| --- |
| 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={Baice}, |
| journal={Chinese Journal of Biotechnology}, |
| year={2026} |
| } |
| ``` |
|
|
| ## License |
|
|
| MIT License. See [LICENSE](https://github.com/SmartisanNaive/ZoneVision/blob/main/LICENSE). |
|
|
