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The way I view it: 'Classification' (in the context of machine learning) is a type of problem in which you assign a 'label' to an object. Formally, 'Classification' is a type of problem whereas labeling is a function from an object to a set of labels (maybe infinite).
Data labeling, in the context of machine learning, is the process of detecting and tagging data samples. The process can be manual but is usually performed or assisted by software.
Label: Labels are the final output. You can also consider the output classes to be the labels. When data scientists speak of labeled data, they mean groups of samples that have been tagged to one or more labels.
Data labeling is the manual curation of data by humans on machine learning and AI applications. Roughly we call it supervised machine learning because computers need human supervision to get trained to execute tasks that are tricky for machines, but definitely easy for humans such as image recognition.
Use a soft lead #2 pencil to write using very light pressure on the reverse side of the photograph. If you have a set of similar photos, write in a consistent spot such as the top or the bottom on the reverse side of the print. Some modern photographic papers are glossy or slick making it impossible to use pencil.
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