Simplify drone YOLO loading with ultralytics native HF Hub support

models/detectors/drone_yolo.py

@@ -4,7 +4,6 @@ from typing import List, 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
@@ -14,7 +13,6 @@ class DroneYoloDetector(ObjectDetector):
     """Drone detector backed by a YOLO model on the Hugging Face Hub."""
 
     REPO_ID = "rujutashashikanjoshi/yolo12-drone-detection-0205-100m"
-    DEFAULT_WEIGHT = "best.pt"
     supports_batch = True
     max_batch_size = 8
 
@@ -25,15 +23,13 @@ class DroneYoloDetector(ObjectDetector):
             self.device = device
         else:
             self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
-        weight_file = os.getenv("DRONE_YOLO_WEIGHT", self.DEFAULT_WEIGHT)
         logging.info(
-            "Loading drone YOLO weights %s/%s onto %s",
+            "Loading drone YOLO from HuggingFace Hub: %s onto %s",
             self.REPO_ID,
-            weight_file,
             self.device,
         )
-        weight_path = hf_hub_download(repo_id=self.REPO_ID, filename=weight_file)
-        self.model = YOLO(weight_path)
+        # Load directly from HuggingFace Hub using ultralytics native support
+        self.model = YOLO(f"hf://{self.REPO_ID}")
         self.model.to(self.device)
         self.class_names = self.model.names