--- title: Export annotations and data from Label Studio short: Export annotations type: guide tier: all order: 166 order_enterprise: 166 meta_title: Export Annotations meta_description: Label Studio documentation for exporting data labeling annotations to use in machine learning models and data science projects. section: "Import & Export" --- At any point in your labeling project, you can export the annotations from Label Studio. Label Studio stores your annotations in a raw JSON format in the SQLite database backend, PostgreSQL database backend, or whichever cloud or database storage you specify as target storage. Cloud storage buckets contain one file per labeled task named `task_id.json`. For more information about syncing target storage, see [Cloud storage setup](storage.html). Image annotations exported in JSON format use percentages of overall image size, not pixels, to describe the size and location of the bounding boxes. For more information, see [how to convert the image annotation units](#Units-of-image-annotations). !!! note Some export formats export only the annotations and not the data from the task. For more information, see the [export formats supported by Label Studio](#Export-formats-supported-by-Label-Studio). {% insertmd includes/annotation_ids.md %}
### Export using the UI in Community Edition of Label Studio Use the following steps to export data and annotations from the Label Studio UI. 1. For a project, click **Export**. 2. Select an available export format. 3. Click **Export** to export your data. !!! note 1. The export will always include the annotated tasks, regardless of filters set on the tab. 2. Cancelled annotated tasks will be included in the exported result too. 3. If you want to apply tab filters to the export, try to use [export snapshots using the SDK](https://labelstud.io/sdk/project.html#label_studio_sdk.project.Project.export_snapshot_create) or [API](#Export-snapshots-using-the-Snapshot-API). ### Export timeout in Community Edition If the export times out, see how to [export snapshots using the SDK](https://labelstud.io/sdk/project.html#label_studio_sdk.project.Project.export_snapshot_create) or [API](#Export-snapshots-using-the-Snapshot-API). You can also use a [console command](#Export-using-console-command) to export your project. For more information, see the following section. ### Export using console command Use the following command to export data and annotations. ```shell label-studio export --export-path= ``` To enable logs: ```shell DEBUG=1 LOG_LEVEL=DEBUG label-studio export --export-path= ```
### Export snapshots using the UI
In Label Studio Enterprise, create a snapshot of your data and annotations. Create a snapshot to export exactly what you want from your data labeling project. This delayed export method makes it easier to export large labeling projects from the Label Studio UI. 1. Within a project in the Label Studio UI, click **Export**. 2. Click **Create New Snapshot**. 3. **Apply filters from tab ...**: Select **Default** from the drop-down list. 4. (Optional) **Snapshot Name**: Enter a snapshot name to make it easier to find in the future. By default, export snapshots are named `PROJECT-NAME-at-YEAR-MM-DD-HH-MM`, where the time is in UTC. 5. **Include in the Snapshot…**: Choose which type of data you want to include in the snapshot. Select **All tasks**, **Only annotated** tasks, or **Only reviewed** tasks. 6. **Drafts**: Choose whether to export the complete draft annotations (**Complete drafts**) for tasks, or only the IDs (**Only IDs**) of draft annotations, to indicate that drafts exist. 7. **Predictions**: Choose whether to export the complete predictions (**Complete predictions**) for tasks, or only the IDs (**Only IDs**) of predictions to indicate that the tasks had predictions. 8. **Annotations**: Enable the types of annotations that you want to export. You can specify **Annotations**, **Ground Truth** annotations, and **Skipped** annotations. By default, only annotations are exported. 9. (Optional) Enable the **Remove user details** option to remove the user's details. 10. Click **Create a Snapshot** to start the export process. 11. You see the list of snapshots available to download, with details about what is included in the snapshot, when it was created, and who created it. 12. Click **Download** and select the export format that you want to use. Now, the snapshot file downloads to your computer.
### Export using the Easy Export API You can call the Label Studio API to export annotations. For a small labeling project, call the [export endpoint](/api#operation/api_projects_export_read) to export annotations. #### Export all tasks including tasks without annotations Label Studio open source exports tasks with annotations only by default. If you want to easily export all tasks including tasks without annotations, you can call the [Easy Export API](https://api.labelstud.io/#operation/api_projects_export_read) with query param `download_all_tasks=true`. For example: ``` curl -X GET https://localhost:8080/api/projects/{id}/export?exportType=JSON&download_all_tasks=true ``` If your project is large, you can use a [snapshot export](https://api.labelstud.io/#operation/api_projects_exports_create) (or [snapshot SDK](https://labelstud.io/sdk/project.html#create-new-export-snapshot)) to avoid timeouts in most cases. Snapshots include all tasks without annotations by default. ### Export snapshots using the Snapshot API For a large labeling project with hundreds of thousands of tasks, do the following: 1. Make a POST request to [create a new export file or snapshot](/api#operation/api_projects_exports_create). The response includes an `id` for the created file. 2. [Check the status of the export file created](/api#operation/api_projects_exports_read) using the `id` as the `export_pk`. 3. Using the `id` from the created snapshot as the export primary key, or `export_pk`, make a GET request to [download the export file](/api#operation/api_projects_exports_download_read). ## Export formats supported by Label Studio Label Studio supports many common and standard formats for exporting completed labeling tasks. If you don't see a format that works for you, you can contribute one. For more information, see the [GitHub repository for the Label Studio Converter tool](https://github.com/HumanSignal/label-studio-converter). ### ASR_MANIFEST Export audio transcription labels for automatic speech recognition as the JSON manifest format expected by [NVIDIA NeMo models](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/core/core.html). Supports audio transcription labeling projects that use the `Audio` tag with the `TextArea` tag. ```json {“audio_filepath”: “/path/to/audio.wav”, “text”: “the transcription”, “offset”: 301.75, “duration”: 0.82, “utt”: “utterance_id”, “ctm_utt”: “en_4156”, “side”: “A”} ``` ### Brush labels to NumPy and PNG Export your brush mask labels as NumPy 2d arrays and PNG images. Each label outputs as one image. Supports brush labeling image projects that use the `BrushLabels` tag. ### COCO A popular machine learning format used by the [COCO dataset](http://cocodataset.org/#home) for object detection and image segmentation tasks. Supports bounding box and polygon image labeling projects that use the `BrushLabels`, `RectangleLabels`, `KeyPointLabels` (see note below), or `PolygonLabels` tags. {% details KeyPointLabels Export Support %} If using `KeyPointLabels`, you will need to add the following to your labeling config: * At least one `` option. You will use this as a parent bounding box for the keypoints. * Add a `model_index` to every `