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Thoughts on hardware setup for Computer Vision software 

Pysource
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Learn how to choose the ideal hardware for your computer vision projects. Our guide covers key factors, optimization techniques, and examples.
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13 сен 2024

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Комментарии : 5   
@soumen_das
@soumen_das День назад
Can you pls deep dive on choosing a camera? For eg there are different types of cameras such area scan, line scan, webcam, mobile cam etc. How do I choose which to use?
@sololearner89
@sololearner89 3 месяца назад
Thank you so much, i really learned a lot from your channel only from this video. I would be grateful if you made a longer video with some implementations
@user-mc7tg4pf3i
@user-mc7tg4pf3i 2 месяца назад
Hell Sir Thanks for your all videos and efforts. I am following your channel, but I request you please upload one detail video on how to finetuning Yolov5 model for custome images classification.
@Elnasir
@Elnasir 3 месяца назад
Hi. Please I need the solution you implemented for your optimization (processing more frames). Kindly share your optimization technique. Thank you.
@pysource-com
@pysource-com 3 месяца назад
Hi Elnasir, The optimization is a complex operation and often more complex then the entire project itself. I can give you here some guidelines on what to look for. Mainly there are 4 things to look into: - python code must be optimized, avoid using python loops and instead use operation on the array with numpy library - you can use libraries like timeit , to check the speed of every chunk of your code on python and try to optimize it as much as possible. - the deep learning model must be converted into tensorRT format, optimised for the size of the data you need to pass into it (number of images per batch and size of image), tailored to the specific GPU you're using. - you need to leverage multiprocessing and split the task of your software into multiple process to leverage the power of the CPU
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