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logichenry/
ZoneVision

pytorch

object-detection

instance-segmentation

medical-imaging

microbiology

antibiotic-susceptibility-testing

mit

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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

Plate geometry — YOLO26n + Hough Circles detect the 96-well grid; estimate px/mm from 9.0 mm well pitch
Zone segmentation — RF-DETR-Seg-Small produces per-zone masks
Mask refinement (optional) — SAM3 refines boundaries
Measurement — Pixel-to-mm conversion, diameter/area extraction, QC flags
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 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.