diff --git a/README.md b/README.md
index 1a1bf7c..40d49f8 100644
--- a/README.md
+++ b/README.md
@@ -37,6 +37,18 @@ python generate_IN100.py \
 
 Note: Replace `train` with `val` to generate ImageNet-100 val data as well
 
+Corruption Benchmarks:
+
+```
+python generate_IN100.py \
+  --source_folder /path/to/ImageNet-1K-c-(bar) data
+  --target_folder /path/to/ImageNet-100-c-(bar) data
+```
+
+This lets you create the same class IN-100-c and IN-100-c-bar dataset. Those are the corrupted version of the testset, with 19/10 different corruptions and 5 severity levels each, that have to be looped over. Obtain information towards the corruption benchmarks from:
+ImagenNet-c: https://github.com/hendrycks/robustness
+ImageNet-c-bar: https://github.com/facebookresearch/augmentation-corruption
+
 ## Training ResNets on ImageNet-100
 
 The implementation of training and validation code can be used in main_IN100.py, and run it for the usage.
@@ -75,4 +87,4 @@ If you use this toolbox in your work, please cite this project.
 
 ## Acknowledgements
 
-Part of this code is based on [HobbitLong/SupContrast](https://github.com/HobbitLong/SupContrast).
\ No newline at end of file
+Part of this code is based on [HobbitLong/SupContrast](https://github.com/HobbitLong/SupContrast).
diff --git a/generate_IN100-c.py b/generate_IN100-c.py
new file mode 100644
index 0000000..50daf7a
--- /dev/null
+++ b/generate_IN100-c.py
@@ -0,0 +1,53 @@
+import os
+import shutil
+import argparse
+
+
+def parse_option():
+    parser = argparse.ArgumentParser('argument for generating ImageNet-100')
+
+    parser.add_argument('--source_folder', type=str,
+     default='', help='folder of ImageNet-1K dataset')
+    parser.add_argument('--target_folder', type=str,
+     default='', help='folder of ImageNet-100 dataset')
+    parser.add_argument('--target_class', type=str,
+     default='IN100.txt', help='class file of ImageNet-100')
+
+    opt = parser.parse_args()
+
+    return opt
+
+f = []
+def generate_data(source_folder, target_folder, target_class):
+
+    txt_data = open(target_class, "r") 
+    for ids, txt in enumerate(txt_data):
+        s = str(txt.split('\n')[0])
+        f.append(s)
+
+    # Loop over corruption type folders
+    for corruption in os.listdir(source_folder):
+        corruption_path = os.path.join(source_folder, corruption)
+        if os.path.isdir(corruption_path):
+            # Loop over severity levels 1-5
+            for severity in os.listdir(corruption_path):
+                severity_path = os.path.join(corruption_path, severity)
+                if os.path.isdir(severity_path):
+                    # Create corresponding folders in target directory
+                    target_corruption = os.path.join(target_folder, corruption)
+                    target_severity = os.path.join(target_corruption, severity)
+                    os.makedirs(target_severity, exist_ok=True)
+                    
+                    # Loop over class folders
+                    for dirs in os.listdir(severity_path):
+                        for tg_class in f:
+                            if dirs == tg_class:
+                                print(f'{dirs} is transferred from {corruption}/{severity}')
+                                src_path = os.path.join(severity_path, dirs)
+                                dst_path = os.path.join(target_severity, dirs)
+                                shutil.copytree(src_path, dst_path)
+
+
+opt = parse_option()
+generate_data(opt.source_folder, opt.target_folder, opt.target_class)
+