Earlier, the datasets of labelled images were relatively small and they were able to solve simple recognition tasks well because of their size. But in real life, objects exhibit a large number of properties. So, to train the model for recognition, it is important to use larger training sets with a large number of attributes. The new larger datasets such as LabelMe consists of hundreds of thousands of fully-segmented images, and ImageNet consists of around 15 million labelled high-resolution images in over 22,000 categories. …

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Aditi Mittal

Machine Learning Enthusiast | Software Developer

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