x512?

I download the ISIC 2018 training dataset, and the size of images is 4288x2848
but in your code the image_size=224. Why?
If my original custom data's size is 512x512, should I resize it to 224x224?

How could I change the data_loader.py? I think I should change the following lines:

# image_size=224
ResizeRange = random.randint(300,320)
CropRange = random.randint(250,270)
Transform.append(T.Resize((int(256*aspect_ratio)-int(256*aspect_ratio)%16,256)))

And should I also change the model architecture in the network.py after I change the input size?

Thanks in advance!!!

Model prediction error

Training is fine, when I tested an image and got a error message as follow:
UnboundLocalError: local variable 'x1' referenced before assignment

The error occur from network.py

60 class Recurrent_block(nn.Module):
61    def __init__(self,ch_out,t=2):
62        super(Recurrent_block,self).__init__()
63        self.t = t
64        self.ch_out = ch_out
65        self.conv = nn.Sequential(
66            nn.Conv2d(ch_out,ch_out,kernel_size=3,stride=1,padding=1,bias=True),
67		    nn.BatchNorm2d(ch_out),
68			nn.ReLU(inplace=True)
69        )
70
71    def forward(self,x):
72        for i in range(self.t):
73
74            if i==0:
75                x1 = self.conv(x)
76            
77           x1 = self.conv(x+x1)
78        return x1           <====================== error here

How to fix it?
Thanks a lot

I'm just beginning to learn. I have a problem that I can't solve. Can you help me

About the input file and the evaluation

I have 2 questions.
1.What kind of images can I use? My input includes single channel '.png' images which should be segmented and '.png' GT images which is composed of 0 and 255. Can I use them directly, or what is your image format?

What does it mean that SR = SR > treshhold in get_accuracy in evaluation? For example, my SR.max() is 8 , SR.min() is -10 for a instance. Are you supposed SR is ranged in (0:1)? Why did I get this wried results?

Thank you so much

about label issue

I change the background to 0 and others to 1 in my labels.But when I save the GT that is loaded from dataloader as a image, I get a image full of 0. Do you know why it happened?

Problems about JS=1 & DC>1

First of all, thank you for your code where I do learned a lot from. I work on a dataset looks like isic2018 where only got one category and around 900 images for training.

But I occur to a problem that when training, JS would keep equal to 1 and DC would even keeps greater than 1. I can't find reason since normalization in data_loader has already make sure DC could not larger than 1. Do you have any ideas?

I noticed things would happens when I use R2Unet and when SR output is totally black(nothing is divided out), maybe that would be helpful to find out where got something wrong.

Another problem is it seems the model cannot learn things during epoches processing, the best model would come out in first 5 epoches when training for like 200 epoches. Is that because model would get better result on small set and when it generalized to more images the score would drop?
By the way, implementing only U-net would not get DC>1 and get some result.

Thank you in advance for your help~

Edit:
Just found out JS would also stuck on 1.0 and DC greater than 1 when training Attention U-net but this time it get some result. So it maybe just JS and DC calculation problem
No way this time same situation happens to U-net and I changed nothing...

Attention-Unet : IndexError: tuple index out of range

Hey Lee,
So I am wanted to implement Attention Unet. But there is some issue coming up while using it.
I am using my own dataset and also doing a lot of preprocessing with data.
When I am using your model I get this error.

Any idea about it?
Its for medical binary segmentation

some detail in network

how can i see the result about the segmentation through the photos

the difference of train_result and valid_result!

