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Update neural-magic.md (#7347)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Abirami Vina <abirami.vina@gmail.com>
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docs/en/yolov5/tutorials/train_custom_data.md
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@ -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.