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skblv/yolo11m-cls-sarrarp50-gesture
yolo11m-cls-sarrarp50-gesture is a image classification model from skblv. Use it when you need a label for an image. It is set up for ultralytics. The card lists the license as agpl-3.0.
Supervised YOLO11m-cls baseline recognizing the current suturing action on robot-assisted radical prostatectomy frames from SAR-RARP50.
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From the Hugging Face model README
Supervised YOLO11m-cls baseline recognizing the current suturing action on robot-assisted radical prostatectomy frames from SAR-RARP50.
Trained as a baseline for the SDSC × Chicago Booth surgical video understanding leaderboard (Skill assessment tab, used as a skill proxy).
This closed-set example mirrors the leaderboard format, not a text-input API for this checkpoint.
[surgical frame]
What suturing action is being performed in this frame?
Choose one label.
- Other
- Picking Up The Needle
- Positioning The Needle Tip
- Pushing The Needle Through The Tissue
- Pulling The Needle Out Of The Tissue
- Tying A Knot
- Cutting The Suture
- Returning Or Dropping The Needle
yolo11m-cls.pt fine-tuned for 8-way single-label gesture classifications73_sarrarp50_supervised.py; training curves in loss_curve.csvFull 636-frame validation split (1 Hz frames from held-out operations; 95% bootstrap CI):
| Metric | Value |
|---|---|
| Exact-match accuracy | 53.1% (49.4–57.2) |
Best result on the Skill assessment leaderboard as of Aug 2026; see the leaderboard.
from ultralytics import YOLO
model = YOLO("best.pt")
result = model("frame.jpg")[0]
print(result.names[result.probs.top1])
Derived from Ultralytics YOLO11 weights; this checkpoint is therefore distributed under AGPL-3.0.
Research baseline only. Not a medical device. Single-frame gesture recognition misses temporal cues; this is a proxy task, not an OSATS/GRS skill score.