README.md

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

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.