> For the complete documentation index, see [llms.txt](https://docs.eyepop.ai/developer-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.eyepop.ai/developer-documentation/self-service-training/how-to-train-a-model/defining-your-computer-vision-model.md).

# Defining Your Computer Vision Model

### EyePop.ai supports three model types:

* **Detection**
  * Locates and identifies objects in an image. Example: Detecting eyeglasses in photos. 
* **Classification**
  * Categorizes images into defined labels. Example: Identifying whether an image shows a dog or a cat.
* **Segmentation**
  * Outlines and isolates objects from backgrounds. Example: Determining the precise area taken up by a product.

When choosing and preparing your model, it’s important to clearly define the problem you aim to solve and ensure it’s well-scoped for the model type chosen. Are you detecting an object, condition, or feature? What are your specific attributes of interest (i.e. size, position, or appearance)? In the example use case that follows, the problem we aim to solve is identifying images of glasses in various contexts, which we accomplish using a Detection model type. 


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## 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, and the optional `goal` query parameter:

```
GET https://docs.eyepop.ai/developer-documentation/self-service-training/how-to-train-a-model/defining-your-computer-vision-model.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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.