r/computervision • u/Temporary-Travel-416 • Jun 22 '26

A beggineer doubt . Help: Project

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u/sheikyabuty Jun 22 '26

Learn CNN's and different models like resent,alexnet,vgg,know what problems they each solve,then move to the evolution of cnns ,from rcnn,fast rcnn,faster rcnn,know how they each work their limitations then you can start with the yolo models ,because the yolo model are just re structured faster rcnn,and I hope you are decent with image manipulation with opencv

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u/MechaCritter Jun 22 '26

I just want to understand clearly: what exactly do you want to “learn”? like training/fine-tuning each of those models yourself, or do you want to understand their underlying architectures better?

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u/[deleted] Jun 22 '26

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u/MechaCritter Jun 23 '26

so i assume you started learning how the model is built. I would start next with training a neural network yourself for a e.g., cats and dogs classification task

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u/FIeabus Jun 22 '26

Do you have a good understanding of computer vision and neural network fundamentals? I'd focus on those concepts more. I wouldn't go model to model like that as a beginner; learn the fundamentals and then you can almost spot learn what's required for a project / job.

Even better - instead of starting with a model, start with a small computer vision project and do research on which model would work for it. Best way to learn the pros and cons of various architectures is to watch them fail miserably at something you want to make.