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lets learn the state of the art in image classification in 2022 well find out what makes the most docHub difference in image classification the input is an image and the output is a single class label like cat dog etc before we go over the state-of-the-art models lets learn about the imagenet data set on which image classification accuracy is reported it is a data set consisting of more than 14 million images with over 20 000 classes a subset of this data set was used in the imagenet large scale visual recognition challenge between 2012 and 2017. this subset consists of 1.28 million training images with 1000 classes they also provide 50 000 validation images and 100 000 test images for reporting accuracy there are two measures of accuracy if the model is given only one guess to get the correct answer the accuracy is called top one accuracy on the other hand in top five accuracy if the correct answer or label is in the top five guesses made by the model we consider the answer c
