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license: mit
tags:
- object-detection
- image-classification
- set-game
- card-game
- computer-vision
- synthetic-data
size_categories:
- 1K<n<10K
---
# Set Solver Dataset
Training data for the [Set card game](https://www.setgame.com/) solver.
**[Live demo](https://huggingface.co/spaces/wangtianthu/set-solver)** | **[Models](https://huggingface.co/wangtianthu/set-solver-models)** | **[Source code](https://github.com/wangtian24/set-solver)**
## Dataset Structure
### Classifier — Card Attribute Classification
`classifier/{number}/{color}/{shape}/{fill}/`
1943 cropped card images organized into 81 classes (3 numbers x 3 colors x 3 shapes x 3 fills).
| Attribute | Values |
|-----------|--------|
| Number | one, two, three |
| Color | red, green, blue (purple) |
| Shape | diamond, oval, squiggle |
| Fill | empty, full (solid), partial (striped) |
### Detector — Card Detection (YOLO format)
`detector/images/` and `detector/labels/`
5000 synthetic board images (1280x960) with YOLO-format bounding box annotations.
- **Train**: 4000 images (`detector/images/train/`)
- **Val**: 1000 images (`detector/images/val/`)
- **Labels**: YOLO `.txt` files + rich `.json` metadata per image
- **Class**: single class (`card`)
The synthetic boards feature:
- Grid, random, overlap, and pile card layouts
- Varied background textures (wood, marble, gradients)
- Perspective transforms simulating camera angles
- Noise objects (buttons, coins, paper clips)
- 9-12 cards per board
## Usage
### Detector training with Ultralytics
```python
from ultralytics import YOLO
model = YOLO("yolo11n.pt")
model.train(data="detector/dataset.yaml", epochs=10, imgsz=640)
```
### Classifier training
```python
from torchvision.datasets import ImageFolder
from torchvision import transforms
dataset = ImageFolder("classifier/", transform=transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
]))
```
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