Update README.md

README.md

@@ -8,7 +8,7 @@ tags:
   - person-re-identification
   - UAV
   - drone
-  - YOLOv8
+  - YOLO
   - aerial-imagery
 pretty_name: Person Detection and Re-Identification from Low Altitude UAV-based Platform
 size_categories:
@@ -59,7 +59,7 @@ The dataset supports two tasks:
  
 ## Detection Task
  
-The detection subset contains **5 batches** of labeled drone footage, each with approximately 100–300 images. Annotations are in YOLOv8 format, with bounding boxes around detected persons.
+The detection subset contains **5 batches** of labeled drone footage, each with approximately 100–300 images. Annotations are in YOLO format, with bounding boxes around detected persons.
  
 Batches 3 and 5 correspond to "outside cage" scenarios and were used for a separate evaluation subset.
  
@@ -71,7 +71,7 @@ Batches 3 and 5 correspond to "outside cage" scenarios and were used for a separ
  
 ## Re-Identification Task
  
-The re-identification subset contains **2 batches** of labeled images (`re-id-test1` and `re-id-test2`), annotated with individual identity labels in YOLOv8 format. This data was used to evaluate a person re-identification pipeline using the OSNet model.
+The re-identification subset contains **2 batches** of labeled images (`re-id-test1` and `re-id-test2`), annotated with individual identity labels in YOLO format. This data was used to evaluate a person re-identification pipeline using the OSNet model.
  
 Pre-computed gallery feature vectors are provided as `.npy` files (one per identity), enabling reproduction of the re-identification evaluation without re-extracting features.
  
@@ -87,7 +87,7 @@ Pre-computed gallery feature vectors are provided as `.npy` files (one per ident
 - **Platform**: DJI Mini drone
 - **Altitude**: Low altitude
 - **Annotation tool**: Roboflow
-- **Annotation format**: YOLOv8 (TXT)
+- **Annotation format**: YOLO (TXT)
 - **Pre-processing/Augmentation**: None applied to the exported dataset
 ## Usage
  
@@ -96,7 +96,7 @@ Pre-computed gallery feature vectors are provided as `.npy` files (one per ident
 ```python
 from ultralytics import YOLO
  
-model = YOLO("yolov8n.pt")
+model = YOLO("yolov8s.pt")
 results = model.val(data="Detection/Batch-1.yolov8/data.yaml")
 ```