Update `neural-magic.md` (#7347)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Abirami Vina <abirami.vina@gmail.com>

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.
 

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 you’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:
 

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 🚀 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
 

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
 <details open>
 <summary>Use <a href="https://roboflow.com/?ref=ultralytics">Roboflow</a> to create your dataset in YOLO format 🌟</summary>
 
-!!! 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.