@LeeJunHyun
Hi:
thank you for the code, i have trained the unet with the data from ISIC2018 ,but the train_result and valid_result is different, could you give me some advise? Why is the evaluation high from the beginning?
Epoch [1/100], Loss: 616.1876,
[Training] Acc: 0.9034, SE: 0.7722, SP: 0.9790, PC: 0.8181, F1: 0.7387, JS: 0.6296, DC: 0.7387
[Validation] Acc: 0.8565, SE: 0.4684, SP: 0.9901, PC: 0.6672, F1: 0.4806, JS: 0.4005, DC: 0.4806
Best U_Net model score : 0.8811
Epoch [2/100], Loss: 398.6359,
[Training] Acc: 0.9281, SE: 0.8297, SP: 0.9785, PC: 0.8587, F1: 0.8081, JS: 0.7108, DC: 0.8081
[Validation] Acc: 0.8758, SE: 0.5535, SP: 0.9764, PC: 0.7565, F1: 0.5566, JS: 0.4618, DC: 0.5566
Best U_Net model score : 1.0184
Epoch [3/100], Loss: 340.5711,
[Training] Acc: 0.9354, SE: 0.8442, SP: 0.9802, PC: 0.8693, F1: 0.8246, JS: 0.7319, DC: 0.8246
[Validation] Acc: 0.8137, SE: 0.4210, SP: 0.9514, PC: 0.5229, F1: 0.3643, JS: 0.2888, DC: 0.3643
Epoch [4/100], Loss: 320.5319,
[Training] Acc: 0.9374, SE: 0.8481, SP: 0.9785, PC: 0.8721, F1: 0.8273, JS: 0.7371, DC: 0.8273
[Validation] Acc: 0.8686, SE: 0.4233, SP: 0.9935, PC: 0.6778, F1: 0.4639, JS: 0.3859, DC: 0.4639
Epoch [5/100], Loss: 291.2066,
[Training] Acc: 0.9426, SE: 0.8587, SP: 0.9787, PC: 0.8788, F1: 0.8391, JS: 0.7521, DC: 0.8391
[Validation] Acc: 0.8385, SE: 0.3736, SP: 0.9840, PC: 0.5603, F1: 0.3846, JS: 0.3200, DC: 0.3846
Epoch [6/100], Loss: 268.9106,
[Training] Acc: 0.9467, SE: 0.8685, SP: 0.9784, PC: 0.8816, F1: 0.8483, JS: 0.7636, DC: 0.8483
[Validation] Acc: 0.8908, SE: 0.6225, SP: 0.9703, PC: 0.7103, F1: 0.6074, JS: 0.5185, DC: 0.6074
Best U_Net model score : 1.1259
Epoch [7/100], Loss: 268.7711,
[Training] Acc: 0.9445, SE: 0.8677, SP: 0.9770, PC: 0.8796, F1: 0.8469, JS: 0.7619, DC: 0.8469
[Validation] Acc: 0.8663, SE: 0.4388, SP: 0.9958, PC: 0.7275, F1: 0.4932, JS: 0.4153, DC: 0.4932
Epoch [8/100], Loss: 254.9294,
[Training] Acc: 0.9472, SE: 0.8714, SP: 0.9777, PC: 0.8798, F1: 0.8495, JS: 0.7647, DC: 0.8495
[Validation] Acc: 0.8714, SE: 0.6046, SP: 0.9706, PC: 0.7240, F1: 0.5918, JS: 0.4992, DC: 0.5918
Epoch [9/100], Loss: 239.5681,
[Training] Acc: 0.9492, SE: 0.8778, SP: 0.9768, PC: 0.8858, F1: 0.8571, JS: 0.7746, DC: 0.8571
[Validation] Acc: 0.8870, SE: 0.6585, SP: 0.9685, PC: 0.7207, F1: 0.6181, JS: 0.5193, DC: 0.6181
Best U_Net model score : 1.1375
Epoch [10/100], Loss: 235.1525,
[Training] Acc: 0.9498, SE: 0.8782, SP: 0.9764, PC: 0.8861, F1: 0.8595, JS: 0.7766, DC: 0.8595
[Validation] Acc: 0.8666, SE: 0.4735, SP: 0.9908, PC: 0.7191, F1: 0.5103, JS: 0.4270, DC: 0.5103
Epoch [11/100], Loss: 234.3776,
[Training] Acc: 0.9506, SE: 0.8804, SP: 0.9757, PC: 0.8870, F1: 0.8615, JS: 0.7794, DC: 0.8615
[Validation] Acc: 0.8819, SE: 0.5309, SP: 0.9843, PC: 0.6968, F1: 0.5439, JS: 0.4530, DC: 0.5439
Epoch [12/100], Loss: 227.3490,
[Training] Acc: 0.9525, SE: 0.8807, SP: 0.9762, PC: 0.8875, F1: 0.8605, JS: 0.7787, DC: 0.8605
[Validation] Acc: 0.8778, SE: 0.5388, SP: 0.9807, PC: 0.7516, F1: 0.5626, JS: 0.4659, DC: 0.5626
Epoch [13/100], Loss: 215.5042,
[Training] Acc: 0.9541, SE: 0.8852, SP: 0.9766, PC: 0.8886, F1: 0.8659, JS: 0.7854, DC: 0.8659
[Validation] Acc: 0.8602, SE: 0.3876, SP: 0.9956, PC: 0.6710, F1: 0.4320, JS: 0.3634, DC: 0.4320
Epoch [14/100], Loss: 210.8664,
[Training] Acc: 0.9544, SE: 0.8862, SP: 0.9762, PC: 0.8874, F1: 0.8650, JS: 0.7848, DC: 0.8650
[Validation] Acc: 0.8937, SE: 0.6567, SP: 0.9757, PC: 0.7512, F1: 0.6259, JS: 0.5324, DC: 0.6259
Best U_Net model score : 1.1583
Epoch [15/100], Loss: 210.2750,
[Training] Acc: 0.9549, SE: 0.8889, SP: 0.9770, PC: 0.8924, F1: 0.8697, JS: 0.7906, DC: 0.8697
[Validation] Acc: 0.8631, SE: 0.5639, SP: 0.9737, PC: 0.7506, F1: 0.5617, JS: 0.4642, DC: 0.5617
Epoch [16/100], Loss: 202.6749,
