Downloads · 30 days
0
dcrey7/kicky-ai-rfdetr-seg
kicky-ai-rfdetr-seg is a object detection model from dcrey7. Use it when you need objects located in an image. It is set up for rfdetr. The card lists the license as apache-2.0.
RF-DETR-Seg-Small instance segmentation model that detects ball, player, goal in amateur football clips. Distilled from SAM3 and NVIDIA LocateAnything-3B auto labels with zero manual annotation.
Downloads · 30 days
0
Access
Public
Updated Sep 3, 2026
Repo size
134 MB
Likes
0
Public
Click a slice to open those files.
.pth134 MB · 100%
From the Hugging Face model README
RF-DETR-Seg-Small instance segmentation model that detects ball, player, goal in amateur football clips. Distilled from SAM3 and NVIDIA LocateAnything-3B auto labels with zero manual annotation.
Built for the Build Small Hackathon (Hugging Face and Gradio), June 2026. Every model in the pipeline stays under the 32B parameter cap.
| detector | goal | leg | pose capture |
|---|---|---|---|
| SAM3 + LocateAnything-3B (teacher) | 83% | 82% | 92% |
| this model (student) | 75% | 75% | 100% |
Real time, runs about 50x faster than the SAM3 and LocateAnything teacher. It beats the teacher on pose because the player masks are cleaner, and it recovers the ball on clips SAM3 missed entirely.
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 about 1.8k auto labelled frames.
The ball is 3 to 4 pixels wide and motion blurred at 30fps. Most of the pipeline work went into recovering it.
Originally published during the Build Small Hackathon at
build-small-hackathon/kicky-ai-rfdetr-seg. Mirrored here by the author, dcrey7.