Python + Yolo + DQN - complete the machine vision program
Good day, Cossacks.
There is a task regarding machine vision. While it was modular code, it worked, albeit imperfectly. Now that I have separated it into files, I have run into issues with asynchronous processing, frame drops, and other matters. Since Yolo and DQN require machine resources, we need to discuss how to enhance the project's functionality (we will talk about what needs to be improved and what features we would like to have) and optimize, configure both the code and quantum optimization, along with other minor details.
Everything will be done in stages, as testing will be necessary, so tasks will be formed accordingly. I would like to see your hourly rates and a brief description of your experience.
I will then write to you in private messages to better understand you and convey what I want. From there, we will establish pricing and a more precise technical specification.
Sorry for the lengthy message, and thank you for your understanding.
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3 days113 USD
121 3 days113 USD
3 years of experience deploying systems on servers with 4GB of RAM. Experience working with YOLO on servers, quantizing models, and optimizing code.
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7 days226 USD
301 2 2 7 days226 USD
Greetings! I have experience in developing my own AI models (GAN, RNN, LSTM). I have used Tensorflow, PyTorch. I am ready to consider more details.
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1 day45 USD
868 14 0 1 day45 USD
The water is fine, everything is ok 🙂
Regarding the task, I see that the main problems right now are due to asynchronicity and resources, plus frame drops, which is especially critical for Yolo and DQN. If the modular code worked but not perfectly, then the separation of files somewhere disrupted the connections between components or caused additional delays. We need to look at how data is transmitted between modules, whether there are bottlenecks in video stream processing.
Regarding optimization – we can dig into multithreading, quantum optimization (if we really need to squeeze the maximum), as well as simply properly setting up the pipeline. Let's then figure out what we have and what we want to achieve.
Regarding the stages – it makes complete sense, because you can't avoid testing in such cases. It's better to identify critical points right away to avoid redoing everything unnecessarily.
Question: what is this all running on right now? What hardware, what libraries? Because this greatly affects which optimization approaches make sense.
… Regarding hours – I will write in private to discuss it in more detail, as it depends on the complexity of the tasks that arise along the way.
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