A question about validation set drawing image results about ultralytics HOT 2 OPEN

@Love-syntacticSugar hello! It's great to see you've done a thorough investigation into the threading issue with the plot_images method. 👍

Setting the threaded=False parameter indeed forces the plotting to run on the main thread, which can help avoid issues related to thread safety and data consistency, especially in environments where the Python GIL (Global Interpreter Lock) might cause unexpected behavior when using multiple threads.

Regarding security concerns, using a single thread by setting threaded=False generally doesn't introduce security risks by itself. It primarily affects performance due to sequential execution. The main risk in multithreading environments typically involves data corruption or race conditions, not security vulnerabilities.

If you prefer not to modify the threading behavior, another approach could be to ensure that all data passed between threads is properly synchronized or that thread-safe data structures are used. However, this might require more in-depth changes to the codebase.

If the current solution of setting threaded=False works for you without significant performance drawbacks, it's a valid approach to stick with it, especially if it maintains the stability of your validation outputs.

Keep exploring and feel free to reach out if you have more questions! 🚀

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👋 Hello @Love-syntacticSugar, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

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