| --- |
| 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 **Superseded** by **[`lgd-chips-gen2`](https://huggingface.co/sroot/lgd-chips-gen2)** (the current |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| | 1 | **lgd-chips-gen1** (this) | black / white / green / red / pink | blackjack gate: recall 0.91 / precision 0.77 | superseded | |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. |
|
|
