diff --git a/examples/3D Object Dataset/upload.py b/examples/3D Object Dataset/upload.py
new file mode 100644
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+++ b/examples/3D Object Dataset/upload.py	
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+"""
+Dataset Download Source: http://cvgl.stanford.edu/data2/3Ddataset.zip
+Dataset format: Images(.bmp file)
+Dataset Features: bicycle, car, cellphone, head, iron, monitor, mouse, shoe, stapler, toaster
+
+Folder Structure:
+3Ddataset
+  -bicycle
+    -bicycle_1
+      - Various Images in .bmp format
+    -bicycle_2
+    -bicycle_3
+    ...
+    -bicycle_10
+  -car
+    -car_1
+    -car_2
+    ...
+    -car_10
+  ...
+  Total 10 features
+"""
+
+import os
+import numpy as np
+from PIL import Image
+import torchvision.transforms as transforms
+from hub import Transform, dataset
+import pandas as pd
+
+NUM_FEATURES = 10
+
+
+class DatasetGenerator(Transform):
+    def meta(self):
+        # here we specify the attributes of return type
+        return {
+            "image_label": {"shape": (1,), "dtype": "int", "dtag": "text"},
+            "named_image_label": {"shape": (1,), "dtype": "U25", "dtag": "text"},
+            "image": {
+                "shape": (1,),
+                "dtype": "uint32",
+                "chunksize": 100,
+                "dtag": "image",
+            },
+        }
+
+    def forward(self, image_info):
+        # we need to return a dictionary of numpy arrays from here
+        ds = {}
+        ds["image_label"] = np.empty(1, dtype="int")
+        ds["image_label"][0] = image_info["image_label"]
+
+        ds["named_image_label"] = np.empty(1, dtype="object")
+        ds["named_image_label"][0] = image_info["named_image_label"]
+
+        ds["image"] = np.empty(1, object)
+        ds["image"][0] = np.array(Image.open(image_info["image_path"]).convert("RGB"))
+        print("------------------------------------------------")
+        print(ds["named_image_label"][0] + " image loaded successfully")
+        return ds
+
+
+def map_labels(labels_list):
+    dic = {labels_list[i]: i for i in range(1, NUM_FEATURES + 1)}
+    return dic
+
+
+def load_dataset(base_path):
+    labels_list = os.listdir(base_path)
+    labels_dict = map_labels(labels_list)
+    image_info_list = []
+    for label in labels_list:
+        for label_num in range(1, NUM_FEATURES + 1):
+            curr_path = base_path + "/" + label + "/" + label + "_" + str(label_num)
+            images_list = os.listdir(curr_path)
+            for image in images_list:
+                image_info = {}
+                if image.lower().startswith(
+                    label
+                ):  # all images' name starts with the feature name (observation)
+                    image_info["image_path"] = curr_path + "/" + image
+                    image_info["image_label"] = labels_dict[label]
+                    image_info["named_image_label"] = label
+                    image_info_list.append(image_info)
+
+    # the generator iterates through the argument given, one by one and applies forward. This is done lazily.
+    ds = dataset.generate(DatasetGenerator(), image_info_list)
+    return ds
+
+
+def main():
+    base_path = "./3Ddataset"
+    # stores the dataset in username/datasetname
+    ds = load_dataset(base_path)
+    ds.store("ThreeDimensionalDataset")
+
+
+if __name__ == "__main__":
+    main()