README.md

metadata

title: Stop Sign Predictor
emoji: 🦀
colorFrom: yellow
colorTo: yellow
sdk: gradio
sdk_version: 5.47.2
app_file: app.py
pinned: false
license: mit

Stop Sign Classifier

This Hugging Face Space hosts a Gradio app that predicts whether an uploaded traffic image contains a Stop Sign.
It uses a classmate’s AutoGluon MultiModal model trained during Homework 2.

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Dataset & Model Card

• Dataset: Traffic sign images (binary classification: Stop Sign vs. No Stop Sign).
• Dataset Information: This app uses the ecopus-sign--classification The dataset is licensed under MIT and consists of ~300 records in Parquet format (split into original and augmented)
• Model Repo: scottymcgee/image-classifier
• Framework: AutoGluon MultiModal
• Task: Binary classification → predict Stop Sign or No Stop Sign.

Input Features

Feature	Type	Description
Image	JPG/PNG/Webcam	Traffic scene input image

Label

• 0 → 🚫 No Stop Sign
• 1 → 🛑 Stop Sign

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App Interface

• Widgets:

  • Image Upload (supports drag and drop or webcam).
  • Confidence Threshold slider (filter low confidence results).

• Output:

  • Original uploaded image.
  • Preprocessed 256×256 version (what the model actually sees).
  • Human readable prediction with probabilities.

• Examples: 3 preloaded example images for quick testing.

• Validation: Ensures uploaded files are valid images and not oversized.

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Example Usage

Example Image	Predicted Class
stop1.jpg	🛑 Stop Sign
no_stop1.jpg	🚫 No Stop Sign
stop2.jpg	🛑 Stop Sign

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Technical Details

• Backend: AutoGluon MultiModalPredictor loaded from classmate’s Hugging Face repo.
• Interface: Gradio.
• Deployment: Hugging Face Spaces (sdk: gradio).
• Environment: Python 3.10, pinned requirements.

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Limitations

• Binary labels only: App only distinguishes Stop Sign vs. No Stop Sign.
• Dataset limitations: Accuracy depends heavily on dataset quality (lighting, occlusion, unusual traffic scenes).
• Threshold behavior: High thresholds may filter out useful predictions.

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Future Improvements

• Expand dataset to include more traffic sign types (yield, speed limit, etc.).
• Add bounding box detection to highlight the Stop Sign in the image.
• Display top-k predictions dynamically instead of threshold filtering.
• Improve UI with tooltips and confidence visualization (bar chart).

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AI Disclosure

This app was developed with support from AI assistance in:

• Adding input validation and a configurable threshold slider
• Streamlining the Gradio interface for better usability
• Drafting the initial version of this README

All core modeling and training artifacts come from a classmate’s AutoGluon model.

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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference