/Object-Classification-CIFAR-10-

Using CNN network to recognize random objects from CIFAR-10 database

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Object-Classification-CIFAR-10-

  • The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes. The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. There are 6,000 images of each class.
  • The Architecture that I designed is a CNN architecture which achieved an accuracy of 77.43% without image argumentation and 80.07 percent with Image argumentation.