Manual

• 1: Basics
• 1.1: Registration
• 1.2: Create annotation task
• 1.3: Create multi tasks
• 1.4: Jobs page
• 1.5: Tasks page
• 1.6: Task details
• 1.7: Interface of the annotation tool
• 1.8: Basic navigation
• 1.9: Top Panel
• 1.10: Controls sidebar
• 1.11: Objects sidebar
• 1.12: CVAT Workspace
• 1.13: 3D task workspace
• 1.14: Standard 3D mode (basics)
• 1.15: Settings
• 1.16: Types of shapes
• 1.17: Shape mode (basics)
• 1.18: Track mode (basics)
• 1.19: 3D Object annotation
• 1.20: Attribute annotation mode (basics)
• 1.21: Vocabulary
• 1.22: Cloud storages page
• 1.23: Attach cloud storage
• 2: Advanced
• 2.1: Projects page
• 2.2: Organization
• 2.3: Search
• 2.4: Shape mode (advanced)
• 2.5: Track mode (advanced)
• 2.6: 3D Object annotation (advanced)
• 2.7: Attribute annotation mode (advanced)
• 2.8: Annotation with rectangles
• 2.9: Annotation with polygons
• 2.9.1: Manual drawing
• 2.9.2: Drawing using automatic borders
• 2.9.3: Edit polygon
• 2.9.4: Track mode with polygons
• 2.9.5: Creating masks
• 2.10: Annotation with polylines
• 2.11: Annotation with points
• 2.11.1: Points in shape mode
• 2.11.2: Linear interpolation with one point
• 2.12: Annotation with ellipses
• 2.13: Annotation with cuboids
• 2.13.1: Creating the cuboid
• 2.13.2: Editing the cuboid
• 2.14: Annotation with skeletons
• 2.14.1: Creating the skeleton
• 2.14.2: Editing the skeleton
• 2.15: Annotation with brush tool
• 2.16: Annotation with tags
• 2.17: Models
• 2.18: CVAT Analytics and quality assessment in Cloud
• 2.18.1: Annotation quality & Honeypot
• 2.18.2: CVAT Performance & Monitoring
• 2.19: OpenCV and AI Tools
• 2.20: Automatic annotation
• 2.21: Specification for annotators
• 2.22: Backup Task and Project
• 2.23: Frame deleting
• 2.24: Import datasets and upload annotation
• 2.25: Export annotations and data from CVAT
• 2.25.1:
• 2.25.2:
• 2.25.3:
• 2.25.4:
• 2.25.5:
• 2.25.6:
• 2.25.7:
• 2.25.8:
• 2.25.9:
• 2.25.10:
• 2.25.11:
• 2.25.12:
• 2.25.13:
• 2.25.14:
• 2.25.15:
• 2.25.16:
• 2.25.17:
• 2.25.18:
• 2.25.19:
• 2.25.20:
• 2.25.21:
• 2.26: XML annotation format
• 2.27: Shortcuts
• 2.28: Filter
• 2.29: Review
• 2.30: Contextual images
• 2.31: Shape grouping
• 2.32: Dataset Manifest
• 2.33: Data preparation on the fly
• 2.34: Serverless tutorial

1 - Basics

1.1 - Registration

To start to annotate in CVAT, you need to create an account or log in to the existing account.

This section describes App CVAT, that is suitable for small personal projects, that do not require user management. It is also ok to use if you just want to try what is CVAT.

While it is easy to use, it has some limitations. For example, in App CVAT you cannot create a superuser (admin account) or administer user roles. All these features are available for Admin user in local version of CVAT.

See:

• User registration
• Account access

To creata account or log in, go to the App CVAT login page:

Note: By default authentication and registration with Google and GitHub work only for App CVAT.
If you want to use Google and GitHub authentication on a local installation, see Social auth configuration.

User registration

To register as a non-admin user, do the following:

1. Click Create an account.

   

2. Fill in all blank fields, accept terms of use, and click the Create an account button.

A username generates from the email automatically. You can edit it if needed.

To register with Google or GitHub, click the button with the name of the service, and follow instructions on the screen.

Account access

To access your account, do the following:

1. Go to the login page.
2. Enter username or email. The password field will appear.
3. Enter the password and click Next.

