From 2756fba0ef78c9e4c3467c0d1e76898dcc2cfb0f Mon Sep 17 00:00:00 2001 From: Vinta Chen Date: Sun, 27 Sep 2026 14:38:06 +0800 Subject: [PATCH 1/2] 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. From 7a841f2e681305f96b9e6d96f0d8f4af789bcf3f Mon Sep 17 00:00:00 2001 From: Vinta Chen Date: Sun, 27 Sep 2026 14:38:10 +0800 Subject: [PATCH 2/2] docs: fix duplicate rembg link in image-processing intro The rembg paragraph linked the same GitHub URL twice; the model-license link now points at the README's models section, where that claim is stated. Co-Authored-By: Claude --- website/data/category_intros/image-processing.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/data/category_intros/image-processing.md b/website/data/category_intros/image-processing.md index 0a7bbcdb..32fa4ce9 100644 --- a/website/data/category_intros/image-processing.md +++ b/website/data/category_intros/image-processing.md @@ -19,7 +19,7 @@ pyvips builds a pipeline of operations and runs it only when you write the resul Wand is a [ctypes-based ImageMagick binding](https://docs.wand-py.org/en/latest/), so install ImageMagick's MagickWand library first. Its objects are resources like open files: [use them in a `with` block](https://docs.wand-py.org/en/latest/guide/resource.html) so they get closed. Wand's docs say to [never use Wand directly in an HTTP service](https://docs.wand-py.org/en/latest/guide/security.html) or on any public server. Hand the images to a background worker through a queue, and limit ImageMagick's resources and formats in its `policy.xml`. -rembg runs as a [CLI, a Python library, an HTTP server, or a Docker container](https://github.com/danielgatis/rembg). In code, create a session once with `new_session()` and pass it to each `remove()` call, since `remove` otherwise [starts a new session every call](https://github.com/danielgatis/rembg/blob/main/USAGE.md). The model weights [carry their own licenses](https://github.com/danielgatis/rembg), separate from rembg's MIT license, so check the one you use before you ship it in a commercial product. +rembg runs as a [CLI, a Python library, an HTTP server, or a Docker container](https://github.com/danielgatis/rembg). In code, create a session once with `new_session()` and pass it to each `remove()` call, since `remove` otherwise [starts a new session every call](https://github.com/danielgatis/rembg/blob/main/USAGE.md). The model weights [carry their own licenses](https://github.com/danielgatis/rembg#models), separate from rembg's MIT license, so check the one you use before you ship it in a commercial product. thumbor is an HTTP server: you [set the size and crop in the image URL](https://github.com/thumbor/thumbor), and it [detects faces and important features](https://thumbor.readthedocs.io/en/latest/) to crop around them. Set a `SECURITY_KEY` so [every URL is signed](https://thumbor.readthedocs.io/en/latest/security.html) and nobody can tamper with it, and build those URLs in Python with [libthumbor](https://thumbor.readthedocs.io/en/latest/libraries.html). In production, [turn off `ALLOW_UNSAFE_URL`](https://thumbor.readthedocs.io/en/latest/configuration.html) and run [more than one instance](https://thumbor.readthedocs.io/en/latest/hosting.html) behind a load balancer.