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best.pt weights for pedestrian detection. Multi-object tracking is performed separately using ByteTrack and is available in the accompanying GitHub repository and Hugging Face Space.| Item | Value |
|---|---|
| Architecture | YOLO26s |
| Task | Pedestrian Detection |
| Dataset | MOT20 |
| Classes | Person |
| Training Resolution | 640 × 640 |
| Evaluation Resolution | 1280 × 1280 |
1imgsz = 1280
2conf = 0.25
3iou = 0.50| Metric | Value |
|---|---|
| Precision | 94.57% |
| Recall | 91.30% |
| mAP@0.5 | 92.10% |
| mAP@0.5:0.95 | 67.49% |
| Metric | Value |
|---|---|
| HOTA | 50.0% |
| DetA | 55.7% |
| AssA | 45.1% |
| LocA | 85.0% |
Note: These tracking metrics are reported for the complete YOLO26s + ByteTrack pipeline and are not produced by the detector alone.
1from ultralytics import YOLO
2
3model = YOLO("best.pt")1results = model.predict(
2 source="image.jpg",
3 imgsz=1280,
4 conf=0.25
5)1from huggingface_hub import hf_hub_download
2from ultralytics import YOLO
3
4weights = hf_hub_download(
5 repo_id="Halftom/mot20-yolo26s-pedestrian",
6 filename="best.pt"
7)
8
9model = YOLO(weights)https://github.com/Haftom19/MOT20-Pedestrian-Tracking-YOLO26s-ByteTrackhttps://huggingface.co/spaces/Halftom/mot20-pedestrian-tracking