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👋 Hello @Brayan532, thank you for reaching out to the Ultralytics community with your question about YOLO11n 🚀! We recommend visiting the Docs if you're new here. It’s filled with Python and CLI examples that might address similar queries. If this is a custom training ❓ Question, as it appears to be, please provide more details about your datasets, including sample images and any training logs you have so far. It's crucial to follow our Tips for Best Training Results to optimize your training process. Since reproducibility is key, ensuring you're using the latest version might resolve some underlying issues. Upgrade the pip install -U ultralytics If you want to engage with the community for real-time advice, feel free to join us on Discord 🎧, explore in-depth Discourse discussions, or participate in our Subreddit threads. Rest assured, this is an automated response, but an Ultralytics engineer will be with you soon to provide further assistance. Thank you for your patience and understanding! 😊 |
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@Brayan532 to achieve a mAP50-95 of 39.5% with YOLO11n starting from random weights, it's recommended to use a sufficiently large and well-labeled dataset, typically around 1500 images per class and 10000 instances per class. For epochs, start with 300 and adjust based on validation results. For more details, you can refer to the YOLO11 documentation. |
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You can follow this. |
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Hi There,
I would like to know at least how large should be the train and validation dataset (coco) to get to (Map50-95) 39.5% by YOLO11n **starting with random weights ***. Moreover, how many epoches should be considered in this setting, at least?
Best wishes.
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