From cdb36b6b663ece0e0269cfcb4bb8d4ace6d9a567 Mon Sep 17 00:00:00 2001 From: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com> Date: Thu, 19 Dec 2024 03:46:50 +0800 Subject: [PATCH] Fix incorrect `scale` description (#18303) Signed-off-by: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com> Co-authored-by: Glenn Jocher --- docs/en/guides/model-training-tips.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/en/guides/model-training-tips.md b/docs/en/guides/model-training-tips.md index b0eada0df0f..6ace0bc50ea 100644 --- a/docs/en/guides/model-training-tips.md +++ b/docs/en/guides/model-training-tips.md @@ -58,7 +58,7 @@ When it comes to YOLO11, you can easily implement subset training by using the ` Multiscale training is a technique that improves your model's ability to generalize by training it on images of varying sizes. Your model can learn to detect objects at different scales and distances and become more robust. -For example, when you train YOLO11, you can enable multiscale training by setting the `scale` parameter. This parameter adjusts the size of training images by a specified factor, simulating objects at different distances. For example, setting `scale=0.5` will reduce the image size by half, while `scale=2.0` will double it. Configuring this parameter allows your model to experience a variety of image scales and improve its detection capabilities across different object sizes and scenarios. +For example, when you train YOLO11, you can enable multiscale training by setting the `scale` parameter. This parameter adjusts the size of training images by a specified factor, simulating objects at different distances. For example, setting `scale=0.5` randomly zooms training images by a factor between 0.5 and 1.5 during training. Configuring this parameter allows your model to experience a variety of image scales and improve its detection capabilities across different object sizes and scenarios. ### Caching