To log in with Google or GitHub, click the button with the name of the service.

1.2 - Create annotation task

To start annotating in CVAT, you need to create an annotation task and specify its parameters.

To create a task, on the Tasks page click + and select Create new task.

See:

• Create a task
  • Label shape
  • Add an attribute
  • Select files
  • Editing labels in RAW format
  • Data formats for a 3D task
• Advanced configuration

Create a task

To create a new task, open task configurator:

And specify the following parameters:

1. In the Name field, enter the name of the new task.

   

2. (Optional) From the Projects drop-down, select a project for the new task.
   Leave this field empty if you do not want to assign the task to any project.

   

   Note: Following steps are valid if the task does not belong to a project.
   If the task has been assigned to a project, the project’s labels will be applied to the task.

3. On the Constructor tab, click Add label.
   The label constructor menu will open:

   

4. In the Label name field, enter the name of the label.

5. (Optional) To limit the use of the label to a certain shape tool, from the Label shape drop-down select the shape.

6. (Optional) Select the color for the label.

   

7. (Optional) Click Add an attribute and set up its properties.

8. Click Select files to upload files for annotation.

9. Click Continue to submit the label and start adding a new one
   or Cancel to terminate the current label and return you to the labels list.

10. Click Submit and open to submit the configuration and open the created task,
    or Submit and continue, to submit the configuration and start a new task.

Label shape

Labels (or classes) are categories of objects that you can annotate.

Label shape limits the use of the label to certain shape tool.

Any is the default setting that does not limit the use of the label to any particular shape tool.

For example, you added:

• Label sun with the Label shape type ellipse
• Label car with the Label shape type any

As a result:

• The sun label will be available only for ellipse shape.

• The car label will be available for all shapes.

  

The tools on the Controls sidebar will be limited to the selected types of shapes.

For example, if you select Any, all tools will be available, but if you select Rectangle for all labels, only the Rectangle tool will be visible on the sidebar.

Note: You cannot apply the Label shape to the AI and OpenCV tools, these tools will always be available.

You can change the shape of the label as needed. This change will not affect the existing annotation.

For example, if you created objects using polygons and then changed the label shape to polylines, all previously created objects will remain polygons. However, you will not be able to add new polygon objects with the same label.

Note: You cannot change the shape of the skeleton label.
The Label shape field for the skeleton label is disabled.

Add an attribute

Attribute is a property of an annotated object, such as color, model, or other quality.

For example, you have a label for face and want to specify the type of face. Instead of creating additional labels for male and female, you can use attributes to add this information.

There are two types of attributes:

• Immutable attributes are unique and do not change from frame to frame. For example, age, gender, and color.
• Mutable attributes are temporary and can change from frame to frame. For example, pose, quality, and truncated.

Added attributes will be available from the Objects menu:

To add an attribute, do the following:

1. Go to the Constructor tab and click Add attribute.

   

2. In the Name field enter the name of the attribute.

3. From the drop-down, select way to display the attribute in the Objects menu:

   • Select enables a drop-down list, from which you can select an attribute.
     If in the Attribute value field you add __undefined__, the drop-down list will have a blank value.
     This is useful for cases where the attribute of the object cannot be clarified:

   • 

   • Radio enables the selection of one option from several options.

   • Checkbox enables the selection of multiple options.

   • Text sets the attribute to a text field.

   • Number sets the attribute to numerical field in the following format: min;max;step.

4. In the Attribute values field, add attribute values.
   To separate values use Enter.
   To delete value, use Backspace or click x next to the value name.

5. (Optional) For mutable attributes, select Mutable.

6. (Optional) To set the default attribute, hover over it with mouse cursor and click on it. The default attribute will change color to blue.

To delete an attribute, click Delete attribute.

Select files

There are several ways to upload files:

Data source	Description
My computer	Use this option to select files from your laptop or PC. To select file: 1. Click on the Select files field: . 2. Select files to upload.
Connected file share	Advanced option. Upload files from a local or cloud shared folder. Note, that you need to mount a fileshare first. For more information, see Share path
Remote source	Enter a list of URLs (one per line) in the field.
Cloud Storage	Advanced option. To upload files from cloud storage, type the cloud storage name, choose the manifest file, and select the required files. For more information, see Attach cloud storage

Editing labels in RAW format

The Raw is a way of working with labels for an advanced user.

