Update README.md

Author: NareshGuru77

README.md

@@ -10,9 +10,9 @@ conda_env_local.yml file can be used to create an anaconda environment to run th
 
 ## Training script:
 
-To train the One-De model on cityscapes dataset: <br />
+To train the One-De model on cityscapes dataset:
 
-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
+    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.
 
@@ -24,24 +24,25 @@ 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). <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
+
+    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: 
 
-  @InProceedings{pmlr-v199-gurulingan22a,
-    title = 	 {Curbing Task Interference using Representation Similarity-Guided Multi-Task Feature Sharing},
-    author =       {Gurulingan, Naresh Kumar and Arani, Elahe and Zonooz, Bahram},
-    booktitle = 	 {Proceedings of The 1st Conference on Lifelong Learning Agents},
-    pages = 	 {937--951},
-    year = 	 {2022},
-    editor = 	 {Chandar, Sarath and Pascanu, Razvan and Precup, Doina},
-    volume = 	 {199},
-    series = 	 {Proceedings of Machine Learning Research},
-    month = 	 {22--24 Aug},
-    publisher =    {PMLR},
-    pdf = 	 {https://proceedings.mlr.press/v199/gurulingan22a/gurulingan22a.pdf},
-    url = 	 {https://proceedings.mlr.press/v199/gurulingan22a.html},
-  }
+    @InProceedings{pmlr-v199-gurulingan22a,
+      title = 	 {Curbing Task Interference using Representation Similarity-Guided Multi-Task Feature Sharing},
+      author =       {Gurulingan, Naresh Kumar and Arani, Elahe and Zonooz, Bahram},
+      booktitle = 	 {Proceedings of The 1st Conference on Lifelong Learning Agents},
+      pages = 	 {937--951},
+      year = 	 {2022},
+      editor = 	 {Chandar, Sarath and Pascanu, Razvan and Precup, Doina},
+      volume = 	 {199},
+      series = 	 {Proceedings of Machine Learning Research},
+      month = 	 {22--24 Aug},
+      publisher =    {PMLR},
+      pdf = 	 {https://proceedings.mlr.press/v199/gurulingan22a/gurulingan22a.pdf},
+      url = 	 {https://proceedings.mlr.press/v199/gurulingan22a.html},
+    }