Metric	Value
Overall accuracy	99.9%
Number accuracy	100%
Color accuracy	100%
Shape accuracy	99.9%
Fill accuracy	99.8%
Architecture	MobileNetV3-Small
Input size	224x224
Training data	~9500 cropped card images (81 classes)

File: classifier/classifier_best.pt

Usage

python

from ultralytics import YOLO
from PIL import Image

# Load detector
detector = YOLO("detector/weights/best.pt")
results = detector("board_photo.jpg", conf=0.25)

# Load classifier
import torch
from src.train.classifier import SetCardClassifier

classifier = SetCardClassifier(pretrained=False)
checkpoint = torch.load("classifier/classifier_best.pt", map_location="cpu")
classifier.load_state_dict(checkpoint["model_state_dict"])
classifier.eval()

Training

Both models were trained on synthetic data generated by a custom board generator that produces realistic Set game layouts with varied backgrounds, perspective transforms, and noise objects.

Source code: github.com/wangtian24/set-solver

wangtianthu/set-solver-models

Set Solver Models

Models

Detector — YOLOv11n

Classifier — MobileNetV3

Usage

Training

wangtianthu/
set-solver-models