It is useful when you need to copy labels from one independent task to another.

Note: Be careful with changing the raw specification of an existing task/project. Removing any “id” properties will lead to losing existing annotations. This property will be removed automatically from any text you insert to this field.

Raw presents label data in .json format with an option of editing and copying labels as text. The Done button applies the changes and the Reset button cancels the changes.

Data formats for a 3D task

To create a 3D task, you must prepare an archive with one of the following directory structures.

Note: You can’t mix 2D and 3D data in the same task.

• Velodyne
• 3D pointcloud
• 3D Option 1
• 3D Option 2

  VELODYNE FORMAT
    Structure:
      velodyne_points/
        data/
          image_01.bin
          IMAGE_00 # unknown dirname,
                   # generally image_01.png can be under IMAGE_00, IMAGE_01, IMAGE_02, IMAGE_03, etc
      data/
        image_01.png

   3D POINTCLOUD DATA FORMAT
    Structure:
      pointcloud/
        00001.pcd
      related_images/
        00001_pcd/
          image_01.png # or any other image

    3D, DEFAULT DATAFORMAT Option 1
    Structure:
      data/
        image.pcd
        image.png

    3D, DEFAULT DATAFORMAT Option 2
    Structure:
      data/
        image_1/
            image_1.pcd
            context_1.png # or any other name
            context_2.jpg

Advanced configuration

Use advanced configuration to set additional parameters for the task and customize it to meet specific needs or requirements.

The following parameters are available:

To save and open the task, click Submit & Open .

To create several tasks in sequence, click Submit & Continue.

Created tasks will be displayed on the tasks page.

1.3 - Create multi tasks

Use Create multi tasks to create multiple video annotation tasks with the same configuration.

The Сreate multi tasks feature is available for videos only.

To create the multi tasks, on the Tasks page click + and select Create multi tasks.

See:

• Create multi tasks
• Example
• Errors
• Advanced configuration

Create multi tasks

To add several tasks in one go, open the task configurator:

And specify the following parameters:

1. In the Name field, enter the name of the new task:

   • Enter the name of the task. If the name includes more than one word, use the underscore: Word1 word2 word3
   • (Optional) {{index}} adds an index to the file in the set (starting from 0).
   • (Optional) {{file_name}} adds the file’s name to the task’s name.

     Note: use hyphen between three parameters: Word1 word2 word3 {{index}} {{file_name}}

2. (Optional) From the Projects drop-down, select a project for the tasks.
   Leave this field empty if you do not want to assign tasks to any project.

   

   Note: Following steps are valid if the tasks do not belong to a project.
   If the tasks have been assigned to a project, the project’s labels will be applied to the tasks.

3. On the Constructor tab, click Add label.

4. In the Label name field, enter the name of the label.

5. (Optional) Select the color for the label.

6. (Optional) Click Add an attribute and set up its properties.

7. Click Select files to upload files for annotation.

   Note: You cannot upload multiple tasks from the cloud storage.

8. Click Submit N tasks

Example

A step-by-step example for creating the multiple tasks:

1. In the Name field, enter the Create_multitask-{{index}}-{{file_name}}.

2. Add labels.

3. Select files.
   In case there are more than four files, only the total number of selected files will be displayed:

4. Click Submit N tasks

   

5. You will see a progress bar that shows the progress of the tasks being created:

   

6. Click Ok.

   

The result will look like the following:

Errors

During the process of adding multiple tasks, the following errors may occur:

Error	Description
	Wrong file format. You can add only video files.
	In the process of creating a task, CVAT was not able to process the video file. The name of the failed file will be displayed on the progress bar. To fix this issue: If you want to try again, click Retry failed tasks. If you want to skip the file, click OK.

Advanced configuration

Use advanced configuration to set additional parameters for the task and customize it to meet specific needs or requirements.

For more information, see Advanced configuration

1.4 - Jobs page

On the jobs page, users (for example, with the worker role) can see the jobs that are assigned to them without having access to the task page, as well as track progress, sort and apply filters to the job list.

