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I know this question is likely to be closed as "opinion based", but I could not find any resource online and every link pointed in asking on Stack Overflow, so please be patient.

I'm trying to understand if Tensorflow is the right tool for object detection. I'm not talking about classification, but real object detection and recognition.
My use case is the following: given image A (live photo), find the matching one inside a catalogue of thousand of different images.
For example: live scanning of a supermarket product, find the matching one inside an high res catalogue of images. I'm not interested to know if the product is a shoe or a toothpaste, I want to know the "most matching" image (ie Prada model X or Colgate mint flavoured).

I already have a working script developed few years ago with OpenCV, using SURF feature detection with FLANN, but I wanted to know if there's a better tool for the job.
Can anyone point me in the right direction?

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While I'm unsure whether it provides a better solution than any you've already implemented, TensorFlow, and deep learning in general, can indeed be used for this purpose. A neural network can be created which takes an image as input and outputs a numeric vector. The Euclidean distance between vectors can be used to determine the similarity between different images, an approach which has been applied effectively for facial recognition (see this paper).

For a starting point in implementing this solution using TensorFlow, see this tutorial.

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Thank you very much! It's a very interesting article. Regarding performance, there's only one way to find it out: benchmarks, benchmarks, benchmarks!

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