# Train custom Neural Networks

## **Why train models in Supervisely**

* Get started in one click: don't waste dozens of hours figuring out how to install, configure and adapt another model from the Internet - we've done the hard part for you.
* Convenient user interface. With the simplified way we create interfaces using Supervisely Apps, we can add a GUI to make it easier to interact with the integrated code.
* Deployment on any computer. Run the auto-generated command in a terminal to launch Supervisely Agent and simply deploy new training tasks with a few clicks.
* We work together as a team. Because these models and tools now work online instead of on your personal computer, you can easily share and collaborate with your team members.
* Integration with other applications: a project converted into an application has access to the entire ecosystem: import from different formats, visualization, exploration and much more!

## **Some examples of the train dashboards:**

* [Train Detectron2](https://ecosystem.supervisely.com/apps/detectron2/supervisely/train)
* [Train MMSegmentation](https://ecosystem.supervisely.com/apps/mmsegmentation/train)
* [Train YOLOv8](https://ecosystem.supervisely.com/apps/yolov8/train)
* [Train MMClassification](https://ecosystem.supervisely.com/apps/mmclassification/supervisely/train)
* [Train UNet](https://ecosystem.supervisely.com/apps/unet/supervisely/train)

***

## **Step-by-step tutorials on training models in Supervisely:**

* [No-code tutorial: train and predict YOLOv8 on custom data](https://supervisely.com/blog/train-yolov8-on-custom-data-no-code/)
* [How to Train Smart Tool for Precise Cracks Segmentation in Industrial Inspection](https://supervisely.com/blog/industrial-inspection-cracks-segmentation/)
* [How to Train a Model with Only 62 Labeled Images using Semi-Supervised Learning](https://supervisely.com/blog/train-a-model-with-62-labeled-images-hrda-semi-supervised/)


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.supervisely.com/neural-networks/legacy/custom-nn.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