On the job page there is a list of jobs presented in the form of tiles, where each tile is one job. Each element contains:

• job ID
• dimension 2D or 3D
• preview
• stage and state
• when hovering over an element, you can see:
  • size
  • assignee
• menu to navigate to a task, project, or bug tracker.

To open the job in a new tab, click on the job by holding Ctrl.

In the upper left corner there is a search bar, using which you can find the job by assignee, stage, state, etc. In the upper right corner there are sorting, quick filters and filter.

Filter

Applying filter disables the quick filter.

The filter works similarly to the filters for annotation, you can create rules from properties, operators and values and group rules into groups. For more details, see the filter section. Learn more about date and time selection.

For clear all filters press Clear filters.

Supported properties for jobs list

1.5 - Tasks page

The tasks page contains elements and each of them relates to a separate task. They are sorted in creation order. Each element contains: task name, preview, progress bar, button Open, and menu Actions. Each button is responsible for a in menu Actions specific function:

• Export task dataset — download annotations or annotations and images in a specific format. More information is available in the export/import datasets section.
• Upload annotation upload annotations in a specific format. More information is available in the export/import datasets section.
• Automatic Annotation — automatic annotation with OpenVINO toolkit. Presence depends on how you build the CVAT instance.
• Backup task — make a backup of this task into a zip archive. Read more in the backup section.
• Move to project — Moving a task to a project (you can move only a task which does not belong to any project). In case of label mismatch, you can create or delete necessary labels in the project/task. Some task labels can be matched with the target project labels.
• Delete — delete task.

In the upper left corner there is a search bar, using which you can find the task by assignee, task name etc. In the upper right corner there are sorting, quick filters and filter.

Filter

Applying filter disables the quick filter.

The filter works similarly to the filters for annotation, you can create rules from properties, operators and values and group rules into groups. For more details, see the filter section. Learn more about date and time selection.

For clear all filters press Clear filters.

Supported properties for tasks list

Push Open button to go to task details.

1.6 - Task details

Task details is a task page which contains a preview, a progress bar and the details of the task (specified when the task was created) and the jobs section.

• The next actions are available on this page:

  1. Change the task’s title.
  2. Open Actions menu.
  3. Change issue tracker or open issue tracker if it is specified.
  4. Change labels (available only if the task is not related to the project). You can add new labels or add attributes for the existing labels in the Raw mode or the Constructor mode. By clicking Copy you will copy the labels to the clipboard.
  5. Assigned to — is used to assign a task to a person. Start typing an assignee’s name and/or choose the right person out of the dropdown list. In the list of users, you will only see the users of the organization where the task is created.

• Jobs — is a list of all jobs for a particular task. Here you can find the next data:

  • Jobs name with a hyperlink to it.
  • Frames — the frame interval.
  • A stage of the job. The stage is specified by a drop-down list. There are three stages: annotation, validation or acceptance. This value affects the task progress bar.
  • A state of the job. The state can be changed by an assigned user in the menu inside the job. There are several possible states: new, in progress, rejected, completed.
  • Started on — start date of this job.
  • Duration — is the amount of time the job is being worked.
  • Assignee is the user who is working on the job. You can start typing an assignee’s name and/or choose the right person out of the dropdown list.
  • Reviewer – a user assigned to carry out the review, read more in the review section.
  • Copy. By clicking Copy you will copy the job list to the clipboard. The job list contains direct links to jobs.

  You can filter or sort jobs by status, as well as by assigner or reviewer.

Follow a link inside Jobs section to start annotation process. In some cases, you can have several links. It depends on size of your task and Overlap Size and Segment Size parameters. To improve UX, only the first chunk of several frames will be loaded and you will be able to annotate first images. Other frames will be loaded in background.

1.7 - Interface of the annotation tool

Main user interface

The tool consists of:

• Header - pinned header used to navigate CVAT sections and account settings;

• Top panel — contains navigation buttons, main functions and menu access;

• Workspace — space where images are shown;

• Controls sidebar — contains tools for navigating the image, zoom, creating shapes and editing tracks (merge, split, group);

• Objects sidebar — contains label filter, two lists: objects (on the frame) and labels (of objects on the frame) and appearance settings.

