> ## Documentation Index
> Fetch the complete documentation index at: https://landinglens.docs.landing.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Project Types

export const vp = 'Visual Prompting';

export const smartLabel = 'Smart Labeling';

export const productLL = 'LandingLens';

export const mi = 'Mobile Inference';

export const llsf = 'LandingLens on Snowflake';

export const companyName = 'LandingAI';

*This article applies to these versions of LandingLens:*

<table>
  <thead>
    <tr>
      <th>LandingLens</th>
      <th>LandingLens on Snowflake</th>
    </tr>
  </thead>

  <tbody>
    <tr>
      <td><span class="check-icon">✓</span></td>
      <td><span class="check-icon">✓</span> (see exceptions below)</td>
    </tr>
  </tbody>
</table>

<iframe width="560" height="315" src="https://www.youtube.com/embed/YDBA4vW01ao?&wmode=opaque&rel=0" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen />

LandingLens offers these project types:

* Object Detection
* Classification
* Segmentation
* Anomaly Detection
* {vp} (not available in {llsf})

<img src="https://mintcdn.com/landinglens/H_D2pfx5r704rYro/images/ProjectTypes.png?fit=max&auto=format&n=H_D2pfx5r704rYro&q=85&s=fc97104427032306ad0c620cdf2ca778" alt="ProjectTypes" width="2804" height="520" data-path="images/ProjectTypes.png" />

## Object Detection

Use to identify one or more objects in an image. Object Detection trains based on the labeled pixels (pixels inside the bounding box).

For more information, go to [Object Detection](./object-detection).

## Classification

Use to categorize (or "classify") the content of an image. Classification identifies the image as a whole. Classification trains based on all pixels in an image.

For more information, go to [Classification](./classification).

## Segmentation

Use to specify exact pixels to identify one or more regions within an image.

For more information, go to [Segmentation](./segmentation).

## Anomaly Detection

Use to identify deviations from the norm, especially when you have few or no images of "abnormal" cases.

For more information, go to [Anomaly Detection](./anomaly-detection).

## Visual Prompting

Use to identify objects or areas in an image. You only need to label a few small areas for the model to detect the whole object or area.

For more information, go to [Visual Prompting](./visual-prompting).

<Info>{vp} is not available in {llsf}.</Info>

<Info>Visual Prompting is in Beta. It might not function as well as our other production-ready features.</Info>

## When to Use Each Project Type

Each Project Type is also designed for different use cases:

| Project Type      | When to Use                                                                                                                                    | Examples                                                                                                                                                                                                                                |
| ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Object Detection  | <ul><li>You want to identify multiple objects in an image.</li><li>You want to identify one object in an image.</li></ul>                      | Identify multiple objects in an image.<ul><li>Scratches and missing parts on a laptop</li><li>Apples and bananas</li></ul>Identify one object in an image.<ul><li>Deer</li><li>Person</li><li>License plate frame</li></ul>             |
| Classification    | <ul><li>You want to identify all the content within an image.</li><li>You want to distinguish one object type from another.</li></ul>          | Identify all content within an image.<ul><li>Image of a city</li><li>Image of a basketball</li></ul>Distinguish one object type from another. <ul><li>Screws vs. nails</li><li>Cats vs. dogs</li></ul>                                  |
| Segmentation      | <ul><li>You need to be precise.</li><li>You want to identify one or more regions in an image.</li></ul>                                        | <ul><li>Identify cracks on computer monitors </li></ul>                                                                                                                                                                                 |
| Anomaly Detection | <ul><li>You want to identify deviations from the norm.</li><li>You have few or no images of "abnormal" cases.</li></ul>                        | <ul><li>Identify defects</li><li>Identify missing components on a product</li><li>Identify missing parts from a kit</li><li>Identifiy abnormalities in medical images</li></ul>                                                         |
| {vp}              | <ul><li>You want to create a model quickly. </li><li>You want to identify all regions in an image.</li><li>You are not using {llsf}.</li></ul> | Identify objects with distinct textures.<ul><li>Animals</li><li>Sand vs. rocks on a beach</li></ul>Identify objects that have irregular shapes.<ul><li>Regions of forests in satellite imagery</li><li>Scratches on a product</li></ul> |