The first generation of the Live Game Defender (LGD) casino-chip detector: a YOLO11s object detector that finds gaming chips on the felt and classifies each by colour. Trained on point-labeled blackjack-table footage. Colours only — colour→denomination is per-casino configuration downstream.

Superseded by lgd-chips-gen2 (the current served model — a dual-felt blend covering both blackjack and Ultimate Texas Hold'em, with a different venue-matched palette). Gen 1 is published for provenance.

Generations

Gen	Repo	Palette	Frozen-gate verdict	Status
1	lgd-chips-gen1 (this)	black / white / green / red / pink	blackjack gate: recall 0.91 / precision 0.77	superseded
2	lgd-chips-gen2	black / green / pink / purple / yellow	UTH gate: 0.80 / 0.85 / colour 0.97	✅ current

Card detectors: lgd-cards-gen1 · lgd-cards-gen2 · lgd-cards-gen3.

Classes (5, in `model.classes.json` order)

chip_black chip_white chip_green chip_red chip_pink

Files

model.onnx — ONNX export (run with onnxruntime).
model.classes.json — ordered class-name sidecar (index → colour).
metrics.json — training config + evaluation.

Training

Base: Ultralytics yolo11s.pt (COCO-pretrained), 60 epochs, imgsz=640.
Data: point-labeled chips from our blackjack-table proof-of-concept footage (labeled by vision agents — chip colour is a small class set, so no external labeling service was used).
Hardware: NVIDIA RTX 3060 (12 GB).

Metrics — frozen gate (never trained on)

Gate	Recall	Precision
`chip_test` (blackjack)	0.91	0.77

⚠️ Not casino accuracy (rule of the project). Gate footage is our own PoC recordings. This generation also produced false chip_black boxes on printed felt markings (fixed in gen 2). Dataset-internal mAP@50 (0.97) in metrics.json is not real-world accuracy.

Usage

python

import json, onnxruntime as ort
sess = ort.InferenceSession("model.onnx", providers=["CPUExecutionProvider"])
colours = json.load(open("model.classes.json"))   # index -> "chip_black", ...
# Detect boxes per colour, then count per class for stack/bet estimation.

License & provenance

AGPL-3.0, a fine-tune of Ultralytics YOLO11 (yolo11s.pt, AGPL-3.0) — these weights inherit AGPL-3.0 and are not an original work of ours. Networked deployment triggers AGPL §13 (offer the Corresponding Source). onnxruntime (MIT) keeps the inference code AGPL-free; the weights stay AGPL.

sroot/lgd-chips-gen1

LGD Chips — Gen 1 · YOLO11s casino chip-colour detector (5 colours)