[surgical frame]

Based on this cholecystectomy frame, what should the surgeon do next?
Choose one label.

- Dissection
- Vessel Clipping
- Tissue Retraction
- Coagulation
- Aspiration

Model

Ultralytics yolo11m-cls.pt fine-tuned for 5-way single-label classification (Dissection, Vessel Clipping, Tissue Retraction, Coagulation, Aspiration)
224×224 inputs, batch 32, up to 100 epochs with patience 15, seed 42, standard color/geometric augmentation
Full training code in s69_sapbench_supervised.py; training curves in loss_curve.csv

Evaluation

Full 353-frame validation split (95% bootstrap CI):

Metric	Value
Exact-match accuracy	45.6% (40.5–51.3)

See the leaderboard for comparison with vision-language models.

Usage

python

from ultralytics import YOLO

model = YOLO("best.pt")
result = model("frame.jpg")[0]
print(result.names[result.probs.top1])

License note

Derived from Ultralytics YOLO11 weights; this checkpoint is therefore distributed under AGPL-3.0.

References

Skobelev, K., Fithian, E., Baranovski, Y., et al. A Comparative Study in Surgical AI: Potential and Limitations of Data, Compute, and Scaling. arXiv:2603.27341, 2026.
Dataset: Xu, M., Huang, Z., Imans, D., et al. SAP-Bench: Benchmarking Multimodal Large Language Models in Surgical Action Planning. arXiv:2506.07196, 2025.

Limitations

Research baseline only. Not a medical device. Next-action prediction from a single frame is inherently ambiguous.

skblv/yolo11m-cls-sapbench-action

YOLO11m-cls — SAP-Bench next-action prediction

Prompt example