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README.md

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+---
+license: mit
+tags:
+  - object-detection
+  - instance-segmentation
+  - medical-imaging
+  - microbiology
+  - antibiotic-susceptibility-testing
+library_name: pytorch
+pipeline_tag: object-detection
+---
+
+# ZoneVision — Inhibition-Zone Detection for AST
+
+Automated inhibition-zone (antibiotic halo) detection and quantitative measurement on 96-well plate photographs for antibiotic susceptibility testing (AST).
+
+## Model Weights
+
+| File | Size | Architecture | Purpose |
+|------|------|-------------|---------|
+| `rfdetr_seg_small_best.pth` | 128 MB | RF-DETR-Seg-Small (33.4M params) | End-to-end zone instance segmentation |
+| `sam3.pt` | 3.2 GB | SAM3 | Optional mask refinement within detected ROIs |
+| `yolo26n.pt` | 5.3 MB | YOLO26n | Pretrained backbone for plate geometry estimation |
+| `yolo26n-seg.pt` | 6.4 MB | YOLO26n-seg | YOLO segmentation model (alternative detector) |
+
+## Performance
+
+| Metric | Value |
+|--------|-------|
+| F1 Score | 0.952 |
+| Precision | 0.973 |
+| Recall | 0.931 |
+| Mean IoU | 0.896 |
+| Diameter MAE | 0.234 mm (3.08% relative) |
+| Pearson r (diameter) | 0.973 |
+
+Evaluated on 11 plate photos with 233 manually annotated inhibition zones.
+
+## Pipeline
+
+1. **Plate geometry** — YOLO26n + Hough Circles detect the 96-well grid; estimate px/mm from 9.0 mm well pitch
+2. **Zone segmentation** — RF-DETR-Seg-Small produces per-zone masks
+3. **Mask refinement** (optional) — SAM3 refines boundaries
+4. **Measurement** — Pixel-to-mm conversion, diameter/area extraction, QC flags
+5. **Output** — CSV with per-well phenotypes, overlay images, binary masks
+
+## Quick Start
+
+```bash
+# Install
+pip install -e .
+
+# Download weights
+hf download logichenry/ZoneVision --local-dir weights/
+
+# Run inference
+python scripts/run_pipeline.py \
+  --input path/to/plate_photos/ \
+  --output outputs/ \
+  --config configs/config.yaml \
+  --detector rfdetr
+```
+
+## Training
+
+The RF-DETR model was trained on 233 annotated inhibition zones across 11 plate photos in COCO format. See the [GitHub repo](https://github.com/SmartisanNaive/ZoneVision) for training scripts and dataset preparation tools.
+
+## Intended Use
+
+- Automated measurement of inhibition zones in antibiotic susceptibility testing
+- High-throughput screening of antimicrobial peptide libraries on 96-well plates
+- Quantitative phenotyping for lanthipeptide or bacteriocin activity assays
+
+## Limitations
+
+- Designed for color photographs of 96-well plates; may not generalize to other formats
+- SAM3 refinement requires ~3.2 GB VRAM; can be disabled for resource-constrained environments
+- Best performance on plates with clear zone boundaries; heavily overlapping zones may reduce accuracy
+
+## Citation
+
+```bibtex
+@article{zonevision2026,
+  title={Automated Inhibition-Zone Detection for Antibiotic Susceptibility Testing Using Cascade Vision},
+  author={Han Jiang},
+  journal={Chinese Journal of Biotechnology},
+  year={2026}
+}
+```
+
+## License
+
+MIT License. See [LICENSE](https://github.com/SmartisanNaive/ZoneVision/blob/main/LICENSE).