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Hello, my dataset is a 2D image, have you solved this problem, can we talk?

Hello, my dataset is a 2D image, have you solved this problem, can we talk?

Congratulations to the author for posting CVPR, can you share a copy of the dataset you processed！My e-mail: [email protected]

Author your help is very effective for me, thank you for your help

Hello, I'm here again and I noticed that your experiment is set up print_interval = 20 val_interval = 30 save_interval = 100 I find that you save the best weighting...

Hi!author, your work is excellent, one question I have is why you use a standard convolution with a convolution kernel of 3 as a layer at the very beginning of...

Then in the last two layers of the final decode output

I handled the data preprocessing, and but I printed the size before it was originally passed into the model as x torch.Size([8, 3, 256, 256]).The 647 at the beginning represents...

> I handled the data preprocessing, and but I printed the size before it was originally passed into the model as x torch.Size([8, 3, 256, 256]).The 647 at the beginning...

print('Train_img shape:', Train_img.shape) print('Validation_img shape:', Validation_img.shape) print('Test_img shape:', Test_img.shape) print('Train_mask shape:', Train_mask.shape) print('Validation_mask shape:', Validation_mask.shape) print('Test_mask shape:', Test_mask.shape) Train_img shape: (389, 256, 256, 3) Validation_img shape: (129, 256, 256, 3)...