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This project tackles the challenge of protest detection from images, identifying whether an image represents a protest or not. Inspired by the paper ["Protest activity detection and perceived violence estimation from social media images"], I've extended it using: EfficientNet architecture, Data Augmentation and Comparative Model Analysis.

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aditivermaa04/Protest_Detection_ML_model

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🧠 Protest Detection from Images

This project aims to detect protest activity in images using deep learning techniques. Inspired by the paper "Protest activity detection and perceived violence estimation from social media images", the model classifies whether an image represents a protest or not.

πŸ” Motivation

With the rise of social media as a real-time news source, being able to detect protest-related content has applications in:

  • Public safety
  • Sociopolitical research
  • Crisis response automation

This project explores how EfficientNet, advanced data augmentation, and comparative model analysis can improve image-based protest detection performance.


πŸš€ Features

  • βœ… Binary image classification: Protest vs Non-Protest
  • βœ… Backbone: EfficientNet-B0 to B3
  • βœ… Data augmentation using Albumentations for robustness
  • βœ… Comparative analysis with other CNNs (e.g., ResNet, VGG)
  • βœ… Evaluation with accuracy, ROC-AUC, confusion matrix, etc.

πŸ“Š Dataset

This project uses the dataset provided in the original paper:

Wang, Z., Hale, S. A., Adelani, D. I., & Hanna, A. (2018). Protest activity detection and perceived violence estimation from social media images.

You can refer to the paper for data access.


πŸ“š References


🀝 Contributing

PRs and feedback are welcome! If you’d like to contribute, fork this repo and submit a pull request.


About

This project tackles the challenge of protest detection from images, identifying whether an image represents a protest or not. Inspired by the paper ["Protest activity detection and perceived violence estimation from social media images"], I've extended it using: EfficientNet architecture, Data Augmentation and Comparative Model Analysis.

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