[Training] Acc: 0.9555, SE: 0.8905, SP: 0.9765, PC: 0.8926, F1: 0.8706, JS: 0.7909, DC: 0.8706
[Validation] Acc: 0.8478, SE: 0.3574, SP: 0.9961, PC: 0.5989, F1: 0.3882, JS: 0.3243, DC: 0.3882
Epoch [17/100], Loss: 191.1452,
[Training] Acc: 0.9585, SE: 0.8938, SP: 0.9770, PC: 0.8916, F1: 0.8729, JS: 0.7938, DC: 0.8729
[Validation] Acc: 0.8814, SE: 0.7139, SP: 0.9476, PC: 0.7399, F1: 0.6435, JS: 0.5461, DC: 0.6435
Best U_Net model score : 1.1896
Epoch [18/100], Loss: 203.5803,
[Training] Acc: 0.9555, SE: 0.8900, SP: 0.9771, PC: 0.8930, F1: 0.8703, JS: 0.7914, DC: 0.8703
[Validation] Acc: 0.8602, SE: 0.3996, SP: 0.9807, PC: 0.5780, F1: 0.4198, JS: 0.3436, DC: 0.4198
Epoch [19/100], Loss: 192.3391,
[Training] Acc: 0.9580, SE: 0.8947, SP: 0.9761, PC: 0.8931, F1: 0.8754, JS: 0.7971, DC: 0.8754
[Validation] Acc: 0.8673, SE: 0.4138, SP: 0.9951, PC: 0.6683, F1: 0.4570, JS: 0.3880, DC: 0.4570
Epoch [20/100], Loss: 191.9962,
[Training] Acc: 0.9582, SE: 0.8967, SP: 0.9770, PC: 0.8919, F1: 0.8743, JS: 0.7967, DC: 0.8743
[Validation] Acc: 0.8756, SE: 0.4957, SP: 0.9798, PC: 0.6777, F1: 0.5077, JS: 0.4248, DC: 0.5077
Epoch [21/100], Loss: 190.4904,
[Training] Acc: 0.9585, SE: 0.8967, SP: 0.9761, PC: 0.8925, F1: 0.8759, JS: 0.7978, DC: 0.8759
[Validation] Acc: 0.8679, SE: 0.5535, SP: 0.9696, PC: 0.6921, F1: 0.5424, JS: 0.4508, DC: 0.5424
Epoch [22/100], Loss: 185.0105,
[Training] Acc: 0.9600, SE: 0.9003, SP: 0.9770, PC: 0.8951, F1: 0.8798, JS: 0.8030, DC: 0.8798
[Validation] Acc: 0.8789, SE: 0.6378, SP: 0.9576, PC: 0.6817, F1: 0.5850, JS: 0.4878, DC: 0.5850
Epoch [23/100], Loss: 182.5417,
[Training] Acc: 0.9596, SE: 0.8995, SP: 0.9767, PC: 0.8945, F1: 0.8790, JS: 0.8032, DC: 0.8790
[Validation] Acc: 0.8602, SE: 0.4950, SP: 0.9883, PC: 0.6783, F1: 0.4958, JS: 0.4141, DC: 0.4958
Epoch [24/100], Loss: 183.4632,
[Training] Acc: 0.9601, SE: 0.9005, SP: 0.9768, PC: 0.8950, F1: 0.8798, JS: 0.8035, DC: 0.8798
[Validation] Acc: 0.8804, SE: 0.6130, SP: 0.9796, PC: 0.7332, F1: 0.5899, JS: 0.4913, DC: 0.5899
Epoch [25/100], Loss: 172.4488,
[Training] Acc: 0.9622, SE: 0.9043, SP: 0.9758, PC: 0.8969, F1: 0.8846, JS: 0.8093, DC: 0.8846
[Validation] Acc: 0.8547, SE: 0.4404, SP: 0.9950, PC: 0.6456, F1: 0.4643, JS: 0.3986, DC: 0.4643
Epoch [26/100], Loss: 175.5711,
[Training] Acc: 0.9611, SE: 0.9026, SP: 0.9763, PC: 0.8965, F1: 0.8827, JS: 0.8076, DC: 0.8827
[Validation] Acc: 0.8671, SE: 0.4905, SP: 0.9912, PC: 0.7205, F1: 0.5126, JS: 0.4334, DC: 0.5126
Epoch [27/100], Loss: 165.1405,
[Training] Acc: 0.9633, SE: 0.9072, SP: 0.9766, PC: 0.8961, F1: 0.8853, JS: 0.8098, DC: 0.8853
[Validation] Acc: 0.8669, SE: 0.4838, SP: 0.9912, PC: 0.7026, F1: 0.5064, JS: 0.4288, DC: 0.5064
Epoch [28/100], Loss: 172.5805,
[Training] Acc: 0.9619, SE: 0.9027, SP: 0.9771, PC: 0.8966, F1: 0.8828, JS: 0.8072, DC: 0.8828
Decay learning rate to lr: 0.00011421844870607387.
[Validation] Acc: 0.8818, SE: 0.7281, SP: 0.9499, PC: 0.6922, F1: 0.6223, JS: 0.5203, DC: 0.6223
Epoch [29/100], Loss: 165.0072,
[Training] Acc: 0.9633, SE: 0.9076, SP: 0.9763, PC: 0.8961, F1: 0.8857, JS: 0.8111, DC: 0.8857
Decay learning rate to lr: 0.00011263208136293396.
[Validation] Acc: 0.8596, SE: 0.4434, SP: 0.9904, PC: 0.6226, F1: 0.4550, JS: 0.3834, DC: 0.4550
Epoch [30/100], Loss: 175.4593,
[Training] Acc: 0.9617, SE: 0.9058, SP: 0.9756, PC: 0.8969, F1: 0.8852, JS: 0.8096, DC: 0.8852
Decay learning rate to lr: 0.00011104571401979405.
[Validation] Acc: 0.8681, SE: 0.4951, SP: 0.9931, PC: 0.7088, F1: 0.5199, JS: 0.4469, DC: 0.5199
Epoch [31/100], Loss: 158.7435,
[Training] Acc: 0.9644, SE: 0.9075, SP: 0.9757, PC: 0.8981, F1: 0.8878, JS: 0.8131, DC: 0.8878
Decay learning rate to lr: 0.00010945934667665414.
[Validation] Acc: 0.8584, SE: 0.4345, SP: 0.9936, PC: 0.6534, F1: 0.4599, JS: 0.3897, DC: 0.4599
Epoch [32/100], Loss: 172.6409,
