Classification of Images using ANN in Distributed Environment

Guide: Prof. U.S. Tiwary, Indian Institute of Information Technology, Allahabad

*Collected over 5000 images from 7 different categories. Categories were:

  1. Flowers
  2. Aeroplanes
  3. Homes and Buildings
  4. Birds
  5. Guitars
  6. Insects
  7. Interiors
  • 5000 images were classified in 80%, 20% ratio. 80% images were used to train the model and 20% for testing.
  • Each category had few subcategories which were trained separately by 7 different models.
  • Each layer of ANN was parallelized using Parallel Patterns Library (PPL).
  • Artificial Neural Network was coded in C++.
  • We achieved 98.6% accuracy.