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Given thousands of images, where some of the images contain target objects and others do not, is there an easy way of drawing bounding boxes on these target objects rather than relying on manual annotation? Wouldn't drawing 4 orientations of an object and their respective bounding boxes and randomly inserting them into the images be a viable option?

It becomes painful to manually annotation thousands of images by yourself.

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How many classes do you want to annotate it?

We can get the bounding boxes and class names coarsely by using the pre-trained models such as YOLO-V3 darknet, SSD and others.

Then, we can load those annotations using the tool labelImg and manually correct it. It reduces the lot of work and it is called the Human-AI labeling technique.

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Automatic image annotation (also known as automatic image tagging or linguistic indexing) is the process by which a computer system automatically assigns metadata in the form of captioning or keywords to a digital image. This application of computer vision techniques is used in image retrieval systems to organize and locate images of interest from a database.

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  • $\begingroup$ This is more of a comment/definition than an answer. Can you amend your reply? $\endgroup$ – Oliver Mason Feb 28 at 8:59

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