[Training] Acc: 0.9624, SE: 0.9062, SP: 0.9756, PC: 0.8972, F1: 0.8852, JS: 0.8106, DC: 0.8852
Decay learning rate to lr: 0.00010787297933351423.
[Validation] Acc: 0.8719, SE: 0.5285, SP: 0.9778, PC: 0.6969, F1: 0.5276, JS: 0.4429, DC: 0.5276
Epoch [33/100], Loss: 159.3884,
[Training] Acc: 0.9639, SE: 0.9094, SP: 0.9750, PC: 0.8975, F1: 0.8878, JS: 0.8135, DC: 0.8878
Decay learning rate to lr: 0.00010628661199037432.
[Validation] Acc: 0.8675, SE: 0.5368, SP: 0.9867, PC: 0.6657, F1: 0.5260, JS: 0.4449, DC: 0.5260
Epoch [34/100], Loss: 163.9595,
[Training] Acc: 0.9643, SE: 0.9100, SP: 0.9760, PC: 0.8991, F1: 0.8896, JS: 0.8166, DC: 0.8896
Decay learning rate to lr: 0.00010470024464723441.
[Validation] Acc: 0.8849, SE: 0.7706, SP: 0.9392, PC: 0.7240, F1: 0.6617, JS: 0.5625, DC: 0.6617
Best U_Net model score : 1.2242
Epoch [35/100], Loss: 155.4412,
[Training] Acc: 0.9651, SE: 0.9101, SP: 0.9765, PC: 0.8995, F1: 0.8902, JS: 0.8169, DC: 0.8902
Decay learning rate to lr: 0.0001031138773040945.
[Validation] Acc: 0.8626, SE: 0.4245, SP: 0.9861, PC: 0.6021, F1: 0.4391, JS: 0.3667, DC: 0.4391
Epoch [36/100], Loss: 166.6415,
[Training] Acc: 0.9631, SE: 0.9060, SP: 0.9757, PC: 0.8972, F1: 0.8860, JS: 0.8118, DC: 0.8860
Decay learning rate to lr: 0.00010152750996095459.
[Validation] Acc: 0.8849, SE: 0.6580, SP: 0.9658, PC: 0.7212, F1: 0.6112, JS: 0.5165, DC: 0.6112
Epoch [37/100], Loss: 156.8936,
[Training] Acc: 0.9645, SE: 0.9084, SP: 0.9761, PC: 0.8982, F1: 0.8885, JS: 0.8148, DC: 0.8885
Decay learning rate to lr: 9.994114261781468e-05.
[Validation] Acc: 0.8381, SE: 0.3261, SP: 0.9949, PC: 0.5530, F1: 0.3419, JS: 0.2840, DC: 0.3419
Epoch [38/100], Loss: 153.6247,
[Training] Acc: 0.9656, SE: 0.9129, SP: 0.9736, PC: 0.9010, F1: 0.8931, JS: 0.8206, DC: 0.8931
Decay learning rate to lr: 9.835477527467477e-05.
[Validation] Acc: 0.8735, SE: 0.5074, SP: 0.9846, PC: 0.6922, F1: 0.5077, JS: 0.4240, DC: 0.5077
Epoch [39/100], Loss: 155.5283,
[Training] Acc: 0.9662, SE: 0.9132, SP: 0.9773, PC: 0.8989, F1: 0.8916, JS: 0.8187, DC: 0.8916
Decay learning rate to lr: 9.676840793153486e-05.
[Validation] Acc: 0.8601, SE: 0.4192, SP: 0.9891, PC: 0.6524, F1: 0.4478, JS: 0.3752, DC: 0.4478
Epoch [40/100], Loss: 151.9283,
[Training] Acc: 0.9651, SE: 0.9116, SP: 0.9748, PC: 0.8999, F1: 0.8910, JS: 0.8183, DC: 0.8910
Decay learning rate to lr: 9.518204058839495e-05.
[Validation] Acc: 0.8474, SE: 0.3625, SP: 0.9926, PC: 0.6137, F1: 0.3866, JS: 0.3206, DC: 0.3866
Epoch [41/100], Loss: 149.1667,
[Training] Acc: 0.9671, SE: 0.9157, SP: 0.9766, PC: 0.9018, F1: 0.8954, JS: 0.8234, DC: 0.8954
Decay learning rate to lr: 9.359567324525504e-05.
[Validation] Acc: 0.8795, SE: 0.6205, SP: 0.9780, PC: 0.7092, F1: 0.5919, JS: 0.4968, DC: 0.5919
Epoch [42/100], Loss: 144.5024,
[Training] Acc: 0.9674, SE: 0.9181, SP: 0.9759, PC: 0.9023, F1: 0.8974, JS: 0.8260, DC: 0.8974
Decay learning rate to lr: 9.200930590211513e-05.
[Validation] Acc: 0.8732, SE: 0.5549, SP: 0.9854, PC: 0.7127, F1: 0.5510, JS: 0.4626, DC: 0.5510
Epoch [43/100], Loss: 151.6849,
[Training] Acc: 0.9661, SE: 0.9147, SP: 0.9752, PC: 0.9010, F1: 0.8946, JS: 0.8226, DC: 0.8946
Decay learning rate to lr: 9.042293855897522e-05.
[Validation] Acc: 0.8626, SE: 0.4704, SP: 0.9934, PC: 0.7154, F1: 0.4976, JS: 0.4180, DC: 0.4976
Epoch [44/100], Loss: 150.5371,
[Training] Acc: 0.9662, SE: 0.9132, SP: 0.9762, PC: 0.9005, F1: 0.8925, JS: 0.8209, DC: 0.8925
Decay learning rate to lr: 8.883657121583531e-05.
[Validation] Acc: 0.8754, SE: 0.5314, SP: 0.9848, PC: 0.7298, F1: 0.5441, JS: 0.4539, DC: 0.5441
Epoch [45/100], Loss: 148.0798,
[Training] Acc: 0.9671, SE: 0.9177, SP: 0.9763, PC: 0.9024, F1: 0.8970, JS: 0.8265, DC: 0.8970
Decay learning rate to lr: 8.72502038726954e-05.
[Validation] Acc: 0.8690, SE: 0.4646, SP: 0.9943, PC: 0.7114, F1: 0.4996, JS: 0.4236, DC: 0.4996
