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# 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)
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