I'm thinking about coding a computer-vision program that can take an image as input and identify whether a certain symbol exists inside that image. For example, the input could be a guy holding a paper with International Symbol of Access printed, and the code would process this image and correctly identify the presence of this symbol.

I'm kind of new to this field, and done some research before asking the question. From my understanding I should train a model on a dataset that consists of images which do and do not contain the said symbol. The inputs to the model should be the symbol that I'm trying to detect coupled with the images from the datasets, and the output during the training will be either 1 or 0 depending on the image. The problem that I'm facing is, I don't know whether or not my choice of input/outputs is correct, and also I don't know how to generate my dataset. My project will be using a custom symbol to be detected in the images. I've tried looking up similar researches in the internet, but haven't been able to find any. I plan using tensorflow since I've worked with it earlier.

If you've come across similar projects, I'd really like to hear some guidance or even references that I can work with. Thanks in advance.

CodePudding user response：

If tensorflow is your choice, then you should go for yoloV3
But if pytorch is your choice, then go for yoloV5
Yolo or You Only Look Once algorithm is developed by Ultralytics
For more information and tutorial, you may visit to https://docs.ultralytics.com/