yolo11m-cls-sapbench-action

YOLO11m-cls — SAP-Bench next-action prediction

Supervised YOLO11m-cls baseline predicting the surgeon's next action on cholecystectomy frames from SAP-Bench.

Trained as a baseline for the SDSC × Chicago Booth surgical video understanding leaderboard (Recommendations tab).

Prompt example

This closed-set example mirrors the leaderboard format, not a text-input API for this checkpoint.

[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):

See the leaderboard for comparison with vision-language models.

Usage

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.