update: added new yolo model for UAV detections

demo.html

@@ -254,6 +254,7 @@ button:disabled {
         <option value="owlv2" selected>OWL-V2</option>
         <option value="hf_yolov8">YOLOv8</option>
         <option value="hf_yolov8_defence">YOLOv8m Defence</option>
+        <option value="hf_yolov12_bot_sort">YOLOv12 BoT-SORT + ReID</option>
     </select>
 
     <button type="button" id="executeButton" onclick="executeMission()" disabled>EXECUTE MISSION</button>

models/detectors/yolov12_bot_sort.py

@@ -0,0 +1,56 @@
+import logging
+from typing import Sequence
+
+import numpy as np
+import torch
+from huggingface_hub import hf_hub_download
+from ultralytics import YOLO
+
+from models.detectors.base import DetectionResult, ObjectDetector
+
+
+class HuggingFaceYoloV12BotSortDetector(ObjectDetector):
+    """YOLOv12 model (BoT-SORT + ReID) hosted on Hugging Face."""
+
+    REPO_ID = "wish44165/YOLOv12-BoT-SORT-ReID"
+    WEIGHT_FILE = "MOT_yolov12n.pt"
+
+    def __init__(self, score_threshold: float = 0.3) -> None:
+        self.name = "hf_yolov12_bot_sort"
+        self.score_threshold = score_threshold
+        self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
+        logging.info(
+            "Loading Hugging Face YOLOv12 BoT-SORT weights %s/%s onto %s",
+            self.REPO_ID,
+            self.WEIGHT_FILE,
+            self.device,
+        )
+        weight_path = hf_hub_download(repo_id=self.REPO_ID, filename=self.WEIGHT_FILE)
+        self.model = YOLO(weight_path)
+        self.model.to(self.device)
+        self.class_names = self.model.names
+
+    def predict(self, frame: np.ndarray, queries: Sequence[str]) -> DetectionResult:
+        device_arg = 0 if self.device.startswith("cuda") else "cpu"
+        results = self.model.predict(
+            source=frame,
+            device=device_arg,
+            conf=self.score_threshold,
+            verbose=False,
+        )
+        result = results[0]
+        boxes = result.boxes
+        if boxes is None or boxes.xyxy is None:
+            empty = np.empty((0, 4), dtype=np.float32)
+            return DetectionResult(empty, [], [], [])
+
+        xyxy = boxes.xyxy.cpu().numpy()
+        scores = boxes.conf.cpu().numpy().tolist()
+        label_ids = boxes.cls.cpu().numpy().astype(int).tolist()
+        label_names = [self.class_names.get(idx, f"class_{idx}") for idx in label_ids]
+        return DetectionResult(
+            boxes=xyxy,
+            scores=scores,
+            labels=label_ids,
+            label_names=label_names,
+        )

models/model_loader.py

@@ -6,6 +6,7 @@ from models.detectors.base import ObjectDetector
 from models.detectors.owlv2 import Owlv2Detector
 from models.detectors.yolov8 import HuggingFaceYoloV8Detector
 from models.detectors.yolov8_defence import HuggingFaceYoloV8DefenceDetector
+from models.detectors.yolov12_bot_sort import HuggingFaceYoloV12BotSortDetector
 
 DEFAULT_DETECTOR = "owlv2"
 
@@ -13,6 +14,7 @@ _REGISTRY: Dict[str, Callable[[], ObjectDetector]] = {
     "owlv2": Owlv2Detector,
     "hf_yolov8": HuggingFaceYoloV8Detector,
     "hf_yolov8_defence": HuggingFaceYoloV8DefenceDetector,
+    "hf_yolov12_bot_sort": HuggingFaceYoloV12BotSortDetector,
 }