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bareethul/stop-sign-predictor

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App README

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.

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

FeatureTypeDescription
ImageJPG/PNG/WebcamTraffic scene input image

Label

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

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.

Example Usage

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

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.

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.

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).

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.

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