cs501r_f2018:lab4
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cs501r_f2018:lab4 [2018/09/25 02:16] shreeya |
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| ====Description:==== | ====Description:==== | ||
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| + | For a video including some tips and tricks that can help with this lab: [[https://youtu.be/Ms19kgK_D8w|https://youtu.be/Ms19kgK_D8w]] | ||
| For this lab, you will implement a virtual radiologist. You are given | For this lab, you will implement a virtual radiologist. You are given | ||
| Line 75: | Line 77: | ||
| {{ :cs501r_f2016:screen_shot_2017-10-10_at_10.11.55_am.png?direct&200|}} | {{ :cs501r_f2016:screen_shot_2017-10-10_at_10.11.55_am.png?direct&200|}} | ||
| - | Use the "Deep Convolution U-Net" from this paper: [[https://arxiv.org/pdf/1505.04597.pdf|U-Net: Convolutional Networks for Biomedical Image Segmentation]] (See figure 1, replicated at the right). This should be fairly easy to implement given the | + | Use the "Deep Convolution U-Net" from this paper: [[https://arxiv.org/pdf/1505.04597.pdf|U-Net: Convolutional Networks for Biomedical Image Segmentation]] (See figure 1, replicated at the right). You should use existing pytorch functions (not your own Conv2D module), such as ''nn.Conv2d''; you will also need the pytorch function ''torch.cat'' and ''nn.ConvTranspose2d'' |
| - | ''conv'' helper functions that you implemented previously; you | + | |
| - | may also need the pytorch function ''torch.cat'' and ''nn.ConvTranspose2d'' | + | |
| ''torch.cat'' allows you to concatenate tensors. ''nn.ConvTranspose2d'' is the opposite of ''nn.Conv2d''. It is used to bring an image from low res to higher res. [[https://towardsdatascience.com/up-sampling-with-transposed-convolution-9ae4f2df52d0|This blog]] should help you understand this function in detail. | ''torch.cat'' allows you to concatenate tensors. ''nn.ConvTranspose2d'' is the opposite of ''nn.Conv2d''. It is used to bring an image from low res to higher res. [[https://towardsdatascience.com/up-sampling-with-transposed-convolution-9ae4f2df52d0|This blog]] should help you understand this function in detail. | ||
| Line 104: | Line 104: | ||
| <code python> | <code python> | ||
| + | import torchvision | ||
| import os | import os | ||
| import gzip | import gzip | ||
| Line 141: | Line 142: | ||
| img = self.dataset_folder[index] | img = self.dataset_folder[index] | ||
| label = self.label_folder[index] | label = self.label_folder[index] | ||
| - | return img[0] * 255,label[0][0] | + | return img[0],label[0][0] |
| | | ||
| def __len__(self): | def __len__(self): | ||
| Line 190: | Line 191: | ||
| </code> | </code> | ||
| - | You are welcome (and encouraged) to use the built-in dropout layer. | + | You are welcome (and encouraged) to use the built-in batch normalization and dropout layer. |
| + | |||
| + | Guessing that the pixel is not cancerous every single time will give you an accuracy of ~ 85%. Your trained network should be able to do better than that (but you will not be graded on accuracy). | ||
| - | Guessing that the pixel is not cancerous every single time will give you an accuracy of ~ 85%. I will post my accuracy and loss graph for training dataset soon so you can have a baseline for what your accuracy should be like. | + | {{:cs501r_f2016:training_accuracy.png?400|}} |
| + | {{:cs501r_f2016:training_loss.png?400|}} | ||
cs501r_f2018/lab4.txt · Last modified: 2021/06/30 23:42 (external edit)
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