diff --git a/docs/en/integrations/neural-magic.md b/docs/en/integrations/neural-magic.md index 845121e3..90e90dbe 100644 --- a/docs/en/integrations/neural-magic.md +++ b/docs/en/integrations/neural-magic.md @@ -146,7 +146,7 @@ DeepSparse provides additional features for practical integration of YOLOv8 in a # For evaluating model performance on a dataset deepsparse.yolov8.eval --model_path "path/to/yolov8n.onnx" - ``` + ``` Running the annotate command processes your specified image, detecting objects, and saving the annotated image with bounding boxes and classifications. The annotated image will be stored in an annotation-results folder. This helps provide a visual representation of the model's detection capabilities. diff --git a/docs/en/integrations/roboflow.md b/docs/en/integrations/roboflow.md index 0af75eda..21838f91 100644 --- a/docs/en/integrations/roboflow.md +++ b/docs/en/integrations/roboflow.md @@ -8,9 +8,14 @@ keywords: Ultralytics, YOLOv8, Roboflow, vector analysis, confusion matrix, data [Roboflow](https://roboflow.com/?ref=ultralytics) has everything you need to build and deploy computer vision models. Connect Roboflow at any step in your pipeline with APIs and SDKs, or use the end-to-end interface to automate the entire process from image to inference. Whether youโ€™re in need of [data labeling](https://roboflow.com/annotate?ref=ultralytics), [model training](https://roboflow.com/train?ref=ultralytics), or [model deployment](https://roboflow.com/deploy?ref=ultralytics), Roboflow gives you building blocks to bring custom computer vision solutions to your project. -!!! Note +!!! Question "Licensing" - Ultralytics offers two licensing options: the [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE), an OSI-approved open-source license ideal for students and enthusiasts, and the [Enterprise License](https://ultralytics.com/license) for businesses seeking to incorporate our AI models into their products and services. For more details see [Ultralytics Licensing](https://ultralytics.com/license). + Ultralytics offers two licensing options: + + - The [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE), an OSI-approved open-source license ideal for students and enthusiasts. + - The [Enterprise License](https://ultralytics.com/license) for businesses seeking to incorporate our AI models into their products and services. + + For more details see [Ultralytics Licensing](https://ultralytics.com/license). In this guide, we are going to showcase how to find, label, and organize data for use in training a custom Ultralytics YOLOv8 model. Use the table of contents below to jump directly to a specific section: diff --git a/docs/en/yolov5/tutorials/roboflow_datasets_integration.md b/docs/en/yolov5/tutorials/roboflow_datasets_integration.md index a278ef26..40eef97d 100644 --- a/docs/en/yolov5/tutorials/roboflow_datasets_integration.md +++ b/docs/en/yolov5/tutorials/roboflow_datasets_integration.md @@ -8,9 +8,14 @@ keywords: Ultralytics, YOLOv5, Roboflow, data organization, data labelling, data You can now use Roboflow to organize, label, prepare, version, and host your datasets for training YOLOv5 ๐Ÿš€ models. Roboflow is free to use with YOLOv5 if you make your workspace public. -!!! Warning +!!! Question "Licensing" - Roboflow users can use Ultralytics under the [AGPL license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) or procure an [Enterprise license](https://ultralytics.com/license) directly from Ultralytics. Be aware that Roboflow does **not** provide Ultralytics licenses, and it is the responsibility of the user to ensure appropriate licensing. + Ultralytics offers two licensing options: + + - The [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE), an OSI-approved open-source license ideal for students and enthusiasts. + - The [Enterprise License](https://ultralytics.com/license) for businesses seeking to incorporate our AI models into their products and services. + + For more details see [Ultralytics Licensing](https://ultralytics.com/license). ## Upload diff --git a/docs/en/yolov5/tutorials/train_custom_data.md b/docs/en/yolov5/tutorials/train_custom_data.md index 7f19e9de..289e40ec 100644 --- a/docs/en/yolov5/tutorials/train_custom_data.md +++ b/docs/en/yolov5/tutorials/train_custom_data.md @@ -25,6 +25,15 @@ pip install -r requirements.txt # install Creating a custom model to detect your objects is an iterative process of collecting and organizing images, labeling your objects of interest, training a model, deploying it into the wild to make predictions, and then using that deployed model to collect examples of edge cases to repeat and improve. +!!! Question "Licensing" + + Ultralytics offers two licensing options: + + - The [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE), an OSI-approved open-source license ideal for students and enthusiasts. + - The [Enterprise License](https://ultralytics.com/license) for businesses seeking to incorporate our AI models into their products and services. + + For more details see [Ultralytics Licensing](https://ultralytics.com/license). + ### 1. Create Dataset YOLOv5 models must be trained on labelled data in order to learn classes of objects in that data. There are two options for creating your dataset before you start training: @@ -32,10 +41,6 @@ YOLOv5 models must be trained on labelled data in order to learn classes of obje
Use Roboflow to create your dataset in YOLO format ๐ŸŒŸ -!!! Warning - - Roboflow users can use Ultralytics under the [AGPL license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) or can request an [Enterprise license](https://ultralytics.com/license) directly from Ultralytics. Be aware that Roboflow does not provide Ultralytics licenses, and it is the responsibility of the user to ensure appropriate licensing. - ### 1.1 Collect Images Your model will learn by example. Training on images similar to the ones it will see in the wild is of the utmost importance. Ideally, you will collect a wide variety of images from the same configuration (camera, angle, lighting, etc.) as you will ultimately deploy your project.