# Model Card for scottymcgee/image-classifier-stop-sign

This model classifies traffic-sign images as either **containing a stop sign** or **not containing a stop sign**.  
It was trained with AutoGluon’s `MultiModalPredictor` on a binary image dataset of street signs.

## Model Details

### Model Description

- **Developed by:** Scotty McGee  
- **Model type:** Image classifier (binary classification)  
- **Languages (NLP):** Not applicable (vision model)  
- **Finetuned from model:** Timm image backbone used by AutoGluon (default is EfficientNet or ResNet depending on config)

### Model Sources

- **Repository:** https://huggingface.co/scottymcgee/image-classifier  

## Uses

### Direct Use

Use this model to classify whether an input image contains a stop sign or not. It takes an RGB image as input and returns a predicted label and probabilities.

### Downstream Use

It can be incorporated into larger perception systems (e.g., driver assistance, robotics) as a pre-screening classifier.

### Out-of-Scope Use

Not intended for:
- Safety-critical deployment without further validation.
- Identifying other sign types beyond stop / no-stop.
- High-stakes enforcement or surveillance applications.

## Bias, Risks, and Limitations

The model is trained on the specific dataset you provided. It may:
- Misclassify unusual or occluded stop signs.
- Perform poorly on non-U.S. stop sign shapes/colors if not present in training.
- Inherit any biases in the training images.

### Recommendations

Always test on your target data before deployment. Combine with additional checks in safety-critical scenarios.

## How to Get Started with the Model

```python
from autogluon.multimodal import MultiModalPredictor

predictor = MultiModalPredictor.load("scottymcgee/image-classifier-stop-sign")
preds = predictor.predict(["example.jpg"])
print(preds)