Epoch [46/100], Loss: 134.4733,
[Training] Acc: 0.9695, SE: 0.9201, SP: 0.9762, PC: 0.9029, F1: 0.8991, JS: 0.8285, DC: 0.8991
Decay learning rate to lr: 8.566383652955549e-05.
[Validation] Acc: 0.8679, SE: 0.5878, SP: 0.9576, PC: 0.6365, F1: 0.5286, JS: 0.4398, DC: 0.5286
Epoch [47/100], Loss: 142.5119,
[Training] Acc: 0.9674, SE: 0.9185, SP: 0.9749, PC: 0.9015, F1: 0.8962, JS: 0.8254, DC: 0.8962
Decay learning rate to lr: 8.407746918641558e-05.
[Validation] Acc: 0.8480, SE: 0.3821, SP: 0.9937, PC: 0.5798, F1: 0.3997, JS: 0.3380, DC: 0.3997
Epoch [48/100], Loss: 154.5033,
[Training] Acc: 0.9666, SE: 0.9182, SP: 0.9748, PC: 0.9024, F1: 0.8973, JS: 0.8267, DC: 0.8973
Decay learning rate to lr: 8.249110184327567e-05.
[Validation] Acc: 0.8576, SE: 0.3789, SP: 0.9932, PC: 0.6079, F1: 0.4055, JS: 0.3419, DC: 0.4055
Epoch [49/100], Loss: 132.7678,
[Training] Acc: 0.9695, SE: 0.9216, SP: 0.9762, PC: 0.9037, F1: 0.9014, JS: 0.8320, DC: 0.9014
Decay learning rate to lr: 8.090473450013576e-05.
[Validation] Acc: 0.8607, SE: 0.4490, SP: 0.9907, PC: 0.6172, F1: 0.4580, JS: 0.3901, DC: 0.4580
Epoch [50/100], Loss: 135.9399,
[Training] Acc: 0.9695, SE: 0.9216, SP: 0.9764, PC: 0.9028, F1: 0.9005, JS: 0.8305, DC: 0.9006
Decay learning rate to lr: 7.931836715699585e-05.
[Validation] Acc: 0.8806, SE: 0.5015, SP: 0.9903, PC: 0.7104, F1: 0.5225, JS: 0.4403, DC: 0.5225
Epoch [51/100], Loss: 132.9578,
[Training] Acc: 0.9692, SE: 0.9218, SP: 0.9756, PC: 0.9048, F1: 0.9018, JS: 0.8317, DC: 0.9018
Decay learning rate to lr: 7.773199981385594e-05.
[Validation] Acc: 0.8362, SE: 0.2915, SP: 0.9979, PC: 0.5544, F1: 0.3252, JS: 0.2741, DC: 0.3252
Epoch [52/100], Loss: 134.7240,
[Training] Acc: 0.9691, SE: 0.9224, SP: 0.9756, PC: 0.9042, F1: 0.9017, JS: 0.8321, DC: 0.9017
Decay learning rate to lr: 7.614563247071603e-05.
[Validation] Acc: 0.8628, SE: 0.4795, SP: 0.9916, PC: 0.6601, F1: 0.4853, JS: 0.4079, DC: 0.4853
Epoch [53/100], Loss: 137.5579,
[Training] Acc: 0.9687, SE: 0.9198, SP: 0.9757, PC: 0.9035, F1: 0.8989, JS: 0.8291, DC: 0.8989
Decay learning rate to lr: 7.455926512757612e-05.
[Validation] Acc: 0.8373, SE: 0.2815, SP: 0.9977, PC: 0.4900, F1: 0.3105, JS: 0.2634, DC: 0.3105
Epoch [54/100], Loss: 129.5814,
[Training] Acc: 0.9698, SE: 0.9255, SP: 0.9757, PC: 0.9042, F1: 0.9041, JS: 0.8353, DC: 0.9041
Decay learning rate to lr: 7.297289778443621e-05.
[Validation] Acc: 0.8768, SE: 0.5064, SP: 0.9883, PC: 0.7182, F1: 0.5200, JS: 0.4341, DC: 0.5200
Epoch [55/100], Loss: 128.9607,
[Training] Acc: 0.9703, SE: 0.9237, SP: 0.9754, PC: 0.9052, F1: 0.9031, JS: 0.8337, DC: 0.9031
Decay learning rate to lr: 7.13865304412963e-05.
[Validation] Acc: 0.8811, SE: 0.6047, SP: 0.9803, PC: 0.7549, F1: 0.5893, JS: 0.4919, DC: 0.5893
Epoch [56/100], Loss: 128.9532,
[Training] Acc: 0.9700, SE: 0.9231, SP: 0.9772, PC: 0.9047, F1: 0.9020, JS: 0.8328, DC: 0.9020
Decay learning rate to lr: 6.980016309815639e-05.
[Validation] Acc: 0.8550, SE: 0.4100, SP: 0.9934, PC: 0.6078, F1: 0.4294, JS: 0.3644, DC: 0.4294
Epoch [57/100], Loss: 129.5934,
[Training] Acc: 0.9710, SE: 0.9257, SP: 0.9762, PC: 0.9044, F1: 0.9043, JS: 0.8368, DC: 0.9043
Decay learning rate to lr: 6.821379575501648e-05.
[Validation] Acc: 0.8531, SE: 0.4024, SP: 0.9960, PC: 0.6601, F1: 0.4330, JS: 0.3699, DC: 0.4330
Epoch [58/100], Loss: 128.0230,
[Training] Acc: 0.9706, SE: 0.9255, SP: 0.9778, PC: 0.9069, F1: 0.9050, JS: 0.8367, DC: 0.9050
Decay learning rate to lr: 6.662742841187657e-05.
[Validation] Acc: 0.8653, SE: 0.4470, SP: 0.9906, PC: 0.6303, F1: 0.4607, JS: 0.3883, DC: 0.4607
Epoch [59/100], Loss: 123.0228,
[Training] Acc: 0.9720, SE: 0.9290, SP: 0.9762, PC: 0.9065, F1: 0.9073, JS: 0.8401, DC: 0.9073
Decay learning rate to lr: 6.504106106873666e-05.
[Validation] Acc: 0.8283, SE: 0.2809, SP: 0.9983, PC: 0.4528, F1: 0.3051, JS: 0.2593, DC: 0.3051
