From 2756fba0ef78c9e4c3467c0d1e76898dcc2cfb0f Mon Sep 17 00:00:00 2001 From: Vinta Chen Date: Sun, 27 Sep 2026 14:38:06 +0800 Subject: [PATCH] docs: name Ultralytics' license by family in computer-vision intro The intro named Ultralytics' license with its version, AGPL-3.0, against the rule that intros name a license by family only. Co-Authored-By: Claude --- website/data/category_intros/computer-vision.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/data/category_intros/computer-vision.md b/website/data/category_intros/computer-vision.md index 26e1e35b..67ab8e2d 100644 --- a/website/data/category_intros/computer-vision.md +++ b/website/data/category_intros/computer-vision.md @@ -11,7 +11,7 @@ How to choose: OpenCV comes as [four pip packages that share the `cv2` namespace](https://github.com/opencv/opencv-python), so install only one: opencv-python for the main modules, or opencv-contrib-python to add the extra modules. If you never call `cv2.imshow` or you build your GUI with another toolkit, install the headless variant of either one, which also makes Docker images smaller. -Ultralytics YOLO covers the [whole life of a model](https://docs.ultralytics.com/modes/): train, validate, predict, export, and track, from Python or the `yolo` command. Its docs recommend [starting training from a pretrained model](https://docs.ultralytics.com/modes/train/). To deploy, [export it](https://docs.ultralytics.com/modes/export/) to ONNX, TensorRT, CoreML, or another format. The code and the models you train with it are [AGPL-3.0](https://www.ultralytics.com/license), so unless you open-source your whole project, you need an Enterprise License. +Ultralytics YOLO covers the [whole life of a model](https://docs.ultralytics.com/modes/): train, validate, predict, export, and track, from Python or the `yolo` command. Its docs recommend [starting training from a pretrained model](https://docs.ultralytics.com/modes/train/). To deploy, [export it](https://docs.ultralytics.com/modes/export/) to ONNX, TensorRT, CoreML, or another format. The code and the models you train with it are [AGPL](https://www.ultralytics.com/license), so unless you open-source your whole project, you need an Enterprise License. Kornia is a [differentiable computer vision library like OpenCV, with strong GPU support](https://kornia.readthedocs.io/en/latest/get-started/introduction.html). Every operator works on PyTorch tensors and supports autograd, so vision ops can run on the GPU and sit inside your training loop.