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Note: V5 in the filename (yolo_v5_best.pt) refers to the 5th training iteration of this project, NOT the YOLOv5 architecture. The actual architecture is YOLOv8m-seg from Ultralytics.
| Property | Value |
|---|---|
| Architecture | YOLOv8m-seg (Medium) |
| Task | Instance Segmentation |
| Developer | Sam Vision |
| Training Version | V5 (5th iteration) |
| Input Size | 640x640 |
| Confidence Threshold | 0.35 (recommended) |
| Number of Classes | 15 |
| Framework | PyTorch / Ultralytics |
The class names listed in this model card are NOT the original training labels. They have been renamed to neutral/medical terminology for platform compliance purposes. The actual model internally uses different class names related to explicit human anatomy detection. If you load the model weights directly (.ptor.onnx), the original class names will be visible viamodel.names.
| Index | Published Name |
|---|---|
| 0 | body_f |
| 1 | pelvis_m |
| 2 | face_f |
| 3 | pelvis_f |
| 4 | gluteus_f |
| 5 | body_m |
| 6 | chest_f |
| 7 | lower_pelvis_f |
| 8 | covered_chest_f |
| 9 | covered_pelvis_f |
| 10 | face_m |
| 11 | covered_gluteus_f |
| 12 | chest_m |
| 13 | gluteus_m |
| 14 | lower_pelvis_m |
1from ultralytics import YOLO
2model = YOLO('yolo_v5_best.pt')
3print(model.names) # Returns the original class names used during training1from ultralytics import YOLO
2from huggingface_hub import hf_hub_download
3
4model_path = hf_hub_download(
5 repo_id='yujigiovani/medical-skin-anatomy-segmentatio',
6 filename='yolo_v5_best.pt'
7)
8model = YOLO(model_path)
9results = model('your_image.jpg', conf=0.35)
10results[0].show()yolo_v5_best.pt - PyTorch weights (recommended for training/fine-tuning)yolo_v5_best.onnx - ONNX export (recommended for inference/deployment, e.g. AnyLabeling)