Epoch [60/100], Loss: 130.3902,
[Training] Acc: 0.9706, SE: 0.9260, SP: 0.9770, PC: 0.9051, F1: 0.9045, JS: 0.8364, DC: 0.9045
Decay learning rate to lr: 6.345469372559675e-05.
[Validation] Acc: 0.8359, SE: 0.2593, SP: 0.9984, PC: 0.5649, F1: 0.2984, JS: 0.2458, DC: 0.2984
Epoch [61/100], Loss: 122.7981,
[Training] Acc: 0.9713, SE: 0.9274, SP: 0.9758, PC: 0.9058, F1: 0.9055, JS: 0.8380, DC: 0.9055
Decay learning rate to lr: 6.186832638245684e-05.
[Validation] Acc: 0.8321, SE: 0.2448, SP: 0.9982, PC: 0.4488, F1: 0.2759, JS: 0.2312, DC: 0.2759
Epoch [62/100], Loss: 133.0434,
[Training] Acc: 0.9709, SE: 0.9258, SP: 0.9767, PC: 0.9066, F1: 0.9051, JS: 0.8376, DC: 0.9051
Decay learning rate to lr: 6.028195903931692e-05.
[Validation] Acc: 0.8631, SE: 0.4801, SP: 0.9922, PC: 0.7014, F1: 0.4991, JS: 0.4219, DC: 0.4991
Epoch [63/100], Loss: 117.8739,
[Training] Acc: 0.9725, SE: 0.9315, SP: 0.9749, PC: 0.9069, F1: 0.9094, JS: 0.8429, DC: 0.9094
Decay learning rate to lr: 5.8695591696177006e-05.
[Validation] Acc: 0.8605, SE: 0.3999, SP: 0.9923, PC: 0.6408, F1: 0.4280, JS: 0.3599, DC: 0.4280
Epoch [64/100], Loss: 127.6605,
[Training] Acc: 0.9711, SE: 0.9297, SP: 0.9756, PC: 0.9067, F1: 0.9078, JS: 0.8405, DC: 0.9078
Decay learning rate to lr: 5.710922435303709e-05.
[Validation] Acc: 0.8230, SE: 0.2021, SP: 0.9992, PC: 0.4158, F1: 0.2313, JS: 0.1916, DC: 0.2313
Epoch [65/100], Loss: 128.5371,
[Training] Acc: 0.9711, SE: 0.9305, SP: 0.9759, PC: 0.9075, F1: 0.9088, JS: 0.8425, DC: 0.9088
Decay learning rate to lr: 5.552285700989717e-05.
[Validation] Acc: 0.8552, SE: 0.3914, SP: 0.9823, PC: 0.5981, F1: 0.3986, JS: 0.3271, DC: 0.3986
Epoch [66/100], Loss: 118.5605,
[Training] Acc: 0.9728, SE: 0.9315, SP: 0.9774, PC: 0.9074, F1: 0.9093, JS: 0.8431, DC: 0.9093
Decay learning rate to lr: 5.3936489666757256e-05.
[Validation] Acc: 0.8552, SE: 0.3702, SP: 0.9854, PC: 0.6322, F1: 0.3886, JS: 0.3183, DC: 0.3886
Epoch [67/100], Loss: 120.1187,
[Training] Acc: 0.9726, SE: 0.9323, SP: 0.9766, PC: 0.9069, F1: 0.9098, JS: 0.8438, DC: 0.9098
Decay learning rate to lr: 5.235012232361734e-05.
[Validation] Acc: 0.8365, SE: 0.2735, SP: 0.9978, PC: 0.4829, F1: 0.3005, JS: 0.2550, DC: 0.3005
Epoch [68/100], Loss: 115.3787,
[Training] Acc: 0.9732, SE: 0.9331, SP: 0.9780, PC: 0.9084, F1: 0.9115, JS: 0.8460, DC: 0.9115
Decay learning rate to lr: 5.076375498047742e-05.
[Validation] Acc: 0.8570, SE: 0.4180, SP: 0.9947, PC: 0.6673, F1: 0.4447, JS: 0.3768, DC: 0.4447
Epoch [69/100], Loss: 112.7998,
[Training] Acc: 0.9734, SE: 0.9329, SP: 0.9766, PC: 0.9083, F1: 0.9115, JS: 0.8459, DC: 0.9115
Decay learning rate to lr: 4.9177387637337506e-05.
[Validation] Acc: 0.8590, SE: 0.4145, SP: 0.9868, PC: 0.5589, F1: 0.4077, JS: 0.3382, DC: 0.4077
Epoch [70/100], Loss: 117.0291,
[Training] Acc: 0.9731, SE: 0.9315, SP: 0.9768, PC: 0.9082, F1: 0.9102, JS: 0.8444, DC: 0.9102
Decay learning rate to lr: 4.759102029419759e-05.
[Validation] Acc: 0.8575, SE: 0.3105, SP: 0.9938, PC: 0.5990, F1: 0.3534, JS: 0.2878, DC: 0.3534
Epoch [71/100], Loss: 111.5511,
[Training] Acc: 0.9739, SE: 0.9315, SP: 0.9786, PC: 0.9082, F1: 0.9103, JS: 0.8447, DC: 0.9103
Decay learning rate to lr: 4.600465295105767e-05.
[Validation] Acc: 0.8644, SE: 0.4540, SP: 0.9872, PC: 0.6522, F1: 0.4710, JS: 0.3954, DC: 0.4710
Epoch [72/100], Loss: 111.7463,
[Training] Acc: 0.9736, SE: 0.9351, SP: 0.9767, PC: 0.9070, F1: 0.9120, JS: 0.8468, DC: 0.9120
Decay learning rate to lr: 4.4418285607917756e-05.
[Validation] Acc: 0.8690, SE: 0.4672, SP: 0.9919, PC: 0.7014, F1: 0.4944, JS: 0.4190, DC: 0.4944
Epoch [73/100], Loss: 113.9828,
[Training] Acc: 0.9736, SE: 0.9342, SP: 0.9778, PC: 0.9080, F1: 0.9120, JS: 0.8468, DC: 0.9120
Decay learning rate to lr: 4.283191826477784e-05.
[Validation] Acc: 0.8249, SE: 0.1878, SP: 0.9992, PC: 0.3749, F1: 0.2150, JS: 0.1798, DC: 0.2150
