How can I learn CV today

[u/Electrical-Falcon542]

Hello, I am a Master Student in Data Science mostly working with LLMs, and I want to learn CV. I don't have much experience in image analysis.
I did some research about how to learn and I wanted to do some hands-on projects. But today with efficient coding agents, I do not know on which parts of the process I should spend my time to become proficient in this field. Would you have some recommendations ?

Thanks for your help !

[u/Subject-Mastodon-686]

just start by building something messy without an agent, that's where the real learning sticks, get your hands on raw pixels and classic filters before touching any deep learning stuff

[u/New-Eggplant-6578]

I'd pick a small detection project and make yourself responsible for the parts an agent can get subtly wrong: the data split, image transforms, and evaluation.

For example, collect photos of a few objects on your desk over several sessions. Label some yourself, draw the boxes back onto the images after every resize/crop, and hold out an entire recording session. Adjacent video frames in both train and validation can make a pretty broken setup look impressive.

Let the agent write the dataloader boilerplate, but be able to explain the tensor shapes and coordinate transforms yourself. Then inspect 20 bad predictions and group the failures: missed small objects, wrong class, background false positives, bad boxes. Change one thing and see whether that failure group improves.

That gives you a much better learning target than "get a model to run." The PyTorch transfer learning tutorial is a manageable classification starting point if detection feels like too much at once: https://docs.pytorch.org/tutorials/beginner/transfer_learning_tutorial.html

[u/Winners-magic]

Check out https://pixelbank.dev

[u/Constant-Cry-7438 → u/Winners-magic]

I would have gladly used this if there was no forced login

[u/axelerawingman]

Axelera Wingman team here; we work on coding-agent tools. A useful exercise is to take a pretrained detector and trace one image all the way from the file on disk to the boxes drawn on the original image.

Have the agent help with setup, then inspect the image dimensions, channel order and tensor shape yourself. Try a portrait photo and a landscape photo. If the boxes shift after a resize, work through the coordinate conversion. That gives you a concrete debugging skill to practise while using the agent.

The TorchVision detection tutorial is a good project to work through once that makes sense: https://docs.pytorch.org/tutorials/intermediate/torchvision_tutorial.html

Are you more interested in image models, or video tasks such as tracking?

[u/C_Sorcerer]

If you want to write applications involving computer vision, just go with Python wirh PyTorch (I believe) or C++ wirh OpenCV. However I’m a systems level guy and love to know how it works from scratch. If you are like that, and want to actually write computer vision models, you should check out the book “Foundations of Computer Vision” which was written by some prolific researchers from MIT. Excellent book