diff --git a/api/CocoDetectionDataset.py b/api/CocoDetectionDataset.py
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index 0000000..6f27871
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+++ b/api/CocoDetectionDataset.py
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+# Custom PyTorch Dataset to load COCO-format annotations and images
+import matplotlib.pyplot as plt
+import cv2
+import os
+import torch
+from PIL import Image
+import numpy as np
+from torch.utils.data import Dataset
+from pycocotools.coco import COCO
+
+# website: https://visionbrick.com/pipeline-for-training-custom-faster-rcnn-object-detection-models-with-pytorch/
+
+class CocoDetectionDataset(Dataset):
+    # Init function: loads annotation file and prepares list of image IDs
+    def __init__(self, image_dir, annotation_path, transforms=None):
+        self.image_dir = image_dir
+        self.coco = COCO(annotation_path)
+        self.image_ids = list(self.coco.imgs.keys())
+        self.transforms = transforms
+ 
+    # Returns total number of images
+    def __len__(self):
+        return len(self.image_ids)
+ 
+    # Fetches a single image and its annotations
+    def __getitem__(self, idx):
+        image_id = self.image_ids[idx]
+        image_info = self.coco.loadImgs(image_id)[0]
+        image_path = os.path.join(self.image_dir, image_info['file_name'])
+        image = Image.open(image_path).convert("RGB")
+ 
+        # Load all annotations for this image
+        annotation_ids = self.coco.getAnnIds(imgIds=image_id)
+        annotations = self.coco.loadAnns(annotation_ids)
+ 
+        # Extract bounding boxes and labels from annotations
+        boxes = []
+        labels = []
+        for obj in annotations:
+            xmin, ymin, width, height = obj['bbox']
+            xmax = xmin + width
+            ymax = ymin + height
+            boxes.append([xmin, ymin, xmax, ymax])
+            labels.append(obj['category_id'])
+ 
+        # Convert annotations to PyTorch tensors
+        boxes = torch.as_tensor(boxes, dtype=torch.float32)
+        labels = torch.as_tensor(labels, dtype=torch.int64)
+        area = torch.as_tensor([obj['area'] for obj in annotations],    dtype=torch.float32)
+        iscrowd = torch.as_tensor([obj.get('iscrowd', 0) for obj in annotations], dtype=torch.int64)
+ 
+        # Package everything into a target dictionary
+        target = {
+            "boxes": boxes,
+            "labels": labels,
+            "image_id": image_id,
+            "area": area,
+            "iscrowd": iscrowd
+        }
+ 
+        # Apply transforms if any were passed
+        if self.transforms:
+            image = self.transforms(image)
+ 
+        return image, target
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