Point to renamed Kicky AI model/dataset repos

README.md

@@ -57,8 +57,8 @@ This Space runs the distilled detector live on **ZeroGPU**; everything else is C
 > Honest note: goal detection on monocular 2D tops out in the low-to-mid 80s % — a ball
 > *in front of* the goal and a ball *in the net* are the same pixels (depth ambiguity).
 
-Model → [`build-small-hackathon/futheros-rfdetr-seg`](https://huggingface.co/build-small-hackathon/futheros-rfdetr-seg) ·
-Dataset → [`build-small-hackathon/futheros-spf`](https://huggingface.co/datasets/build-small-hackathon/futheros-spf)
+Model → [`build-small-hackathon/kicky-ai-rfdetr-seg`](https://huggingface.co/build-small-hackathon/kicky-ai-rfdetr-seg) ·
+Dataset → [`build-small-hackathon/kicky-ai-spf`](https://huggingface.co/datasets/build-small-hackathon/kicky-ai-spf)
 
 Built for the **Build Small Hackathon** by a Sunday-league player who wanted to know if his
 strikes have World Cup form.

server.py

@@ -21,7 +21,7 @@ from futheros import pipeline as P
 from futheros import coach as C
 from futheros.render import render, pose_card_img
 
-HF_MODEL = "build-small-hackathon/futheros-rfdetr-seg"
+HF_MODEL = "build-small-hackathon/kicky-ai-rfdetr-seg"
 RENDER_DIR = "/tmp/app_renders"; os.makedirs(RENDER_DIR, exist_ok=True)
 HERE = os.path.dirname(os.path.abspath(__file__))
 GT = json.load(open(os.path.join(HERE, "labels/goal_labels.json")))