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README.md

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+---
+license: apache-2.0
+tags:
+- image-classification
+- checkbox-detection
+- computer-vision
+- pytorch
+datasets:
+- wendys-llc/chkbx
+metrics:
+- accuracy
+library_name: pytorch
+---
+
+# Checkbox State Classifier
+
+This model classifies whether a checkbox is checked or 
+unchecked.
+
+## Model Details
+- **Architecture**: EfficientNetV2-S (PyTorch)
+- **Input Size**: 128x128 RGB images
+- **Output**: Binary classification (unchecked: 0, checked: 1)
+- **Validation Accuracy**: 97.1%
+- **Training**: Mixed precision on A100 GPU
+
+## Quick Start
+
+```python
+import torch
+from PIL import Image
+from torchvision import transforms
+from huggingface_hub import hf_hub_download
+import torch.nn as nn
+from torchvision.models import efficientnet_v2_s, 
+EfficientNet_V2_S_Weights
+
+# Define model architecture
+class EfficientNetV2Classifier(nn.Module):
+    def __init__(self, num_classes=2, dropout_rate=0.3):
+        super().__init__()
+        self.backbone = efficientnet_v2_s(weights=EfficientNet
+_V2_S_Weights.IMAGENET1K_V1)
+        num_features = self.backbone.classifier[1].in_features
+        self.backbone.classifier = nn.Sequential(
+            nn.Dropout(dropout_rate),
+            nn.Linear(num_features, 512),
+            nn.SiLU(inplace=True),
+            nn.BatchNorm1d(512),
+            nn.Dropout(dropout_rate),
+            nn.Linear(512, 256),
+            nn.SiLU(inplace=True),
+            nn.BatchNorm1d(256),
+            nn.Dropout(dropout_rate/2),
+            nn.Linear(256, num_classes)
+        )
+    
+    def forward(self, x):
+        return self.backbone(x)
+
+# Download and load model
+model_path = hf_hub_download(repo_id="wendys-llc/checkbox-classifier", 
+filename="checkbox_classifier.pth")
+checkpoint = torch.load(model_path, map_location='cpu')
+
+model = EfficientNetV2Classifier(num_classes=2)
+model.load_state_dict(checkpoint['model_state_dict'])
+model.eval()
+
+# Image preprocessing
+transform = transforms.Compose([
+    transforms.Resize((128, 128)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], 
+std=[0.229, 0.224, 0.225])
+])
+
+# Predict
+def predict(image_path):
+    image = Image.open(image_path).convert('RGB')
+    input_tensor = transform(image).unsqueeze(0)
+    
+    with torch.no_grad():
+        output = model(input_tensor)
+        probabilities = torch.nn.functional.softmax(output, 
+dim=1)
+        predicted = torch.argmax(probabilities, dim=1).item()
+        confidence = probabilities[0][predicted].item()
+    
+    labels = {0: "unchecked", 1: "checked"}
+    return labels[predicted], confidence
+
+# Example usage
+result, conf = predict("checkbox.jpg")
+print(f"Result: {result} (confidence: {conf:.1%})")
+
+Training Dataset
+
+Trained on https://huggingface.co/datasets/wendys-llc/chkbx
+dataset containing ~6,000 annotated checkbox images.
+
+Limitations
+
+- Trained specifically on UI checkboxes, may not work well on
+hand-drawn checkmarks
+- Best performance on clear, high-contrast checkbox images
+- Input images are resized to 128x128, very small checkboxes
+may lose detail