diff --git a/openpmcvl/granular/dataset/__init__.py b/openpmcvl/granular/dataset/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/openpmcvl/granular/dataset/dataset.py b/openpmcvl/granular/dataset/dataset.py
new file mode 100644
index 0000000..cd461af
--- /dev/null
+++ b/openpmcvl/granular/dataset/dataset.py
@@ -0,0 +1,142 @@
+import json
+from tqdm import tqdm
+from PIL import Image
+
+import torch
+import torch.nn.functional as F
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+
+class SubfigureDataset(Dataset):
+    def __init__(self, data_list, transform=None):
+        """
+        PyTorch Dataset class to load images from subfig_path and apply transformations.
+
+        Args:
+            data_list (List[Dict]): List of dictionaries with dataset information.
+            transform (callable, optional): Optional transform to be applied on an image.
+        """
+        self.data_list = data_list
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.data_list)
+
+    def __getitem__(self, idx):
+        item = self.data_list[idx]
+        subfig_path = item["subfig_path"]
+        image = Image.open(subfig_path).convert("RGB")
+        if self.transform:
+            image = self.transform(image)
+        return image, idx
+
+
+class Fig_Separation_Dataset(Dataset):
+    def __init__(
+        self,
+        filepath,
+        only_medical=True,
+        normalization=False,
+        start=0,
+        end=-1,
+        input_size=512,
+    ):
+        self.images = []  # list of {'path':'xxx/xxx.png', 'w':256, 'h':256}
+        if normalization:
+            self.image_transform = transforms.Compose(
+                [
+                    transforms.ToTensor(),
+                    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+                ]
+            )
+        else:
+            self.image_transform = transforms.Compose([transforms.ToTensor()])
+
+        # preprocessing
+        lines = open(filepath).readlines()
+        dataset = [json.loads(line) for line in lines]
+
+        if only_medical:
+            dataset = [data for data in dataset if data["is_medical"]]
+
+        dataset = dataset[start : len(dataset)]
+        filtered_compound_fig_num = 0
+        print(f"Total {len(dataset)} Compound Figures.")
+        count = start
+
+        for datum in tqdm(dataset):
+            image_info = {}
+            image_info["path"] = datum["image_path"]
+            image_info["id"] = datum["id"]
+            image_info["index"] = count
+            image_info["w"] = datum["width"]
+            image_info["h"] = datum["height"]
+            count += 1
+
+            self.images.append(image_info)
+            filtered_compound_fig_num += 1
+
+        self.input_size = input_size
+
+    def __len__(self):
+        return len(self.images)
+
+    def __getitem__(self, index):
+        unpadded_image = Image.open(self.images[index]["path"]).convert("RGB")
+        unpadded_image = self.image_transform(unpadded_image)
+
+        return (
+            unpadded_image,
+            self.images[index]["h"],
+            self.images[index]["w"],
+            self.images[index]["id"],
+            self.images[index]["index"],
+            self.input_size,
+        )
+
+
+def fig_separation_collate(data):
+    """
+    Args:
+        data: refer to __getitem__() in FigCap_Dataset
+
+    Returns
+    -------
+        images: tensor (bs, 3, max_h, max_w)
+        # subfigs: list of lists  [ ... [box(tensor, (subfig_num, 4)), alignment(tensor, (subfig_num, max_l))], ... ]
+        other info: ......
+    """
+    pad_imgs = []
+    unpadded_hws = []
+    image_ids = []
+    image_index = []
+    unpadded_images = []
+
+    for sample in data:
+        unpadded_image, unpadded_h, unpadded_w, sample_id, index, input_size = sample
+        image_ids.append(sample_id)
+        image_index.append(index)
+        unpadded_hws.append([unpadded_h, unpadded_w])
+
+        _, h, w = unpadded_image.shape
+        scale = min(input_size / h, input_size / w)
+        resize_transform = transforms.Resize([round(scale * h), round(scale * w)])
+        resized_img = resize_transform(unpadded_image)  # resize within input_size
+        pad = (0, input_size - round(scale * w), 0, input_size - round(scale * h))
+        padded_img = F.pad(
+            resized_img, pad, "constant", 0
+        )  # pad image to input_size x input_size
+        pad_imgs.append(padded_img)
+
+        unpadded_images.append(unpadded_image)  # [bs * (3, h, w)]
+
+    pad_imgs = torch.stack(pad_imgs, dim=0)  # (bs, 3, max_w, max_h)
+
+    return {
+        "image": pad_imgs,
+        "unpadded_hws": unpadded_hws,
+        "image_id": image_ids,
+        "image_index": image_index,
+        "original_image": unpadded_images,
+    }