Pop-up messages

In CVAT, you’ll receive pop-up messages in the upper-right corner, on any page. Pop-up messages can contain useful information, links, or error messages.

Information message

Informational messages inform about the end of the auto-annotation process. Learn more about auto-annotation.

Jump Suggestion Messages

Open a task

After creating a task, you can immediately open it by clicking Open task. Learn more about creating a task.

Continue to the frame on which the work on the job is finished

When you open a job that you previously worked on, you will receive pop-up messages with a proposal to go to the frame that was visited before closing the tab.

Error Messages

If you perform impossible actions, you may receive an error message. The message may contain information about the error or a prompt to open the browser console (shortcut F12) for information. If you encounter a bug that you can’t solve yourself, you can create an issue on GitHub.

1.8 - Basic navigation

1. Use arrows below to move to the next/previous frame. Use the scroll bar slider to scroll through frames. Almost every button has a shortcut. To get a hint about a shortcut, just move your mouse pointer over an UI element.

   

2. To navigate the image, use the button on the controls sidebar. Another way an image can be moved/shifted is by holding the left mouse button inside an area without annotated objects. If the Mouse Wheel is pressed, then all annotated objects are ignored. Otherwise the a highlighted bounding box will be moved instead of the image itself.

   

3. You can use the button on the sidebar controls to zoom on a region of interest. Use the button Fit the image to fit the image in the workspace. You can also use the mouse wheel to scale the image (the image will be zoomed relatively to your current cursor position).

   

1.9 - Top Panel

Menu button

It is the main menu of the annotation tool. It can be used to download, upload and remove annotations.

Button assignment:

• Upload Annotations — uploads annotations into a task.

• Export as a dataset — download a data set from a task in one of the supported formats. You can also enter a Custom name and enable the Save images checkbox if you want the dataset to contain images.

• Remove Annotations — calls the confirmation window if you click Delete, the annotation of the current job will be removed, if you click Select range you can remove annotation on range frames, if you activate checkbox Delete only keyframe for tracks then only keyframes will be deleted from the tracks, on the selected range.

  

• Open the task — opens a page with details about the task.

• Change job state - changes the state of the job (new, in progress, rejected, completed).

• Finish the job/Renew the job - changes the job stage and state to acceptance and completed / annotation and new correspondingly.

Save Work

Saves annotations for the current job. The button has an indication of the saving process.

Undo-redo buttons

Use buttons to undo actions or redo them.

Done

Used to complete the creation of the object. This button appears only when the object is being created.

Block

Used to pause automatic line creation when drawing a polygon with OpenCV Intelligent scissors. Also used to postpone server requests when creating an object using AI Tools. When blocking is activated, the button turns blue.

Player

Go to the first /the latest frames.

Go to the next/previous frame with a predefined step. Shortcuts: V — step backward, C — step forward. By default the step is 10 frames (change at Account Menu —> Settings —> Player Step).

The button to go to the next / previous frame has the customization possibility. To customize, right-click on the button and select one of three options:

1. The default option - go to the next / previous frame (the step is 1 frame).
2. Go to the next / previous frame that has any objects (in particular filtered). Read the filter section to know the details how to use it.
3. Go to the next / previous frame without annotation at all. Use this option in cases when you need to find missed frames quickly.

Shortcuts: D - previous, F - next.

Play the sequence of frames or the set of images. Shortcut: Space (change at Account Menu —> Settings —> Player Speed).

Go to a specific frame. Press ~ to focus on the element.

To delete frame.

Shortcut: Alt+Del

Fullscreen Player

The fullscreen player mode. The keyboard shortcut is F11.

Info

Open the job info.

Overview:

• Assignee - the one to whom the job is assigned.
• Reviewer – a user assigned to carry out the review, read more in the review section.
• Start Frame - the number of the first frame in this job.
• End Frame - the number of the last frame in this job.
• Frames - the total number of all frames in the job.

Annotations statistics:

This is a table number of created shapes, sorted by labels (e.g. vehicle, person) and type of annotation (shape, track). As well as the number of manual and interpolated frames.

UI switcher

Switching between user interface modes.

