diff --git a/expo/README.md b/expo/README.md
index 011322897..3f9e630e5 100644
--- a/expo/README.md
+++ b/expo/README.md
@@ -215,6 +215,8 @@ #### Setup
 pip install -U pip
 pip install -U setuptools wheel
 pip install autogluon
+
+python run_expriment.py --exp_mode autogluon --task fashion_mnist
 ```
 
 提供github链接，并说明使用的命令以及参数设置
diff --git a/expo/experimenter/autogluon.py b/expo/experimenter/autogluon.py
index 93dfdb4bc..4bcba432c 100644
--- a/expo/experimenter/autogluon.py
+++ b/expo/experimenter/autogluon.py
@@ -32,6 +32,77 @@ class AGRunner:
         test_preds = predictor.predict(test_data)
         return {"test_preds": test_preds, "dev_preds": dev_preds}
 
+    def run_images(self):
+        from autogluon.multimodal import MultiModalPredictor
+        target_col = self.state["dataset_config"]["target_col"]
+        train_path = self.datasets["train"]
+        dev_path = self.datasets["dev"]
+        dev_wo_target_path = self.datasets["dev_wo_target"]  # Updated variable name
+        test_wo_target_path = self.datasets["test_wo_target"]
+        eval_metric = self.state["dataset_config"]["metric"].replace(" ", "_")
+
+        # Load the datasets
+        train_data, dev_data, dev_wo_target_data, test_data = self.load_split_dataset(
+            train_path, dev_path, dev_wo_target_path, test_wo_target_path
+        )
+
+        # Create and fit the predictor
+        predictor = MultiModalPredictor(
+            label=target_col,
+            eval_metric=eval_metric,
+            path="AutogluonModels/ag-{}-{}".format(self.state["task"], datetime.now().strftime("%y%m%d_%H%M")),
+        ).fit(train_data=train_data, tuning_data=dev_data, time_limit=self.time_limit)
+
+        # Make predictions on dev and test datasets
+        dev_preds = predictor.predict(dev_wo_target_data)
+        test_preds = predictor.predict(test_data)
+
+        # Return predictions for dev and test datasets
+        return {
+            "dev_preds": dev_preds,
+            "test_preds": test_preds
+        }
+
+    def load_split_dataset(self, train_path, dev_path, dev_wo_target_path, test_wo_target_path):
+        import os
+        import pandas as pd
+        """
+        Loads training, dev, and test datasets from given file paths
+
+        Args:
+            train_path (str): Path to the training dataset.
+            dev_path (str): Path to the dev dataset with target labels.
+            dev_wo_target_path (str): Path to the dev dataset without target labels.
+            test_wo_target_path (str): Path to the test dataset without target labels.
+
+        Returns:
+            train_data (pd.DataFrame): Loaded training dataset with updated image paths.
+            dev_data (pd.DataFrame): Loaded dev dataset with updated image paths.
+            dev_wo_target_data (pd.DataFrame): Loaded dev dataset without target labels and updated image paths.
+            test_data (pd.DataFrame): Loaded test dataset with updated image paths.
+        """
+
+        # Define the root path to append
+        root_folder = os.path.join("F:/Download/Dataset/", self.state["task"])
+
+        # Load the datasets
+        train_data = pd.read_csv(train_path)
+        dev_data = pd.read_csv(dev_path)  # Load dev dataset with target labels
+        dev_wo_target_data = pd.read_csv(dev_wo_target_path)  # Load dev dataset without target labels
+        test_data = pd.read_csv(test_wo_target_path)
+
+
+        # Get the name of the first column (assuming it's the image path column)
+
+        image_column = train_data.columns[0]
+        # Append root folder path to the image column in each dataset
+        train_data[image_column] = train_data[image_column].apply(lambda x: os.path.join(root_folder, x))
+        dev_data[image_column] = dev_data[image_column].apply(lambda x: os.path.join(root_folder, x))
+        dev_wo_target_data[image_column] = dev_wo_target_data[image_column].apply(
+            lambda x: os.path.join(root_folder, x))
+        test_data[image_column] = test_data[image_column].apply(lambda x: os.path.join(root_folder, x))
+        return train_data, dev_data, dev_wo_target_data, test_data
+
 
 class GluonExperimenter(CustomExperimenter):
     result_path: str = "results/autogluon"
@@ -41,7 +112,8 @@ class GluonExperimenter(CustomExperimenter):
         self.framework = AGRunner(self.state)
 
     async def run_experiment(self):
-        result = self.framework.run()
+        # result = self.framework.run()
+        result = self.framework.run_images()
         user_requirement = self.state["requirement"]
         dev_preds = result["dev_preds"]
         test_preds = result["test_preds"]