diff --git a/README.md b/README.md
index 53e5640..e38055a 100644
--- a/README.md
+++ b/README.md
@@ -33,21 +33,38 @@ MedSegDiff a Diffusion Probabilistic Model (DPM) based framework for Medical Ima
 
 ## Example Cases
 ### Melanoma Segmentation from Skin Images
-1. Download ISIC dataset from https://challenge.isic-archive.com/data/. Your dataset folder under "data_dir" should be like:
+1. Download ISIC dataset from https://challenge.isic-archive.com/data/. Your dataset folder under "data" should be like:
 
-ISIC/
+~~~
+data
+|   ----ISIC
+|       ----Test
+|       |   |   ISBI2016_ISIC_Part1_Test_GroundTruth.csv
+|       |   |   
+|       |   ----ISBI2016_ISIC_Part1_Test_Data
+|       |   |       ISIC_0000003.jpg
+|       |   |       .....
+|       |   |
+|       |   ----ISBI2016_ISIC_Part1_Test_GroundTruth
+|       |           ISIC_0000003_Segmentation.png
+|       |   |       .....
+|       |           
+|       ----Train
+|           |   ISBI2016_ISIC_Part1_Training_GroundTruth.csv
+|           |   
+|           ----ISBI2016_ISIC_Part1_Training_Data
+|           |       ISIC_0000000.jpg
+|           |       .....
+|           |       
+|           ----ISBI2016_ISIC_Part1_Training_GroundTruth
+|           |       ISIC_0000000_Segmentation.png
+|           |       .....
+~~~
 
-     ISBI2016_ISIC_Part3B_Test_Data/...
-     
-     ISBI2016_ISIC_Part3B_Training_Data/...
-     
-     ISBI2016_ISIC_Part3B_Test_GroundTruth.csv
-     
-     ISBI2016_ISIC_Part3B_Training_GroundTruth.csv
     
-2. For training, run: ``python scripts/segmentation_train.py --data_name ISIC --data_dir input data direction --out_dir output data direction --image_size 256 --num_channels 128 --class_cond False --num_res_blocks 2 --num_heads 1 --learn_sigma True --use_scale_shift_norm False --attention_resolutions 16 --diffusion_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False --lr 1e-4 --batch_size 8``
-    
-3. For sampling, run: ``python scripts/segmentation_sample.py --data_name ISIC --data_dir input data direction --out_dir output data direction --model_path saved model --image_size 256 --num_channels 128 --class_cond False --num_res_blocks 2 --num_heads 1 --learn_sigma True --use_scale_shift_norm False --attention_resolutions 16 --diffusion_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False --num_ensemble 5``
+2. For training, run: ``python scripts/segmentation_train.py --data_name ISIC --data_dir *input data direction* --out_dir *output data direction* --image_size 256 --num_channels 128 --class_cond False --num_res_blocks 2 --num_heads 1 --learn_sigma True --use_scale_shift_norm False --attention_resolutions 16 --diffusion_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False --lr 1e-4 --batch_size 8``
+
+3. For sampling, run: ``python scripts/segmentation_sample.py --data_name ISIC --data_dir *input data direction* --out_dir *output data direction* --model_path *saved model* --image_size 256 --num_channels 128 --class_cond False --num_res_blocks 2 --num_heads 1 --learn_sigma True --use_scale_shift_norm False --attention_resolutions 16 --diffusion_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False --num_ensemble 5``
 
 4. For evaluation, run ``python scripts/segmentation_env.py --inp_pth *folder you save prediction images* --out_pth *folder you save ground truth images*``