Publish chip gen1 (blackjack palette)

e01467e verified 11 days ago

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license: agpl-3.0
library_name: ultralytics
pipeline_tag: object-detection
base_model: Ultralytics/YOLO11
tags:
  - object-detection
  - ultralytics
  - yolo11
  - onnx
  - onnxruntime
  - casino-chips
  - chip-detection
  - casino
  - live-game-defender
model-index:
  - name: lgd-chips-gen1
    results:
      - task:
          type: object-detection
        dataset:
          type: lgd-chip-gate-blackjack
          name: LGD chip_test blackjack gate (private, never trained on)
        metrics:
          - type: recall
            value: 0.91
            name: Chip recall @50
          - type: precision
            value: 0.77
            name: Precision

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

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Superseded by lgd-chips-gen2 (the current served model — a dual-felt blend covering both blackjack and Ultimate Texas Hold'em, with a different venue-matched palette).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1	lgd-chips-gen1 (this)	black / white / green / red / pink	blackjack gate: recall 0.91 / precision 0.77	superseded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

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.