Image Recognition with LLaVa in Python

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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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Опубликовано:

4 авг 2024

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Комментарии : 20

@blackstonesoftware7074 18 дней назад

This is quite useful! It gives me some great ideas for my own local apps!

@GuillermoGarcia75 Месяц назад

Riding the awesomeness wave again!

@AlissonSantos-qw6db Месяц назад

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

@R8R809 Месяц назад

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

@wasgeht2409 Месяц назад

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 Месяц назад

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

@wasgeht2409 Месяц назад

@@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?

@Isusgsue Месяц назад

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

@giovannicordova4803 Месяц назад

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

@WebWizard977 Месяц назад

deepseek-coder ❤

@WebWizard977 Месяц назад

deepseek-coder ❤

@antonpictures 19 дней назад

rag - webcam - selfawareness - speech --> tutorial pls

@throwyourmindat Месяц назад

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 Месяц назад

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.

@aoa1015 Месяц назад

How much RAM and VRAM needed ?!

@RedFoxRicky Месяц назад

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

@potatoes1000 20 дней назад

is this fully offline? I am not sure you downloaded the 13B 7.4Gb package

@syedmokarromhossain4867 Месяц назад

First comment 😊😊😊

@fastmamajama День назад

wow this is too easy to be real. i am using opencv to record videos of flying saucers. i could record images and use llama to verify if there is a flying saucer in it. can i also search videos with videos: instead of images:?