> For the complete documentation index, see [llms.txt](https://docs.imerit.net/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.imerit.net/3d-multi-sensor-fusion/labeling/category-schema/classifications.md).

# Classifications

<figure><img src="https://3895963154-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FTcOUG6rfWxqGM0N4db2P%2Fuploads%2FNkuU4eOj8RNWF6FRj6S8%2Fimage.png?alt=media&amp;token=b68258a0-677c-48bf-89b4-6e174f20551c" alt=""><figcaption></figcaption></figure>

Each frame or sequence (aka task) can have properties called classifications that provide additional information about the overall frame or sequence, rather than individual objects. Classifications help capture specific characteristics, conditions, or states that apply broadly to the scene.

#### What Are Frame/Task Classifications?

Unlike object attributes (which describe individual annotations like cars or pedestrians), frame and sequence classifications describe:

* Frame-level: Properties of a single frame/timestamp
* Task-level: Properties of the entire sequence/batch

**Frame Classifications:**

* Weather conditions (sunny, rainy, foggy, snowy)
* Lighting conditions (day, night, dawn, dusk)
* Traffic density (light, moderate, heavy)
* Road surface (dry, wet, icy)
* Image quality (clear, blurry, overexposed)

**Sequence Classifications:**

* Location type (urban, suburban, highway, rural)
* Scenario type (normal driving, construction zone, parking lot)
* Recording conditions (stationary, moving vehicle)

Frame and task classifications use the same attribute types as object attributes:

* [Boolean](/3d-multi-sensor-fusion/labeling/category-schema/pct-class-level.md#boolean): Yes/no properties (e.g., "Is raining")
* [Single Select](/3d-multi-sensor-fusion/labeling/category-schema/pct-class-level.md#single-select-dropdown): One option from a list (e.g., Weather: Sunny/Cloudy/Rainy)
* [Multi-Select:](/3d-multi-sensor-fusion/labeling/category-schema/pct-class-level.md#multi-select-dropdown) Multiple options (e.g., Road features: Speed bumps, Potholes, Crosswalks)
* [Text:](/3d-multi-sensor-fusion/labeling/category-schema/pct-class-level.md#text) Free-form input (e.g., Session notes)


---

# 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 by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.imerit.net/3d-multi-sensor-fusion/labeling/category-schema/classifications.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.
