Joint Image Recognition and Verification

Project Overview

Engineered and optimized deep learning architectures for the dual task of image recognition (classification) and verification (similarity assessment). The system implements state-of-the-art techniques for feature extraction and embedding refinement to achieve high accuracy in both tasks.

Technical Implementation

  • Converted the ConvNeXtV2 architecture from research paper to functional code
  • Integrated comprehensive data augmentation techniques to enhance model robustness:
    • Random rotations, crops, and flips
    • Color jittering and normalization
    • Cutout and mixup augmentations
  • Applied neural network pruning to optimize model size while maintaining performance
  • Leveraged advanced contrastive loss functions to refine feature embeddings:
    • ArcFace for angular margin-based feature learning
    • SphereFace for hyperspherical feature space optimization

Performance Metrics

  • Achieved an Equal Error Rate (EER) of 4% for verification tasks
  • Maintained high classification accuracy across multiple image categories
  • Optimized inference speed for real-time applications

Technologies Used

  • Python
  • TensorFlow
  • Keras
  • PyTorch
  • Computer vision techniques

Applications

This joint recognition and verification system has applications in facial recognition, product identification, security systems, and content-based image retrieval. The architecture’s dual-purpose design makes it particularly valuable for applications requiring both identification and similarity assessment.

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