Create README.md

README.md

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+# NSFW Segmentation
+
+Multi-head release of three single-task segmentation models targeting NSFW anatomy. Each checkpoint runs independently and produces binary masks for the specified classes.
+
+| File | Task | Classes | Mask mAP@0.5 | Mask mAP@0.5:0.95 |
+| --- | --- | --- | --- | --- |
+| `nsfw-seg-breast.pt` | Breast anatomy | breast, areola, nipple | 0.895 | 0.636 |
+| `nsfw-seg-vagina.pt` | Vagina | vagina | 0.995 | 0.871 |
+| `nsfw-seg-penis.pt` | Penis | penis | 0.995 | 0.975 |
+
+## Description
+- Backbone: YOLO11s segmentation head (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.pt")  # swap for vagina or penis
+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.