From 842f88dc842687fbb41eafbd0bcd1f4d99072cdb Mon Sep 17 00:00:00 2001 From: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com> Date: Mon, 30 Dec 2024 22:33:11 +0800 Subject: [PATCH] Add missing `.pt` extension to filenames (#18456) Signed-off-by: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com> Co-authored-by: UltralyticsAssistant Co-authored-by: Glenn Jocher --- docs/en/models/yolov9.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/en/models/yolov9.md b/docs/en/models/yolov9.md index b3812aed..ca7f4ac0 100644 --- a/docs/en/models/yolov9.md +++ b/docs/en/models/yolov9.md @@ -169,10 +169,10 @@ This example provides simple YOLOv9 training and inference examples. For full do The YOLOv9 series offers a range of models, each optimized for high-performance [Object Detection](../tasks/detect.md). These models cater to varying computational needs and accuracy requirements, making them versatile for a wide array of applications. -| Model | Filenames | Tasks | Inference | Validation | Training | Export | -| ---------- | ------------------------------------------------------- | -------------------------------------------- | --------- | ---------- | -------- | ------ | -| YOLOv9 | `yolov9t` `yolov9s` `yolov9m` `yolov9c.pt` `yolov9e.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOv9-seg | `yolov9c-seg.pt` `yolov9e-seg.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | +| Model | Filenames | Tasks | Inference | Validation | Training | Export | +| ---------- | ---------------------------------------------------------------- | -------------------------------------------- | --------- | ---------- | -------- | ------ | +| YOLOv9 | `yolov9t.pt` `yolov9s.pt` `yolov9m.pt` `yolov9c.pt` `yolov9e.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOv9-seg | `yolov9c-seg.pt` `yolov9e-seg.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ✅ | ✅ | ✅ | This table provides a detailed overview of the YOLOv9 model variants, highlighting their capabilities in object detection tasks and their compatibility with various operational modes such as [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md). This comprehensive support ensures that users can fully leverage the capabilities of YOLOv9 models in a broad range of object detection scenarios.