From 4c571311f7a5c80b4ece4b201bec594516edde28 Mon Sep 17 00:00:00 2001 From: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com> Date: Mon, 30 Dec 2024 22:52:21 +0800 Subject: [PATCH] Update information about YOLOv6 pretrained weights (#18450) Signed-off-by: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com> Signed-off-by: Glenn Jocher Co-authored-by: UltralyticsAssistant Co-authored-by: Glenn Jocher --- docs/en/models/yolov6.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/en/models/yolov6.md b/docs/en/models/yolov6.md index c41b40c8..7200da21 100644 --- a/docs/en/models/yolov6.md +++ b/docs/en/models/yolov6.md @@ -40,7 +40,7 @@ This example provides simple YOLOv6 training and inference examples. For full do === "Python" - [PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models as well as configuration `*.yaml` files can be passed to the `YOLO()` class to create a model instance in python: + YOLOv6 `*.yaml` files can be passed to the `YOLO()` class to build the corresponding model in Python: ```python from ultralytics import YOLO @@ -74,13 +74,13 @@ This example provides simple YOLOv6 training and inference examples. For full do The YOLOv6 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](https://www.ultralytics.com/glossary/accuracy) requirements, making them versatile for a wide array of applications. -| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export | -| ---------- | ------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ | -| YOLOv6-N | `yolov6-n.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOv6-S | `yolov6-s.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOv6-M | `yolov6-m.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOv6-L | `yolov6-l.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOv6-L6 | `yolov6-l6.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| Model | Filenames | Tasks | Inference | Validation | Training | Export | +| -------- | -------------- | -------------------------------------- | --------- | ---------- | -------- | ------ | +| YOLOv6-N | `yolov6n.yaml` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOv6-S | `yolov6s.yaml` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOv6-M | `yolov6m.yaml` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOv6-L | `yolov6l.yaml` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOv6-X | `yolov6x.yaml` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | This table provides a detailed overview of the YOLOv6 model variants, highlighting their capabilities in [object detection](https://www.ultralytics.com/glossary/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 YOLOv6 models in a broad range of object detection scenarios.