Update README.md

README.md

@@ -17,6 +17,32 @@ The dataset provides dense annotations for:
 Validated through benchmarking on state-of-the-art models across tracking, trajectory prediction, and intention prediction tasks, with corresponding ground truth annotations for each benchmark.
 
 
+## Quick start
+``` bash
+from datasets import load_dataset
+# Load the dataset again with the 'force_redownload' option
+dataset = load_dataset("KuAvLab/EMT", split="train")#download_mode="reuse_cache_if_exists")# download_mode="force_redownload") or split train 
+```
+### Avaiable labels:
+- Data from datatset has two outputs: image and object:
+- Image contains the frame image while object contains annotation:
+``` # object labels
+bbox: bbox of detected objects
+track_id: tracking id of detected object
+class_id: class id of object
+class_name: type of object
+```
+
+Sample use case:
+```python
+for data in dataset:
+    # Convert image from PIL to OpenCV format (BGR)
+    img = np.array(data['image'])
+    print("Classes:", data['objects']['class_name'])
+    print("Bboxes:", len(data['objects']['bbox']),"\nTrack IDs:", data['objects']['track_id']),"\nclass IDs:", data['objects']['class_id']) )
+```
+
+
 ### Data Collection
 | Aspect | Description |
 |:-------|:------------|