NOTE : Each of these folders below also contains README files to describe the contents of each folder.

The unet models in the hw5_layer, 4_layer, 6_layers and 5_layer_stride_4 folders use the DiceLoss function

The unet models in the BCE folder use the BCELogitLoss function

The best model is present in the "best_model" folder

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Folder Name	Folder Description
hw5_layer	This folder contains the code for 5 layer Monai model used in Homework 5. This unet model
	has channels = (32, 64, 128, 256, 512) and strides = (2, 2, 2,2). Loss function used = DiceLoss The results and
	graphs of this model are saved as png files
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4_layer	This folder contains the code for 4 layer Monai model. This unet model has channels = (64, 128, 256, 512)
	and strides = (2, 2, 2). Loss function used = DiceLoss The results and graphs of this model are saved as png files.
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6_layers	This folder contains the code for 6 layer Monai model. This unet model has channels = (32, 64, 64, 128, 256, 512)
	and strides = (2, 2, 2,2,2). Loss function used = DiceLoss The results and graphs of this model are saved as png files
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5_layer_stride_4	This folder contains the code for 5 layer Monai model where value of the strides are inccreased. This unet
	model has channels = (32, 64, 128, 256, 512) and strides = (4, 4, 4,4). Loss function used = DiceLoss.
	The results and graphs of this model are saved as png files.
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BCE (this has sub-	This folder contains the code of pytorch model (in the "pytorch" subfolder) and Monai model (in the "monai" subfolder)
folders "pytorch"	Both the models have to same number of layers, however the pytorch model is created using the convolutional layers using
and "monai"	pytorch library and the monai unet model is created using the convolutional blocks provided by monai.
	These models use the BCE loss function instead of dice loss
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best_model	This folder contains the model files of the best performing models and also the ipymb files of the specific unet models
	being tested. the ipymb files are:
	1) testing-monai-ADN-70-epochs : contains the monai unet model created using ADN Blocks trained for 70 epochs
	2) testing-monai-ADN-200-epochs : contains the monai unet created using ADN Blocks trained for 200 epochs
	3) testing-monai-replicate-noADN : contains the monai unet model created Conv2d blocks
	4) testing-monai-2 : contains the imported monai unet model
	5) testing-pytorch -best model val : This is a model created using pytorch modules and gave the best dice score and this
	best performing model is applied on the testing dataset for visualization
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miscellaneous	This folder contains the ipymb file(Preprocessing.ipymb) used to preprocess the data and tar_extractor.ipymb used to
	extract the data.
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