Not-For-All-Audiences
File size: 2,276 Bytes
684afc4 8181995 684afc4 8181995 684afc4 8181995 684afc4 8181995 684afc4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | # NSFW Segmentation
Multi-head release of single-task segmentation models targeting NSFW anatomy. Each checkpoint runs independently and produces binary masks for the specified classes.
| File | Backbone | Task | Classes | Mask mAP@0.5 | Mask mAP@0.5:0.95 |
| --- | --- | --- | --- | --- | --- |
| `nsfw-seg-breast-s.pt` | YOLO11s | Breast anatomy | breast, areola, nipple | 0.895 | 0.636 |
| `nsfw-seg-breast-x.pt` | YOLO11x | Breast anatomy | breast, areola, nipple | 0.929 | 0.702 |
| `nsfw-seg-vagina-s.pt` | YOLO11s | Vagina | vagina | 0.995 | 0.871 |
| `nsfw-seg-vagina-x.pt` | YOLO11x | Vagina | vagina | 0.995 | 0.918 |
| `nsfw-seg-penis-s.pt` | YOLO11s | Penis | penis | 0.995 | 0.975 |
| `nsfw-seg-penis-x.pt` | YOLO11x | Penis | penis | 0.995 | 0.987 |
## Description
- Backbones: YOLO11s and YOLO11x segmentation heads (Ultralytics 8.3.204).
- Weights exported as `.pt` checkpoints compatible with `ultralytics>=8.3`.
- One model per label space; load the checkpoint that matches your target anatomy.
## Intended Use
- Automatic instance segmentation for NSFW anatomical structures in moderated, research, or medical-support workflows.
- **Inputs:** RGB images.
- **Outputs:** Binary masks aligned with the class taxonomy above.
## Data Summary
- Training data consisted of curated, privately-held NSFW image sets with polygon masks (YOLO segmentation format).
- Train/validation splits were normalized and merged after preprocessing; metrics reflect held-out validation imagery.
- Datasets are not included in this release.
## Metrics
- Evaluated with `yolo segment val` at 832 px resolution, confidence threshold 0.1.
- Numbers in the table refer to the best checkpoint per task.
## Limitations
- Models are not a substitute for clinical assessment.
- Domain shift (lighting, camera quality, demographics) may impact performance.
- No safety filtering is applied; downstream systems must implement access controls.
## Quickstart
```python
from ultralytics import YOLO
model = YOLO("nsfw-seg-breast-s.pt") # swap for -x or other anatomy
results = model.predict("path/to/image.jpg", imgsz=832, conf=0.1)
```
## Support
For integration questions or feedback, open an issue on the hosting repository and mention the checkpoint name in the subject line.
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