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This repository contains code and dataset for the task crack segmentation using two architectures UNet_VGG16, UNet_Resnet and DenseNet-Tiramusu

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Hi khanhha, thanks for providing this dataset. This is really fantastic. How did you produce the masks for the images? Which tool did you use ... I only found annotation-tools...

When I was reading train_tiramisu.py, I found that the code need to import JointRandomSizedCrop from joint_transforms. However, I did not find the joint_transforms. Whether it is a package, or you...

Can it be used for commercial purposes?

Hi khanhha, I'm a developer working on my open-sourced projects and I found that your github repo crack_segmentation very useful! Thanks a lot for your contribution to the open-sourced community!...

Traceback (most recent call last): File "/environment/python/versions/miniconda3-4.7.12/lib/python3.7/site-packages/visdom/__init__.py", line 711, in _send data=json.dumps(msg), File "/environment/python/versions/miniconda3-4.7.12/lib/python3.7/site-packages/visdom/__init__.py", line 677, in _handle_post r = self.session.post(url, data=data) File "/environment/python/versions/miniconda3-4.7.12/lib/python3.7/site-packages/requests/sessions.py", line 578, in post return self.request('POST',...

Hi @khanhha , thanks for the repo and the pretrained model. I note in inference_unet.py that you perform once inference on the entire image and then on patches of images...

Thank you very much for your sharing! Datasets about crack are really hard to find. But i've found out that some crack mask in dataset are much more wider than...

When I am trying to train the model with resnet101, I am getting the following error. Please help me figure this out. 0%| | 0/14848 [00:00