Update quick start

README.md

@@ -13,7 +13,7 @@ task_categories:
 
 This dataset contains traffic anomalies from **static overhead cameras** (intersections/roundabouts). It includes **32 anomaly categories** with a **relevance mapping** for severity-aware evaluation. For each camera, we provide video frames with YOLOv8 detections, including 24 h of training footage and 48 h of test footage with **1027 anomaly annotations**.
 
-*Created as part of the Master’s thesis by Hanna Lichtenberg (2025), in cooperation with Starwit Technologies GmbH. See the thesis for details and baseline NDCG@50 results.*
+*Created as part of the Master’s thesis by Hanna Lichtenberg (2025), in cooperation with Starwit Technologies GmbH. See the thesis for details and baseline NDCG@50 results. Feedback is welcome!*
 
 ## What’s inside
 
@@ -82,6 +82,30 @@ This dataset contains traffic anomalies from **static overhead cameras** (inters
 
 ## Quick start
 
+Some download possibilities. 
+
+```python
+# Full dataset (all scenes + train/test + mappings)
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    "HannaLicht/overhead-traffic-anomalies",
+    repo_type="dataset",
+    local_dir="OTA"
+)
+
+# Only test data (with anomaly labels)
+snapshot_download(
+    "HannaLicht/overhead-traffic-anomalies",
+    repo_type="dataset",
+    allow_patterns=[
+        "MononElmStreetNB/testdata/**", "RangelineS116thSt/testdata/**",
+        "RangelineSMedicalDr/testdata/**", "event_labels.txt", "relevance_mapping.json",
+    ],
+    local_dir="OTA-test-only"
+)
+```
+
 You can align frames, detections, and labels using `index.csv` timestamps and `object_id`s.
 
 ```python