# ImageNet

[ImageNet](http://image-net.org/) is formally a project aimed at (manually) labeling and categorizing images into almost 22,000 separate object categories for the purpose of computer vision research.

However, when we hear the term *“ImageNet”* in the context of deep learning and Convolutional Neural Networks, we are likely referring to the *ImageNet Large Scale Visual Recognition Challenge*, or ILSVRC for short.

The goal of this image classification challenge is to train a model that can correctly classify an input image into 1,000 separate object categories.

Models are trained on \~1.2 million training images with another 50,000 images for validation and 100,000 images for testing.

These 1,000 image categories represent object classes that we encounter in our day-to-day lives, such as species of dogs, cats, various household objects, vehicle types, and much more. You can find the full list of object categories in the ILSVRC challenge [here](http://image-net.org/challenges/LSVRC/2014/browse-synsets).

When it comes to image classification, the ImageNet challenge is the *de facto* benchmark for computer vision classification algorithms — and the leaderboard for this challenge has been ***dominated*** by Convolutional Neural Networks and deep learning techniques since 2012.


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