How to Configure Ultralytics YOLOv8 Training Parameters in Ultralytics HUB | Episode 55

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  • เผยแพร่เมื่อ 9 ก.ค. 2024
  • In this video, we provide a comprehensive overview of Ultralytics YOLOv8 training parameters. We'll cover image size, batch size, learning rate, weight decay, epochs, device selection, optimizers, and many other adjustable training parameters. All these configurations can be directly managed within the Ultralytics HUB, allowing users to fine-tune YOLOv8 models according to their specific needs. These configurations ensure you can optimize your models for maximum performance. Check out the video highlights below for a detailed breakdown of each section.
    Learn more ➡️ docs.ultralytics.com/hub/mode...
    Key Moments 😍
    0:00 - Introduction
    0:28 - Overview of Ultralytics HUB
    0:40 - Exploring HUB Datasets
    0:54 - Wildlife Dataset Overview
    1:04 - Training YOLOv8 on Wildlife Dataset via HUB
    1:49 - Advanced Model Configuration in Ultralytics HUB
    2:27 - Importance of Epochs in Model Training
    3:39 - Configuring Image Size and Patience in HUB
    4:14 - Setting Cache, Device, and Batch Size in HUB
    6:00 - Adding Custom Arguments in Model Training via HUB
    9:07 - Summary and Conclusion
    Ultralytics ⚡ resources
    - About Us - ultralytics.com/about
    - Join Our Team - ultralytics.com/work
    - Contact Us - ultralytics.com/contact
    - Discord - / discord
    - Ultralytics License - ultralytics.com/license
    YOLOv8 🚀 resources
    - GitHub - github.com/ultralytics/ultral...
    - Docs - docs.ultralytics.com/
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ความคิดเห็น • 4

  • @brunognoato8016
    @brunognoato8016 28 วันที่ผ่านมา +2

    Wow Is amazing 😍

    • @Ultralytics
      @Ultralytics  27 วันที่ผ่านมา

      Thank you!

  • @m033372
    @m033372 15 ชั่วโมงที่ผ่านมา

    Great video! Just curious, what's the biggest mistake you've seen people make when configuring YOLOv8 training parameters? Any funny or disastrous stories to share?

    • @Ultralytics
      @Ultralytics  ชั่วโมงที่ผ่านมา

      Thank you for the kind words! 😊 One common mistake is not adjusting the batch size to fit the GPU memory, which can lead to out-of-memory errors. It's always a good idea to start with a smaller batch size and gradually increase it. For more tips on configuring training parameters, check out our documentation: docs.ultralytics.com/guides/model-training-tips/. If you have any specific issues or need further assistance, feel free to share more details!