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| Property | Value |
|---|---|
| Base model | YOLOv8 (Ultralytics) |
| Training method | Transfer learning |
| Task | Object detection |
| Input | RGB image or video frame |
| Output | Bounding boxes + class labels + confidence scores |
| ID | Label | ID | Label |
|---|---|---|---|
| 0 | bus_stop | 11 | ped_crossing |
| 1 | do_not_enter | 12 | ped_zebra_cross |
| 2 | do_not_stop | 13 | railway_crossing |
| 3 | do_not_turn_l | 14 | red_light |
| 4 | do_not_turn_r | 15 | stop |
| 5 | do_not_u_turn | 16 | t_intersection_l |
| 6 | enter_left_lane | 17 | traffic_light |
| 7 | green_light | 18 | u_turn |
| 8 | left_right_lane | 19 | warning |
| 9 | no_parking | 20 | yellow_light |
| 10 | parking |
1from ultralytics import YOLO
2import torch
3
4# Allow loading of custom YOLOv8 checkpoint
5_orig = torch.load
6def _safe_load(*args, **kwargs):
7 kwargs["weights_only"] = False
8 return _orig(*args, **kwargs)
9torch.load = _safe_load
10
11# Load model
12model = YOLO("best_roboflow.pt")
13
14# Run inference
15results = model.predict("your_image.jpg", conf=0.4)
16results[0].show()