Google’s Open Images and YouTube8-M have released datasets of annotated images and video to aid researchers develop new image analysis techniques.
The tagged Open Images dataset has 9 million entries, while YouTube8-M’s database contains 8 million videos with 50,000 hours of footage.
The datasets have been made available to further the development of machine learning algorithms, a technique whereby a machine can learn to recognise content in images based on tagged data previously supplied to it. Machine learning potentially offers more accurate image analysis software, but requires large volumes of data to do so.
Halcon 13, the latest version of MVTec’s machine vision library, uses deep learning algorithms, notably for OCR which, according to the company speaking at the Vision show in Stuttgart in November, gives a read rate two times faster than earlier OCR tools, but requires around 50 million images to cover all possible characters.
Most industrial vision software companies are experimenting with machine learning techniques in one way or another. Swiss company Vidi Systems is one early industrial image analysis software library that uses deep learning algorithms – it was shortlisted for the Vision Award at Vision 2016.
The Open Images and YouTube8-M datasets could also be useful for engineers developing embedded vision solutions.
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