Loading
¿Tienes un flujo de trabajo de ComfyUI? Obtén un Workflow Doc legible en tu navegador.Haz legible un flujo de trabajo.Pruébalo ahora ↗
A fine-tuned YOLOv8n-cls model that classifies 64×64 image patches from a Go board into three classes: empty, black stone, or white stone. Trained on synthetic board renders from SGF game records, then exported to...
Fuente del modelo
Descripción de la fuente
A fine-tuned YOLOv8n-cls model that classifies 64×64 image patches from a Go board into three classes: empty, black stone, or white stone. Trained on synthetic board renders from SGF game records, then exported to Core ML for on-device inference on iOS.
Fuentes
1 fuenteVerificado 28 sept
Artefactos del modelo
3 artefactosExtractos de fuentes
2 extractos| Base Model | yolov8n-cls.pt (Ultralytics YOLOv8 Nano) |
| Task | Image Classification (3 classes) |
| Input Size | 64×64 pixels |
| Classes | empty, black, white |
| Framework | Ultralytics YOLOv8 |
| Training Device | Apple MPS (Metal Performance Shaders) |
| Optimizer | AdamW |
| Epochs | 30 (early stopped at 17) |
| Batch Size | 128 |
| Learning Rate | 0.001 (cosine schedule) |
| Metric | Value |
|---|---|
| Top-1 Accuracy | 100% |
| Top-5 Accuracy | 100% |
| Best Val Loss | 0.00011 (epoch 17) |
| Best Train Loss | 0.00204 (epoch 14) |
The model converges rapidly — reaching >99.9% accuracy by epoch 1 — because the 64×64 patch classification task is well-separated for these three classes.
Synthetic 64×64 image patches extracted from Go board renders generated from SGF game records. The dataset includes:
.
├── weights/
│ ├── best.pt # Best checkpoint (val_loss=0.00011)
│ └── last.pt # Final epoch checkpoint
├── args.yaml # Training configuration
├── results.csv # Per-epoch metrics
├── results.png # Training curves
├── confusion_matrix.png # Confusion matrix
├── confusion_matrix_normalized.png
└── best.mlpackage/ # Core ML export for iOS
├── Manifest.json
└── Data/com.apple.CoreML/
├── model.mlmodel
└── weights/weight.bin
from ultralytics import YOLO
model = YOLO("best.pt")
results = model("path/to/go_board.jpg")
Drop best.mlpackage into your Xcode project. The model accepts a 64×64 RGB image and outputs class probabilities for empty/black/white.
import CoreML
import Vision
guard let model = try? VNCoreMLModel(for: go_stone_classifier().model) else { return }
let request = VNCoreMLRequest(model: model) { request, error in
if let results = request.results as? [VNClassificationObservation] {
for result in results {
print("\(result.identifier): \(result.confidence)")
}
}
}
If you use this model, please cite:
@misc{yolo-go-stone-classifier,
author = {rociiu},
title = {YOLOv8n-cls Go Stone Classifier},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/rociiu/yolo-go-stone-classifier}},
}
MIT
weights/last.pt
pt · 2,83 MB · SHA-256 741f7a8f4ecb…6cd6 · Hugging Face
--- license: mit language: - en tags: - yolo - yolov8 - ultralytics - go - baduk - weiqi - board-game - image-classification - computer-vision - coreml - ios datasets: - rociiu/go-board-patches metrics: - accuracy model-index: - name: yolo-go-stone-classifier results: - task: type: image-classification dataset: type: go-board-patches name: Go Board Stone Patches metrics: - type: accuracy value: 1.0 name: Top-1 Accuracy - type: val_loss value: 0.00011 --- # YOLOv8n-cls Go Stone Classifier A fine-tuned YOLOv8n-cls model that classifies 64×64 image patches from a Go board into three classes: **empty**, **black stone**, or **white stone**. Trained on synthetic board renders from SGF game records, then exported to Core ML for on-device inference on iOS. ## Model Details | | | |---|---| | **Base Model** | `yolov8n-cls.pt` (Ultralytics YOLOv8 Nano) | | **Task** | Image Classification (3 classes) | | **Input Size** | 64×64 pixels | | **Classes** | `empty`, `black`, `white` | | **Framework** | Ultralytics YOLOv8 | | **Training Device** | Apple MPS (Metal Performance Shaders) | | **Optimizer** | AdamW | | **Epochs** | 30 (early stopped at 17) | | **Batch Size** | 128 | | **Learning Rate** | 0.001 (cosine schedule) | ## Performance | Metric | Value | |---|---| | Top-1 Accuracy | **100%** | | Top-5 Accuracy | 100% | | Best Val Loss | 0.00011 (epoch 17) | | Best Train Loss | 0.00204 (epoch 14) | > The model converges rapidly — reaching >99.9% accuracy by epoch 1 — because the 64×64 patch classification task is well-separated for these three classes. ## Training Data Synthetic 64×64 image patches extracted from Go board renders generated from SGF game records. The dataset includes: - Renders with realistic stone textures, grid lines, star points - Multiple renders per board state (3 renders with random augmentation) - Up to 10 board snapshots per SGF game - Dataset split: 70% train / 15% val / 15% test ## Repository Contents ``` . ├── weights/ │ ├── best.pt # Best checkpoint (val_loss=0.00011) │ └── last.pt # Final epoch checkpoint ├── args.yaml # Training configuration ├── results.csv # Per-epoch metrics ├── results.png # Training curves ├── confusion_matrix.png # Confusion matrix ├── confusion_matrix_normalized.png └── best.mlpackage/ # Core ML export for iOS ├── Manifest.json └── Data/com.apple.CoreML/ ├── model.mlmodel └── weights/weight....
Source context: 29 downloads · 1 likes · Pipeline image-classification · Library ultralytics · Repo rociiu/yolo-go-stone-classifier