mirror of
https://github.com/THU-MIG/yolov10.git
synced 2025-05-23 21:44:22 +08:00
Update neural-magic.md
(#7347)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Abirami Vina <abirami.vina@gmail.com>
This commit is contained in:
parent
eeea5de623
commit
cd8957c098
@ -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:
|
||||
|
||||
|
@ -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
|
||||
|
||||
|
@ -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.
|
||||
|
Loading…
x
Reference in New Issue
Block a user