---
license: apache-2.0
tags:
- image-classification
- computer-vision
- checkbox-detection
- efficientnet
datasets:
- wendys-llc/chkbx
metrics:
- accuracy
- f1
- precision
- recall
base_model: google/efficientnet-b0
model-index:
- name: checkbox-classifier-efficientnet
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      type: wendys-llc/chkbx
      name: Checkbox Detection Dataset
      split: validation
    metrics:
    - type: accuracy
      value: 0.97
      name: Validation Accuracy
library_name: transformers
pipeline_tag: image-classification
---

# Checkbox State Classifier - EfficientNet-B0

A fine-tuned EfficientNet-B0 model for binary classification of checkbox states (checked/unchecked). This model achieves ~95% accuracy on UI checkbox detection.

## Model Description

This model is fine-tuned from [google/efficientnet-b0](https://huggingface.co/google/efficientnet-b0) on the [wendys-llc/chkbx](https://huggingface.co/datasets/wendys-llc/chkbx) dataset. It's designed to classify UI checkboxes in screenshots and interface images.

### Key Features
- **No `trust_remote_code` required** - Uses native transformers support
- **Fast inference** - EfficientNet-B0 is optimized for speed
- **High accuracy** - ~95% on validation set
- **Simple API** - Works with transformers pipeline out of the box

## Usage

### Quick Start with Pipeline (Recommended)

```python
from transformers import pipeline
from PIL import Image

# Load the model
classifier = pipeline("image-classification", model="wendys-llc/checkbox-classifier-efficientnet")

# Classify an image
image = Image.open("checkbox.jpg")
results = classifier(image)

# Print results
for result in results:
    print(f"{result['label']}: {result['score']:.2%}")

# Get just the top prediction
top_result = classifier(image, top_k=1)[0]
print(f"Checkbox is: {top_result['label']} (confidence: {top_result['score']:.2%})")
```

### Using AutoModel and AutoImageProcessor

```python
from transformers import AutoImageProcessor, AutoModelForImageClassification
import torch
from PIL import Image

# Load model and processor
processor = AutoImageProcessor.from_pretrained("wendys-llc/checkbox-classifier-efficientnet")
model = AutoModelForImageClassification.from_pretrained("wendys-llc/checkbox-classifier-efficientnet")

# Prepare image
image = Image.open("checkbox.jpg")
inputs = processor(images=image, return_tensors="pt")

# Get prediction
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    
    # Get predicted class
    predicted_class_idx = logits.argmax(-1).item()
    predicted_label = model.config.id2label[predicted_class_idx]
    
    # Get confidence scores
    probabilities = torch.nn.functional.softmax(logits, dim=-1)
    confidence = probabilities.max().item()

print(f"Prediction: {predicted_label} (confidence: {confidence:.2%})")
```

### Batch Processing

```python
from transformers import pipeline
from PIL import Image

classifier = pipeline("image-classification", model="wendys-llc/checkbox-classifier-efficientnet")

# Process multiple images
images = [Image.open(f"checkbox_{i}.jpg") for i in range(1, 4)]
results = classifier(images)

for i, result in enumerate(results):
    top_pred = result[0]  # Get top prediction
    print(f"Image {i+1}: {top_pred['label']} ({top_pred['score']:.2%})")
```

## Model Details

### Architecture
- **Base Model**: google/efficientnet-b0
- **Model Type**: EfficientNet for Image Classification
- **Number of Labels**: 2 (checked, unchecked)
- **Input Size**: 224x224 RGB images
- **Framework**: PyTorch via Transformers

### Training Details
- **Dataset**: [wendys-llc/chkbx](https://huggingface.co/datasets/wendys-llc/chkbx)
  - ~4,800 training samples
  - ~1,200 validation samples
- **Training Configuration**:
  - Epochs: 15 (with early stopping)
  - Batch Size: 64 (on A100)
  - Learning Rate: Default AdamW
  - Mixed Precision: FP16
  - Hardware: NVIDIA A100 GPU

## Acknowledgments

- Base model: [google/efficientnet-b0](https://huggingface.co/google/efficientnet-b0)
- Dataset: [wendys-llc/chkbx](https://huggingface.co/datasets/wendys-llc/chkbx)
- Framework: [HuggingFace Transformers](https://github.com/huggingface/transformers)

## License

This model is licensed under the Apache 2.0 License. See the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) file for details.