ImageNet is a massive dataset of over 15 million labeled high-resolution images across about 22,000 categories. Developed by researchers at Stanford and Princeton, ImageNet is commonly used for machine learning purposes, particularly in object recognition tasks. Information provided by this dataset has been instrumental in the development and advancement of deep learning algorithms.
It has also been the foundation for the annual ImageNet Large Scale Visual Recognition Challenge (ILSVRC) since 2010.
In ImageNet, each synset is represented by several hundred to a few thousand images, aiming to provide an average of 1000 images to illustrate each synset. Images of each synset are quality controlled and human-annotated.
For machine learning applications, the dataset is often split into training, validation and testing sets. Algorithms are trained on the training dataset, fine-tuned with validation dataset and then tested on the testing dataset. During the ImageNet Large Scale Visual Recognition Challenge, participating algorithms were tasked with two responsibilities: object detection from a set of 200 object classes and image classification among 1000 different classes.
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