From a9e832b7b15a4ac51210fd6713a9fbc55411f5a9 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sun, 26 Jan 2025 15:40:12 +0100 Subject: [PATCH] Fix YOLOv3 table (#18902) Signed-off-by: Glenn Jocher Co-authored-by: UltralyticsAssistant --- docs/en/models/yolov3.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/en/models/yolov3.md b/docs/en/models/yolov3.md index 39b4d5a5..d2d06031 100644 --- a/docs/en/models/yolov3.md +++ b/docs/en/models/yolov3.md @@ -28,11 +28,11 @@ YOLOv3 is designed specifically for object detection tasks. Ultralytics supports All three models support a comprehensive set of modes, ensuring versatility in various stages of [model deployment](https://www.ultralytics.com/glossary/model-deployment) and development. These modes include [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), providing users with a complete toolkit for effective object detection. -| Model Type | Pre-Trained Weights | Tasks Supported | Inference | Validation | Training | Export | -| -------------- | ------------------- | -------------------------------------- | -------------------------------------- | ---------- | -------- | ------ | --- | -| YOLOv3(u) | `yolov3u.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOv3-Tiny(u) | `yolov3-tinyu.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | -| YOLOv3u-SPP(u) | `yolov3-sppu.pt` | | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| Model Type | Pre-Trained Weights | Tasks Supported | Inference | Validation | Training | Export | +| -------------- | ------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ | +| YOLOv3(u) | `yolov3u.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOv3-Tiny(u) | `yolov3-tinyu.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | +| YOLOv3u-SPP(u) | `yolov3-sppu.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ | ## This table provides an at-a-glance view of the capabilities of each YOLOv3 variant, highlighting their versatility and suitability for various tasks and operational modes in object detection workflows.