U-Net-Pytorch [Detail Report]

Semantic Image segmentation of nuclei using Deep Convolution Neural network derived from U-net. Implemented in python using PyTorch.

               Input                                        Output

Directory Tree

+checkpoints_unet
+optimizer_checkpoints_unet
+runs
+graphs_unet
+Samples
+data
	+test
	+train
	+validate
-api.py
-train_Unet.py
-data_augment.py
-networks.py

1. checkpoints_unet: Contains checkpoints for a model pretrained on Kaggle's Datascience-Bowl-2018 dataset. This will also store the checkpoints that will be used for further training.

2. optimizer_checkpoints_unet: Contains optimizer checkpoints corresponding to the ones in checkpoints_unet.(Quite useful when using momentum based optimizers for discontinuous training)

3. runs: folder created by TensorBoardX. Can be used to view the training and validation loss.

tensorboard --logdir= .

4. graph_unet: contains log of training (same data as runs) as JSON file. Can be used for plotting with Matplotlib.

5. Samples: Samples from Kaggle's Datascience-Bowl-2018 dataset run through the pretrained UNet.

6. data: The data folder contains test,training and validation folder (training and validation folder are during the training). I have provided sample processed datapoints from Kaggle's Datascience-Bowl-2018 dataset as a reference. Please note that the change in the structure and storage of datapoints has to be reflected in ImageDataset class in train_Unet.py .

7. api.py: is used to get segmented output of a given input for a pretrained neural network.It will give output in the folder containing script as Output_unet.png

python api.py /path/to/image/image_name.extension 

8. train_Unet.py: Training script for UNet. Please review the comments in the script to understand its hyperparameters and working.

9. data_augment.py: Contains data augmentation class.It is used to augment data during training the Unet.

10. networks.py: Unet Network architecture is defined here.

Note: This network is compatible with Pytorch 0.3.0 . The next version of Pytorch might cause some major syntax changes.