Instructions to use honi05/chess-piece-yolo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use honi05/chess-piece-yolo with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("honi05/chess-piece-yolo") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Chess Piece Detectors (YOLOv8 โ pico / n / s)
Three YOLOv8 detectors trained on cell-box annotations derived from the synthetic
koryakinp/chess-positions
dataset (12 piece classes). Companion to the custom CNN classifier at
honi05/chess-piece-cnn.
| Variant | Origin | Params | mAP@50-95 | Occupied-cell acc | Latency ms/board | Disk MB |
|---|---|---|---|---|---|---|
yolo_pico/best.pt |
scaled YAML, from scratch | 184,300 | 0.255 | 0.2073 | 5.49 | 0.57 |
yolov8n/best.pt |
pretrained | 2,692,548 | 0.995 | 1.0000 | 4.35 | 5.59 |
yolov8s/best.pt |
pretrained | 9,843,604 | 0.995 | 1.0000 | 6.07 | 19.92 |
Classes (0-11): wP, wN, wB, wR, wQ, wK, bP, bN, bB, bR, bQ, bK
Usage
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
w = hf_hub_download("honi05/chess-piece-yolo", "yolov8n/best.pt")
model = YOLO(w)
results = model("board.jpg")
See the full report in the code repo github.com/Honi05/cvproject.
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