1.10 - Controls sidebar

Navigation

Navigation block - contains tools for moving and rotating images.

Icon	Description
	Cursor (Esc)- a basic annotation pedacting tool.
	Move the image- a tool for moving around the image without the possibility of editing.
	Rotate- two buttons to rotate the current frame a clockwise (Ctrl+R) and anticlockwise (Ctrl+Shift+R). You can enable Rotate all images in the settings to rotate all the images in the job

Zoom

Zoom block - contains tools for image zoom.

Icon	Description
	Fit image- fits image into the workspace size. Shortcut - double click on an image
	Select a region of interest- zooms in on a selected region. You can use this tool to quickly zoom in on a specific part of the frame.

Shapes

Shapes block - contains all the tools for creating shapes.

Icon	Description	Links to section
	AI Tools	AI Tools
	OpenCV	OpenCV
	Rectangle	Shape mode; Track mode; Drawing by 4 points
	Polygon	Annotation with polygons; Track mode with polygons
	Polyline	Annotation with polylines
	Points	Annotation with points
	Ellipses	Annotation with ellipses
	Cuboid	Annotation with cuboids
	Brushing tools	Annotation with brushing
	Tag	Annotation with tags
	Open an issue	Review (available only in review mode)

Edit

Edit block - contains tools for editing tracks and shapes.

Icon	Description	Links to section
	Merge Shapes(M) - starts/stops the merging shapes mode.	Track mode (basics)
	Group Shapes (G) - starts/stops the grouping shapes mode.	Shape grouping
	Split - splits a track.	Track mode (advanced)

1.11 - Objects sidebar

In the objects sidebar, you can see the list of available objects on the current frame. The following figure is an example of how the list might look like:

Shape mode	Track mode

Objects properties

Filter input box

The way how to use filters is described in the advanced guide here.

List of objects

• Switch lock property for all - switches lock property of all objects in the frame.
• Switch hidden property for all - switches hide the property of all objects in the frame.
• Expand/collapse all - collapses/expands the details field of all objects in the frame.
• Sorting - sort the list of objects: updated time, ID - accent, ID - descent

Objects on the sidebar

The type of shape can be changed by selecting the Label property. For instance, it can look like shown in the figure below:

Object action menu

The action menu calls up the button:

The action menu contains:

• Create object URL - puts a link to an object on the clipboard. After you open the link, this object will be filtered.

• Make a copy - copies an object. The keyboard shortcut is Ctrl + C > Ctrl + V.

• Propagate function copies the form to multiple frames and displays a dialog box where you can specify the number of copies or the frame to which you want to copy the object. The keyboard shortcut is Ctrl + B.
  There are two options available:

  • Propagate forward () creates a copy of the object on N subsequent frames at the same position.
  • Propagate backward () creates a copy of the object on N previous frames at the same position.

  

• To background - moves the object to the background. The keyboard shortcut - or _

• To foreground - moves the object to the foreground. The keyboard shortcut + or =

• Change instance color- choosing a color using the color picker (available only in instance mode).

  

• Remove - removes the object. The keyboard shortcut Del, Shift+Del.

A shape can be locked to prevent its modification or moving by an accident. Shortcut to lock an object: L.

A shape can be Occluded. Shortcut: Q. Such shapes have dashed boundaries.

You can change the way an object is displayed on a frame (show or hide).

Switch pinned property - when enabled, a shape cannot be moved by dragging or dropping.

**Tracker switcher **- enable/disable tracking for the object.

By clicking on the Details button you can collapse or expand the field with all the attributes of the object.

Labels

In this tab, you can lock or hide objects of a certain label. To change the color for a specific label, you need to go to the task page and select the color by clicking the edit button, this way you will change the label color for all jobs in the task.

Fast label change

You can change the label of an object using hotkeys. In order to do it, you need to assign a number (from 0 to 9) to labels. By default numbers 1,2…0 are assigned to the first ten labels. To assign a number, click on the button placed at the right of a label name on the sidebar.

After that, you will be able to assign a corresponding label to an object by hovering your mouse cursor over it and pressing Ctrl + Num(0..9).

In case you do not point the cursor to the object, pressing Ctrl + Num(0..9) will set a chosen label as default, so that the next object you create (use the N key) will automatically have this label assigned.

