Kicky Ai Rfdetr Seg

Real-time, runs ~50x faster than the SAM3+LA teacher; beats it on pose (cleaner player masks) and recovers the ball on clips SAM3 missed entirely.

Use

from rfdetr import RFDETRSegSmall
m = RFDETRSegSmall(pretrain_weights="checkpoint_best_ema.pth")
det = m.predict(image, threshold=0.3)   # classes: ball, player, goal

Classes: 0 ball, 1 player, 2 goal. Trained at 384px on ~1.8k auto-labelled frames.

--- license: apache-2.0 tags: [object-detection, instance-segmentation, rf-detr, football, soccer] library_name: rfdetr --- # FUT-HEROS — RF-DETR-Seg-Small (football: ball / player / goal) RF-DETR-Seg-Small instance-segmentation model that detects **ball, player, goal** in amateur football clips. **Distilled from SAM3 + NVIDIA LocateAnything-3B auto-labels with zero manual annotation** — see [FUT-HEROS](https://github.com/) for the full pipeline. ## Results (held-out 12-clip test set, via the FUT-HEROS pipeline) | detector | goal | leg | pose-capture | |---|---|---|---| | SAM3+LA (teacher) | 83% | 82% | 92% | | **this model (student)** | 75% | 75% | **100%** | Real-time, runs ~50x faster than the SAM3+LA teacher; beats it on pose (cleaner player masks) and recovers the ball on clips SAM3 missed entirely. ## Use ```python from rfdetr import RFDETRSegSmall m = RFDETRSegSmall(pretrain_weights="checkpoint_best_ema.pth") det = m.predict(image, threshold=0.3) # classes: ball, player, goal ``` Classes: `0 ball, 1 player, 2 goal`. Trained at 384px on ~1.8k auto-labelled frames.