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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 **Superseded** by **[`lgd-chips-gen2`](https://huggingface.co/sroot/lgd-chips-gen2)** (the current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| 1 | **lgd-chips-gen1** (this) | black / white / green / red / pink | blackjack gate: recall 0.91 / precision 0.77 | superseded |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](https://huggingface.co/sroot/lgd-chips-gen2) | black / green / pink / purple / yellow | UTH gate: 0.80 / 0.85 / colour 0.97 | ✅ current |
Card detectors: [lgd-cards-gen1](https://huggingface.co/sroot/lgd-cards-gen1) · [lgd-cards-gen2](https://huggingface.co/sroot/lgd-cards-gen2) · [lgd-cards-gen3](https://huggingface.co/sroot/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.
Built for **Live Game Defender (LGD)** — an on-prem AI integrity monitor for live casino table
games. © 2026 TechTools s.r.o.
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