Image Recognition with LLaVa in Python

In this video we learn how to easily do image recognition and labeling in Python using LLaVa and Ollama locally.
Ollama: ollama.com
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[@GuillermoGarcia75]

Riding the awesomeness wave again!

[@AlissonSantos-qw6db]

Nice, very helpful!
Is it possible to create embeddings of pictures with the model?

[@R8R809]

Thanks for the video, how to make sure that I install Ollama on the GPU not on the CPU?

[@wasgeht2409]

Thanks :) Is it possible to use this model as an ocr alternativ to get for example informationen from a jpeg image which is an id-card ?

[@sumukhas5418]

This will be too much heavy for just that
Instead considering yolo would be a better option

[@wasgeht2409]

@@sumukhas5418 Thanks for the answer :) Actually I am trying pytesseract to read id-card information, which are photographed by a phone and the results are not very good :/ Do you have some ideas, how I could get some better results?

[@giovannicordova4803]

If my local ram is 8 gb, which ollama model would you recommend to use?

[@WebWizard977]

deepseek-coder ❤

[@WebWizard977]

deepseek-coder ❤

[@Isusgsue]

What a nice vid. Can I do a ai without using open ai ?

[@throwyourmindat]

Hi could you please explain why someone has to refresh to get some reasonable output at least what we expect !? Why does something like this happens with such ai models ?

[@aigapol123]

Each Input for the model comes with a random seed, that's why you don't get the same responses with the same input every time, because of that you could try multiple times until you get a reasonable response.

[@syedmokarromhossain4867]

First comment 😊😊😊

[@aoa1015]

How much RAM and VRAM needed ?!

[@RedFoxRicky]

With 4-bit quantization, for LLaVA-1.5-7B, it uses less than 8GB VRAM on a single GPU, typically the 7B model can run with a GPU with less than 24GB memory, and the 13B model requires ~32 GB memory. You can use multiple 24-GB GPUs to run 13B model

[@arjuntt2604]

oh im too fast