Medical Skin Anatomy Segmentatio
If you find this model useful, please consider supporting me on Patreon to help cover cloud GPU computing costs for training future versions (like the upcoming V6)!
Source du modèle
- Type
- Other
- Taille du fichier
- 52,3–104 MB
- Version
- vRevision 28ffb80
- Mis à jour
- 16 sept. 2026
- Téléchargements
- 8
- Source
- Hugging Face
Description de la source
Medical Skin Anatomy Segmentation - Training Version 5
Developed by: Sam Vision
💖 Support my work on Patreon
If you find this model useful, please consider supporting me on Patreon to help cover cloud GPU computing costs for training future versions (like the upcoming V6)!
A YOLOv8m-seg (instance segmentation) model trained to identify 15 anatomical body regions and coverage states.
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.
Architecture
| 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 |
Warning on Class Names
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.
Classes (15 total)
| 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 |
To retrieve the actual class names, load the model directly:
from ultralytics import YOLO
model = YOLO('yolo_v5_best.pt')
print(model.names) # Returns the original class names used during training
Usage
from ultralytics import YOLO
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(
repo_id='yujigiovani/medical-skin-anatomy-segmentatio',
filename='yolo_v5_best.pt'
)
model = YOLO(model_path)
results = model('your_image.jpg', conf=0.35)
results[0].show()
Files
yolo_v5_best.pt- PyTorch weights (recommended for training/fine-tuning)yolo_v5_best.onnx- ONNX export (recommended for inference/deployment, e.g. AnyLabeling)
Sources
1 source
Sources
1 sourceVérifié 1 sept.
Download files
Artefacts du modèle
2 artefacts
Artefacts du modèle
2 artefactsExtraits de sources
2 extraits
Extraits de sources
2 extraits--- license: cc-by-4.0 author: Sam Vision tags: - object-detection - instance-segmentation - computer-vision - ultralytics - yolov8 - anatomy - medical language: - en --- # Medical Skin Anatomy Segmentation - Training Version 5 Developed by: **Sam Vision** ## 💖 Support my work on Patreon If you find this model useful, please consider supporting me on [Patreon](https://www.patreon.com/Sam_Vision) to help cover cloud GPU computing costs for training future versions (like the upcoming V6)! A **YOLOv8m-seg** (instance segmentation) model trained to identify 15 anatomical body regions and coverage states. > 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. ## Architecture | 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 | ## Warning on Class Names > **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 (`.pt` or `.onnx`), the original class names will be visible via `model.names`. ## Classes (15 total) | 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 | To retrieve the actual class names, load the model directly: ```python from ultralytics import YOLO model = YOLO('yolo_v5_best.pt') print(model.names) # Returns the original class names used during training ``` ## Usage ```python from ultralytics import YOLO from huggingface_hub import hf_hub_download model_path = hf_hub_download( repo_id='yujigiovani/medical-skin-anatomy-segmentatio', filename='yolo_v5_best.pt'...
Source context: 8 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo Sam-Vision/medical-skin-anatomy-segmentatio
