site/content/en/docs/api_sdk/sdk/_index.md
CVAT SDK is a Python library. It provides you access to Python functions and objects that simplify server interaction and provide additional functionality like data validation and serialization.
SDK API includes several layers:
cvat_sdk.api_client.
{{< ilink "/docs/api_sdk/sdk/lowlevel-api" "Read more" >}}cvat_sdk.core.
{{< ilink "/docs/api_sdk/sdk/highlevel-api" "Read more" >}}cvat_sdk.pytorch.
{{< ilink "/docs/api_sdk/sdk/pytorch-adapter" "Read more" >}}cvat_sdk.auto_annotation.
{{< ilink "/docs/api_sdk/sdk/auto-annotation" "Read more" >}}cvat_sdk.attributes and cvat_sdk.masks.In general, the low-level API provides single-request operations, while the high-level one implements composite, multi-request operations, and provides local proxies for server objects. For most uses, the high-level API should be good enough, and it should be the right point to start your integration with CVAT.
The PyTorch adapter is a specialized layer
that represents datasets stored in CVAT as PyTorch Dataset objects.
This enables direct use of such datasets in PyTorch-based machine learning pipelines.
The auto-annotation API is a specialized layer
that lets you automatically annotate CVAT datasets
by running a custom function on the local machine.
See also the auto-annotate command in the CLI.
To install an official release of CVAT SDK use this command:
pip install cvat-sdk
To use the cvat_sdk.masks module, request the masks extra:
pip install "cvat-sdk[masks]"
To use the PyTorch adapter or the built-in PyTorch-based auto-annotation functions,
request the pytorch extra:
pip install "cvat-sdk[pytorch]"
We support Python versions 3.10 and higher.
To import package components, use the following code:
For the high-level API:
import cvat_sdk
# or
import cvat_sdk.core
For the low-level API:
import cvat_sdk.api_client
For the PyTorch adapter:
import cvat_sdk.pytorch