Create README.md

README.md

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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)