Epoch [74/100], Loss: 114.5390,
[Training] Acc: 0.9743, SE: 0.9362, SP: 0.9768, PC: 0.9091, F1: 0.9138, JS: 0.8498, DC: 0.9138
Decay learning rate to lr: 4.124555092163792e-05.
[Validation] Acc: 0.8589, SE: 0.4451, SP: 0.9888, PC: 0.6534, F1: 0.4581, JS: 0.3879, DC: 0.4581
Epoch [75/100], Loss: 114.1872,
[Training] Acc: 0.9738, SE: 0.9342, SP: 0.9771, PC: 0.9079, F1: 0.9118, JS: 0.8476, DC: 0.9118
Decay learning rate to lr: 3.9659183578498005e-05.
[Validation] Acc: 0.8792, SE: 0.4889, SP: 0.9894, PC: 0.6991, F1: 0.5079, JS: 0.4266, DC: 0.5079
Epoch [76/100], Loss: 104.4477,
[Training] Acc: 0.9753, SE: 0.9391, SP: 0.9782, PC: 0.9095, F1: 0.9158, JS: 0.8525, DC: 0.9158
Decay learning rate to lr: 3.807281623535809e-05.
[Validation] Acc: 0.8809, SE: 0.5931, SP: 0.9759, PC: 0.7317, F1: 0.5736, JS: 0.4811, DC: 0.5736
Epoch [77/100], Loss: 110.7846,
[Training] Acc: 0.9747, SE: 0.9372, SP: 0.9758, PC: 0.9095, F1: 0.9145, JS: 0.8513, DC: 0.9145
Decay learning rate to lr: 3.648644889221817e-05.
[Validation] Acc: 0.8463, SE: 0.3824, SP: 0.9961, PC: 0.6520, F1: 0.4095, JS: 0.3465, DC: 0.4095
Epoch [78/100], Loss: 111.5872,
[Training] Acc: 0.9746, SE: 0.9378, SP: 0.9767, PC: 0.9086, F1: 0.9143, JS: 0.8507, DC: 0.9143
Decay learning rate to lr: 3.4900081549078255e-05.
[Validation] Acc: 0.8217, SE: 0.2495, SP: 0.9973, PC: 0.4141, F1: 0.2590, JS: 0.2165, DC: 0.2590
Epoch [79/100], Loss: 107.1365,
[Training] Acc: 0.9752, SE: 0.9386, SP: 0.9772, PC: 0.9084, F1: 0.9147, JS: 0.8512, DC: 0.9147
Decay learning rate to lr: 3.331371420593834e-05.
[Validation] Acc: 0.8203, SE: 0.1925, SP: 0.9987, PC: 0.4340, F1: 0.2168, JS: 0.1793, DC: 0.2168
Epoch [80/100], Loss: 110.9553,
[Training] Acc: 0.9747, SE: 0.9401, SP: 0.9775, PC: 0.9104, F1: 0.9167, JS: 0.8541, DC: 0.9167
Decay learning rate to lr: 3.172734686279842e-05.
[Validation] Acc: 0.8400, SE: 0.3086, SP: 0.9943, PC: 0.5604, F1: 0.3288, JS: 0.2702, DC: 0.3288
Epoch [81/100], Loss: 107.6854,
[Training] Acc: 0.9747, SE: 0.9379, SP: 0.9757, PC: 0.9097, F1: 0.9153, JS: 0.8520, DC: 0.9153
Decay learning rate to lr: 3.014097951965851e-05.
[Validation] Acc: 0.8590, SE: 0.4347, SP: 0.9888, PC: 0.6858, F1: 0.4561, JS: 0.3730, DC: 0.4561
Epoch [82/100], Loss: 100.4412,
[Training] Acc: 0.9763, SE: 0.9421, SP: 0.9773, PC: 0.9112, F1: 0.9192, JS: 0.8574, DC: 0.9192
Decay learning rate to lr: 2.8554612176518595e-05.
[Validation] Acc: 0.8586, SE: 0.4027, SP: 0.9890, PC: 0.6283, F1: 0.4167, JS: 0.3446, DC: 0.4167
Epoch [83/100], Loss: 100.5839,
[Training] Acc: 0.9765, SE: 0.9410, SP: 0.9769, PC: 0.9100, F1: 0.9177, JS: 0.8555, DC: 0.9177
Decay learning rate to lr: 2.6968244833378682e-05.
[Validation] Acc: 0.8735, SE: 0.5046, SP: 0.9875, PC: 0.6966, F1: 0.5143, JS: 0.4310, DC: 0.5143
Epoch [84/100], Loss: 103.8184,
[Training] Acc: 0.9758, SE: 0.9386, SP: 0.9776, PC: 0.9094, F1: 0.9157, JS: 0.8532, DC: 0.9157
Decay learning rate to lr: 2.538187749023877e-05.
[Validation] Acc: 0.8493, SE: 0.3664, SP: 0.9941, PC: 0.6099, F1: 0.3903, JS: 0.3255, DC: 0.3903
Epoch [85/100], Loss: 99.9430,
[Training] Acc: 0.9762, SE: 0.9425, SP: 0.9774, PC: 0.9101, F1: 0.9187, JS: 0.8567, DC: 0.9187
Decay learning rate to lr: 2.3795510147098855e-05.
[Validation] Acc: 0.8771, SE: 0.5697, SP: 0.9862, PC: 0.7346, F1: 0.5689, JS: 0.4821, DC: 0.5689
Epoch [86/100], Loss: 98.2186,
[Training] Acc: 0.9769, SE: 0.9432, SP: 0.9774, PC: 0.9120, F1: 0.9199, JS: 0.8589, DC: 0.9199
Decay learning rate to lr: 2.2209142803958942e-05.
[Validation] Acc: 0.8345, SE: 0.2655, SP: 0.9970, PC: 0.5850, F1: 0.3023, JS: 0.2480, DC: 0.3023
Epoch [87/100], Loss: 96.0286,
[Training] Acc: 0.9779, SE: 0.9459, SP: 0.9791, PC: 0.9114, F1: 0.9216, JS: 0.8610, DC: 0.9216
Decay learning rate to lr: 2.062277546081903e-05.
