--- a/README.md
+++ b/README.md
@@ -1,64 +1,64 @@
-# Pytorch implementation of 3D UNet
-
-This implementation is based on the orginial 3D UNet paper and adapted to be used for MRI or CT image segmentation task   
-> Link to the paper: [https://arxiv.org/pdf/1606.06650v1.pdf](https://arxiv.org/pdf/1606.06650v1.pdf)
-
-## Model Architecture
-
-The model architecture follows an encoder-decoder design which requires the input to be divisible by 16 due to its downsampling rate in the analysis path.
-
-![3D Unet](https://github.com/AghdamAmir/3D-UNet/blob/main/3D-UNET.png)
-
-## Dataset
-
-The Dataset class used for training the network is specially adapted to be used for the **Medical Segmentation Decathlon challenge**. 
-
-This dataset contains several segmentation tasks on various organs including **Liver Tumours, Brain Tumours, Hippocampus, Lung Tumours, Prostate, Cardiac,
-Pancreas Tumour, Colon Cancer, Hepatic Vessels and Spleen segmentation**.
-
-- Please also note that in the case which the task contain more than 2 classes (1: for foreground, 0: for background), you will need to modify the output
-of the model to reshape it to the size of the groundtruth mask in train.py file.
-
-> The link to the dataset: [http://medicaldecathlon.com/](http://medicaldecathlon.com/)
-
-- The Dataset class uses Monai package for reading MRI or CT and also applying augmentations on them in the transform.py file. You can modify the applied
-transformation in this file according to your preferences.
-
-## Configure the network
-
-All the configurations and hyperparameters are set in the config.py file.
-Please note that you need to change the path to the dataset directory in the config.py file before running the model.
-
-**Parameters:**
-
-- DATASET_PATH -> the directory path to dataset .tar files
-
-- TASK_ID -> specifies the the segmentation task ID (see the dict below for hints)
-
-- IN_CHANNELS -> number of input channels
-
-- NUM_CLASSES -> specifies the number of output channels for dispirate classes
-
-- BACKGROUND_AS_CLASS -> if True, the model treats background as a class
-
-- TRAIN_VAL_TEST_SPLIT -> delineates the ratios in which the dataset shoud be splitted. The length of the array should be 3.
-
-- TRAINING_EPOCH -> number of training epochs
-
-- VAL_BATCH_SIZE -> specifies the batch size of the training DataLoader
-
-- TEST_BATCH_SIZE -> specifies the batch size of the test DataLoader
-
-- TRAIN_CUDA -> if True, moves the model and inference onto GPU
-
-- BCE_WEIGHTS -> the class weights for the Binary Cross Entropy loss
-
-## Training
-
-After configure config.py, you can start to train by running
-
-`python train.py`
-
-We also employ tensorboard to visualize the training process.
-
-`tensorboard --logdir=runs/`
+# Pytorch implementation of 3D UNet
+
+This implementation is based on the orginial 3D UNet paper and adapted to be used for MRI or CT image segmentation task   
+> Link to the paper: [https://arxiv.org/pdf/1606.06650v1.pdf](https://arxiv.org/pdf/1606.06650v1.pdf)
+
+## Model Architecture
+
+The model architecture follows an encoder-decoder design which requires the input to be divisible by 16 due to its downsampling rate in the analysis path.
+
+![3D Unet](https://github.com/AghdamAmir/3D-UNet/blob/main/3D-UNET.png?raw=true)
+
+## Dataset
+
+The Dataset class used for training the network is specially adapted to be used for the **Medical Segmentation Decathlon challenge**. 
+
+This dataset contains several segmentation tasks on various organs including **Liver Tumours, Brain Tumours, Hippocampus, Lung Tumours, Prostate, Cardiac,
+Pancreas Tumour, Colon Cancer, Hepatic Vessels and Spleen segmentation**.
+
+- Please also note that in the case which the task contain more than 2 classes (1: for foreground, 0: for background), you will need to modify the output
+of the model to reshape it to the size of the groundtruth mask in train.py file.
+
+The link to the dataset: [http://medicaldecathlon.com/](http://medicaldecathlon.com/)
+
+- The Dataset class uses Monai package for reading MRI or CT and also applying augmentations on them in the transform.py file. You can modify the applied
+transformation in this file according to your preferences.
+
+## Configure the network
+
+All the configurations and hyperparameters are set in the config.py file.
+Please note that you need to change the path to the dataset directory in the config.py file before running the model.
+
+**Parameters:**
+
+- DATASET_PATH -> the directory path to dataset .tar files
+
+- TASK_ID -> specifies the the segmentation task ID (see the dict below for hints)
+
+- IN_CHANNELS -> number of input channels
+
+- NUM_CLASSES -> specifies the number of output channels for dispirate classes
+
+- BACKGROUND_AS_CLASS -> if True, the model treats background as a class
+
+- TRAIN_VAL_TEST_SPLIT -> delineates the ratios in which the dataset shoud be splitted. The length of the array should be 3.
+
+- TRAINING_EPOCH -> number of training epochs
+
+- VAL_BATCH_SIZE -> specifies the batch size of the training DataLoader
+
+- TEST_BATCH_SIZE -> specifies the batch size of the test DataLoader
+
+- TRAIN_CUDA -> if True, moves the model and inference onto GPU
+
+- BCE_WEIGHTS -> the class weights for the Binary Cross Entropy loss
+
+## Training
+
+After configure config.py, you can start to train by running
+
+`python train.py`
+
+We also employ tensorboard to visualize the training process.
+
+`tensorboard --logdir=runs/`