diff --git a/expo/data/hf_data.py b/expo/data/hf_data.py
index 952ab5c73..45ff6330b 100644
--- a/expo/data/hf_data.py
+++ b/expo/data/hf_data.py
@@ -1,7 +1,9 @@
 import asyncio
 import os
 from pathlib import Path
-
+import numpy as np
+from PIL import Image
+import io
 import pandas as pd
 from datasets import load_dataset
 
@@ -9,22 +11,25 @@ from expo.data.dataset import ExpDataset, process_dataset, save_datasets_dict_to
 from expo.insights.solution_designer import SolutionDesigner
 
 HFDATSETS = [
-    {"name": "sms_spam", "dataset_name": "ucirvine/sms_spam", "target_col": "label", "modality": "text"},
-    {"name": "banking77", "dataset_name": "PolyAI/banking77", "target_col": "label", "modality": "text"},
-    {"name": "gnad10", "dataset_name": "community-datasets/gnad10", "target_col": "label", "modality": "text"},
-    # {
-    #     "name": "oxford-iiit-pet",
-    #     "dataset_name": "timm/oxford-iiit-pet",
-    #     "target_col": "label_cat_dog",
-    #     "modality": "image",
-    # },
+    # {"name": "sms_spam", "dataset_name": "ucirvine/sms_spam", "target_col": "label", "modality": "text"},
+    # {"name": "banking77", "dataset_name": "PolyAI/banking77", "target_col": "label", "modality": "text"},
+    # {"name": "gnad10", "dataset_name": "community-datasets/gnad10", "target_col": "label", "modality": "text"},
+    {
+        "name": "oxford-iiit-pet",
+        "dataset_name": "timm/oxford-iiit-pet",
+        "image_col": "image",
+        "target_col": "label_cat_dog",
+        "modality": "image",
+    },
     # {   "name": "stanford_cars",
     #     "dataset_name": "tanganke/stanford_cars",
+    #     "image_col": "image",
     #     "target_col": "label",
     #     "modality": "image"},
     # {
     #     "name": "fashion_mnist",
     #     "dataset_name": "zalando-datasets/fashion_mnist",
+    #     "image_col": "image",
     #     "target_col": "label",
     #     "modality": "image",
     # },
@@ -42,16 +47,22 @@ class HFExpDataset(ExpDataset):
         self.dataset_name = dataset_name
         self.modality = kwargs.get("modality", "")
         self.target_col = kwargs.get("target_col", "label")
+        self.image_col = kwargs.get("image_col", "image")
         self.dataset = load_dataset(self.dataset_name, trust_remote_code=True)
         super().__init__(self.name, dataset_dir, **kwargs)
 
     def get_raw_dataset(self):
         raw_dir = Path(self.dataset_dir, self.name, "raw")
         raw_dir.mkdir(parents=True, exist_ok=True)
+
         if os.path.exists(Path(raw_dir, "train.csv")):
             df = pd.read_csv(Path(raw_dir, "train.csv"), encoding="utf-8")
         else:
             df = self.dataset["train"].to_pandas()
+
+            if self.modality == "image":
+                df = self.save_images_and_update_df(df, raw_dir, "train")
+
             df.to_csv(Path(raw_dir, "train.csv"), index=False, encoding="utf-8")
 
         if os.path.exists(Path(raw_dir, "test.csv")):
@@ -59,19 +70,37 @@ class HFExpDataset(ExpDataset):
         else:
             if self.dataset and "test" in self.dataset:
                 test_df = self.dataset["test"].to_pandas()
+
+                if self.modality == "image":
+                    test_df = self.save_images_and_update_df(test_df, raw_dir, "test")
+
                 test_df.to_csv(Path(raw_dir, "test.csv"), index=False, encoding="utf-8")
             else:
                 test_df = None
+
         return df, test_df
 
+    def save_images_and_update_df(self, df, raw_dir, split):
+        image_dir = Path(raw_dir, f"{split}_images")
+        image_dir.mkdir(parents=True, exist_ok=True)
+
+        def process_image(idx, row):
+            image_bytes = row[self.image_col]["bytes"]
+            image = Image.open(io.BytesIO(image_bytes))
+            if image.mode == "RGBA":
+                image = image.convert("RGB")
+            img_path = Path(image_dir, f"{idx}.jpg")
+            image.save(img_path)
+            return str(img_path)
+
+        df["image"] = df.apply(lambda row: process_image(row.name, row), axis=1)
+        return df
+
     def get_df_head(self, raw_df):
-        if self.modality == "text":
-            examples = []
-            for i in range(5):
-                examples.append(raw_df.iloc[i].to_dict())
-            return examples
-        elif self.modality == "image":
-            return ""
+        examples = []
+        for i in range(5):
+            examples.append(raw_df.iloc[i].to_dict())
+        return examples
 
     def get_dataset_info(self):
         dataset_info = super().get_dataset_info()
@@ -82,7 +111,7 @@ class HFExpDataset(ExpDataset):
 
 if __name__ == "__main__":
     dataset_dir = "D:/work/automl/datasets"
-    save_analysis_pool = False
+    save_analysis_pool = True
     force_update = False
     datasets_dict = {"datasets": {}}
     solution_designer = SolutionDesigner()
@@ -92,8 +121,13 @@ if __name__ == "__main__":
             dataset_dir,
             dataset_meta["dataset_name"],
             target_col=dataset_meta["target_col"],
+            image_col=dataset_meta["image_col"],
             force_update=force_update,
             modality=dataset_meta["modality"],
         )
-        asyncio.run(process_dataset(hf_dataset, solution_designer, save_analysis_pool, datasets_dict))
+        asyncio.run(
+            process_dataset(
+                hf_dataset, solution_designer, save_analysis_pool, datasets_dict
+            )
+        )
     save_datasets_dict_to_yaml(datasets_dict, "hf_datasets.yaml")