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

from autogluon.multimodal import MultiModalPredictor

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