| # 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) | |