How can I use yolo for box detection?

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Sure, I will if I get successful. But first I need help. I do not have training data and thus I will have to create my own. Now as you said I want to detect the whole grid directly.In total one row has 9 points (8 boxes) and in total I have gt 81 points to detect. But the major problem is that internet images aren't of same sizes and thus how to I prepare input vector. For simple mnist all images are 28 * 28 or 32 * 32. But in that small fitting is absolutely trash. I think maybe 400 * 400 image would be fine but again fitting all the images to 400 * 400 would distort them. Any suggestions?

Different sizes of input images are an advantage. And you don't need digit classifier at all, as YOLO will do everything at one go. As for the training data, sudoku boards are quite simple, so apart from searching the net, you may generate numerous examples, rotate them, skew them etc. to enrich your set.You shouldn't rely exclusively on artificial data though.

No no, to detect those 81 boxes I need to feed a long vector of 81 * 4 of several images coordinates so that it can train on this data. Therefore it would be a difficult task and would take time or I can just mark newspaper sudoku's grid boxes with some distinctive color and automate this process of pixel collection once pictures are clicked. Also, can I get an implementation of yolo like something written by someone so that I can get some idea? When I see on the net all talks are about pre-trained models and no code.

It will definitely take quite some of time to prepare the training data from real images. Here is how to compile and train Yolo: https://github.com/AlexeyAB/darknet Here is how to use it in OpenCV: https://www.learnopencv.com/deep-lear...