Add detector and classifier models with training artifacts

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+*.png filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: mit
+tags:
+  - object-detection
+  - image-classification
+  - yolo
+  - set-game
+  - card-game
+  - computer-vision
+---
+
+# Set Solver Models
+
+Trained models for the [Set card game](https://www.setgame.com/) solver.
+
+![Example detection](./example-detection.png)
+
+**Live demo**: [huggingface.co/spaces/wangtianthu/set-solver](https://huggingface.co/spaces/wangtianthu/set-solver)
+
+## Models
+
+### Detector — YOLOv11n
+
+Detects individual Set cards on a board image.
+
+| Metric | Value |
+|--------|-------|
+| mAP50 | 99.5% |
+| mAP50-95 | 97.4% |
+| Architecture | YOLOv11n |
+| Input size | 640x640 |
+| Epochs | 10 |
+| Training data | 4000 synthetic board images |
+
+**Files**: `detector/weights/best.pt` (PyTorch), `detector/weights/best.onnx` (ONNX)
+
+### Classifier — MobileNetV3
+
+Classifies each card's 4 attributes: shape, color, number, and fill.
+
+| 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](https://github.com/wangtian24/set-solver)

classifier/classifier_best.pt

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classifier/training_results.json

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+{
+  "test_loss": 0.009692762911799945,
+  "test_accuracy": {
+    "number": 1.0,
+    "color": 1.0,
+    "shape": 0.9992082343626286,
+    "fill": 0.997624703087886
+  },
+  "avg_test_accuracy": 0.9992082343626286,
+  "train_size": 9479,
+  "val_size": 1895,
+  "test_size": 1263
+}

detector/args.yaml

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+task: detect
+mode: train
+model: yolo11n.pt
+data: /Users/wangtian/workspace/set-solver/data/synthetic/dataset.yaml
+epochs: 10
+time: null
+patience: 20
+batch: 16
+imgsz: 640
+save: true
+save_period: -1
+cache: false
+device: mps
+workers: 8
+project: /Users/wangtian/workspace/set-solver/weights
+name: detector
+exist_ok: true
+pretrained: true
+optimizer: auto
+verbose: true
+seed: 0
+deterministic: true
+single_cls: false
+rect: false
+cos_lr: false
+close_mosaic: 10
+resume: false
+amp: true
+fraction: 1.0
+profile: false
+freeze: null
+multi_scale: 0.0
+compile: false
+overlap_mask: true
+mask_ratio: 4
+dropout: 0.0
+val: true
+split: val
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+iou: 0.7
+max_det: 300
+half: false
+dnn: false
+plots: true
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+visualize: false
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+classes: null
+retina_masks: false
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+show: false
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+save_crop: false
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+simplify: true
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+lrf: 0.01
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+copy_paste: 0.0
+copy_paste_mode: flip
+auto_augment: randaugment
+erasing: 0.4
+cfg: null
+tracker: botsort.yaml
+save_dir: /Users/wangtian/workspace/set-solver/weights/detector

detector/results.csv

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