> 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/train-your-own-model/defining-your-computer-vision-model.md).

# Defining Your Computer Vision Model

Pick a model type and scope the problem it solves

EyePop.ai offers three model types. **Find Objects** is available today; the other two are marked *Coming soon* in the console.

* **Find Objects** (detection)
  * Locates and identifies objects in an image. Example: Detecting eyeglasses in photos.
* **Auto Tag Images** (classification) — *Coming soon*
  * Categorizes images into defined labels. Example: Identifying whether an image shows a dog or a cat.
* **Find Outlines** (segmentation) — *Coming soon*
  * 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 the Find Objects model type.