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Optimize Docs images (#15900)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2024-08-30 05:52:10 +05:00 • committed by GitHub
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docs/en/guides/yolo-thread-safe-inference.md
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@ -13,7 +13,7 @@ Running YOLO models in a multi-threaded environment requires careful considerati
Python threads are a form of parallelism that allow your program to run multiple operations at once. However, Python's Global Interpreter Lock (GIL) means that only one thread can execute Python bytecode at a time.
<p align="center">
<img width="800" src="https://user-images.githubusercontent.com/26833433/281418476-7f478570-fd77-4a40-bf3d-74b4db4d668c.png" alt="Single vs Multi-Thread Examples">
<img width="800" src="https://github.com/ultralytics/docs/releases/download/0/single-vs-multi-thread-examples.avif" alt="Single vs Multi-Thread Examples">
</p>
While this sounds like a limitation, threads can still provide concurrency, especially for I/O-bound operations or when using operations that release the GIL, like those performed by YOLO's underlying C libraries.