Update README.md

Author: NareshGuru77

README.md

@@ -1,19 +1,19 @@
 # Curbing Task Interference using Representation Similarity-Guided Multi-Task Feature Sharing
 
-OUTPUT_DIR: Directory to save output contents.
-DATA_DIR: Directory containing the datasets.
-MODEL_DIR: Directory containing the trained models.
+OUTPUT_DIR: Directory to save output contents. <br />
+DATA_DIR: Directory containing the datasets. <br />
+MODEL_DIR: Directory containing the trained models. <br />
 
 ## Environment:
 
 conda_env_local.yml file can be used to create an anaconda environment to run the code.
 
 ## Training script:
 
-To train the One-De model on cityscapes dataset:
+To train the One-De model on cityscapes dataset: <br />
 
 python train.py --batch-size 8 --workers 8 --data-folder /DATA_DIR/Cityscapes --crop-size 512 1024 --checkname train_cs --config-file ./model_cfgs/cityscapes/one_de.yaml --epochs 140 --lr .0001 --output-dir OUTPUT_DIR --lr-strategy stepwise --lr-decay 98 126 --base-optimizer RAdam --dataset cityscapes
-
+<br />
 Other model configs can be found in 'model_cfgs' directory.
 
 
@@ -22,17 +22,17 @@ Other model configs can be found in 'model_cfgs' directory.
 Models can be evaluated using --eval-only arg along with train script.
 
 
-## Get CKA similarities and task groupings:
-The following code runs grouping using seperate decoder (Sep-De).
+## Get CKA similarities and task groupings:  
+The following code runs grouping using seperate decoder (Sep-De). <br />
 python explain.py --batch-size 4 --workers 0 --crop-size 480 640 --config-file ./model_cfgs/cityscapes/sep_de_group.yaml --resume MODEL_DIR/model_latest_140.pth --data-folder /DATA_DIR/NYUv2 --data-folder-1 /DATA_DIR/NYUv2/image/train --explainer-name CKA --compare-tasks --dataset cityscapes
 
 ## Cite Our Work
 
-If you find the code concerning Progressive Decoder Fusion (PDF) useful in your research, please consider citing our paper:
+If you find the code concerning Progressive Decoder Fusion (PDF) useful in your research, please consider citing our paper: <br />
 
 Pending.
 
-If you find the code for UniNet useful in your research, please consider citing our paper:
+If you find the code for UniNet useful in your research, please consider citing our paper: <br />
 
 @InProceedings{Gurulingan_2021_ICCV, <br />
     author    = {Gurulingan, Naresh Kumar and Arani, Elahe and Zonooz, Bahram}, <br />