[Validation] Acc: 0.8534, SE: 0.3963, SP: 0.9900, PC: 0.6184, F1: 0.4122, JS: 0.3423, DC: 0.4122
Epoch [88/100], Loss: 95.6034,
[Training] Acc: 0.9776, SE: 0.9456, SP: 0.9782, PC: 0.9116, F1: 0.9217, JS: 0.8614, DC: 0.9217
Decay learning rate to lr: 1.9036408117679116e-05.
[Validation] Acc: 0.8494, SE: 0.3946, SP: 0.9945, PC: 0.6363, F1: 0.4100, JS: 0.3464, DC: 0.4100
Epoch [89/100], Loss: 103.0424,
[Training] Acc: 0.9763, SE: 0.9436, SP: 0.9764, PC: 0.9108, F1: 0.9194, JS: 0.8582, DC: 0.9194
Decay learning rate to lr: 1.7450040774539202e-05.
[Validation] Acc: 0.8722, SE: 0.5715, SP: 0.9749, PC: 0.7317, F1: 0.5528, JS: 0.4574, DC: 0.5528
Epoch [90/100], Loss: 94.8561,
[Training] Acc: 0.9779, SE: 0.9454, SP: 0.9788, PC: 0.9129, F1: 0.9223, JS: 0.8623, DC: 0.9223
Decay learning rate to lr: 1.586367343139929e-05.
[Validation] Acc: 0.8499, SE: 0.3627, SP: 0.9857, PC: 0.5811, F1: 0.3696, JS: 0.2977, DC: 0.3696
Epoch [91/100], Loss: 97.1100,
[Training] Acc: 0.9772, SE: 0.9441, SP: 0.9789, PC: 0.9123, F1: 0.9209, JS: 0.8604, DC: 0.9209
Decay learning rate to lr: 1.4277306088259374e-05.
[Validation] Acc: 0.8549, SE: 0.3847, SP: 0.9918, PC: 0.6167, F1: 0.4026, JS: 0.3344, DC: 0.4026
Epoch [92/100], Loss: 97.6109,
[Training] Acc: 0.9772, SE: 0.9463, SP: 0.9780, PC: 0.9128, F1: 0.9219, JS: 0.8623, DC: 0.9219
Decay learning rate to lr: 1.2690938745119459e-05.
[Validation] Acc: 0.8810, SE: 0.5486, SP: 0.9763, PC: 0.6881, F1: 0.5342, JS: 0.4462, DC: 0.5342
Epoch [93/100], Loss: 98.1793,
[Training] Acc: 0.9771, SE: 0.9475, SP: 0.9777, PC: 0.9124, F1: 0.9231, JS: 0.8634, DC: 0.9231
Decay learning rate to lr: 1.1104571401979544e-05.
[Validation] Acc: 0.8654, SE: 0.5406, SP: 0.9752, PC: 0.6586, F1: 0.5128, JS: 0.4222, DC: 0.5128
Epoch [94/100], Loss: 95.9679,
[Training] Acc: 0.9780, SE: 0.9477, SP: 0.9769, PC: 0.9127, F1: 0.9232, JS: 0.8641, DC: 0.9232
Decay learning rate to lr: 9.518204058839629e-06.
[Validation] Acc: 0.8524, SE: 0.3428, SP: 0.9933, PC: 0.6244, F1: 0.3769, JS: 0.3094, DC: 0.3769
Epoch [95/100], Loss: 97.0966,
[Training] Acc: 0.9774, SE: 0.9460, SP: 0.9774, PC: 0.9115, F1: 0.9209, JS: 0.8613, DC: 0.9209
Decay learning rate to lr: 7.931836715699714e-06.
[Validation] Acc: 0.8344, SE: 0.2619, SP: 0.9978, PC: 0.4991, F1: 0.2908, JS: 0.2430, DC: 0.2908
Epoch [96/100], Loss: 93.9771,
[Training] Acc: 0.9782, SE: 0.9485, SP: 0.9789, PC: 0.9114, F1: 0.9232, JS: 0.8641, DC: 0.9232
Decay learning rate to lr: 6.345469372559799e-06.
[Validation] Acc: 0.8573, SE: 0.4120, SP: 0.9915, PC: 0.6316, F1: 0.4266, JS: 0.3564, DC: 0.4266
Epoch [97/100], Loss: 90.2515,
[Training] Acc: 0.9787, SE: 0.9472, SP: 0.9778, PC: 0.9126, F1: 0.9225, JS: 0.8633, DC: 0.9225
Decay learning rate to lr: 4.759102029419884e-06.
[Validation] Acc: 0.8519, SE: 0.3352, SP: 0.9952, PC: 0.6188, F1: 0.3719, JS: 0.3101, DC: 0.3719
Epoch [98/100], Loss: 94.7197,
[Training] Acc: 0.9784, SE: 0.9477, SP: 0.9788, PC: 0.9125, F1: 0.9234, JS: 0.8642, DC: 0.9234
Decay learning rate to lr: 3.172734686279969e-06.
[Validation] Acc: 0.8474, SE: 0.3480, SP: 0.9933, PC: 0.6090, F1: 0.3693, JS: 0.3056, DC: 0.3693
Epoch [99/100], Loss: 95.0584,
[Training] Acc: 0.9779, SE: 0.9487, SP: 0.9775, PC: 0.9103, F1: 0.9225, JS: 0.8632, DC: 0.9225
Decay learning rate to lr: 1.5863673431400541e-06.
[Validation] Acc: 0.8265, SE: 0.1964, SP: 0.9988, PC: 0.4323, F1: 0.2260, JS: 0.1865, DC: 0.2260
Epoch [100/100], Loss: 92.9643,
[Training] Acc: 0.9780, SE: 0.9449, SP: 0.9783, PC: 0.9149, F1: 0.9227, JS: 0.8639, DC: 0.9227
Decay learning rate to lr: 1.393369198233324e-19.
[Validation] Acc: 0.8399, SE: 0.2861, SP: 0.9969, PC: 0.6008, F1: 0.3243, JS: 0.2663, DC: 0.3243

tensorflow

Information about custom dataset

Hi there I wanted to use your model to train my dataset and I was wondering what format I must label my dataset.

Thanks in advance