Appearance

Color By options

Change the color scheme of the annotation:

• Instance — every shape has a random color

  

• Group — every group of shape has its own random color, ungrouped shapes are white

  

• Label — every label (e.g. car, person) has its own random color

  

  You can change any random color pointing to a needed box on a frame or on an object sidebar.

Fill Opacity slider

Change the opacity of every shape in the annotation.

Selected Fill Opacity slider

Change the opacity of the selected object’s fill. It is possible to change the opacity while drawing an object in the case of rectangles, polygons, and cuboids.

Outlines borders checkbox

You can change a special shape border color by clicking on the Eyedropper icon.

Show bitmap checkbox

If enabled all shapes are displayed in white and the background is black.

Show projections checkbox

Enables/disables the display of auxiliary perspective lines. Only relevant for cuboids

Hide objects sidebar

Hide - the button hides the object’s sidebar.

1.12 - CVAT Workspace

In CVAT the workspace serves as the central hub where annotators interact with images, videos, and the various tools available to create high-quality annotations.

The workspace includes the following elements:

See:

• Image settings in CVAT
  • Adding grid overlay to image in CVAT
  • Changing color settings of image is CVAT
• Adding layers and Z-axis slider
• Interacting with Objects

Image settings in CVAT

The Image settings panel serves as a versatile tool for fine-tuning the visual aspects of your image. Whether you need to brighten the image, increase contrast, or make other adjustments, this panel is your go-to.

Additionally, the panel allows you to overlay a grid on the image for more precise annotation.

Note: Adjusting the image settings only alters how the pictures are displayed. The images themselves will remain unmodified and unchanged.

By default, the Image settings panel is not visible. To access it, click on the Arrow Up () icon located at the bottom of the workspace.

Adding grid overlay to image in CVAT

To add the grid to the image, do the following:

1. Open the Image Settings panel.
2. Locate and check the box that allows you to overlay a grid on the image.
3. Specify the grid cell size in square millimeters by entering the desired number in the Size field.
4. From the Color drop-down list, select the color of the grid.
5. Use the Opacity slider to change the transparency of the grid overlay.

Changing color settings of image in CVAT

To change the color setting of the image is CVAT, do the following:

1. Open the Image Settings panel.
2. Use the slider to change the color quality.

There are four color quality settings in CVAT:

Brightness increases and decreases the overall lightness of the image:

Contrast is the range of brightness, from lightest to darkest, in an image.

Saturation describes the intensity of the color.

Gamma correction can be used to control the overall brightness of an image

To reset the setting to default values, click Reset color settings

Adding layers and Z-axis slider

Z-axis Slider enables you to add annotation layers while hiding the layers positioned beyond.

You can also move between layers by moving the slider to the layer you need.

The slider becomes active when multiple Z-layers are present within a frame. Click + on the slider to add a new layer; upon pressing it, a new layer is automatically created and activated.

You can also relocate objects between layers using the + and - keys.

Interacting with Objects

The workspace is also equipped with the following features:

• Right-clicking an object opens the Object Card. This interface contains essential controls for modifying the object’s label and attributes, as well as providing access to an action menu.

  

• Right-clicking on a polygon point will open a menu, from which you can Delete point or Set start point.

  

1.13 - 3D task workspace

If the related_images folder contains any images, a context image will be available in the perspective window. The contextual image could be compared to 3D data and would help to identify the labels of marked objects.

Perspective – a main window for work with objects in a 3D task.

Projections - projections are tied to an object so that a cuboid is in the center and looks like a rectangle. Projections show only the selected object.

• Top – a projection of the view from above.
• Side – a projection of the left side of the object.
• Front - a frontal projection of the object.

1.14 - Standard 3D mode (basics)

Standard 3d mode - Designed to work with 3D data. The mode is automatically available if you add PCD or Kitty BIN format data when you create a task. read more

You can adjust the size of the projections, to do so, simply drag the boundary between the projections.

1.15 - Settings

To open the settings open the user menu in the header and select the settings item or press F2.

Settings have two tabs:

In tab Player you can:

• Control step of C and V shortcuts.
• Control speed of Space/Play button.
• Select canvas background color. You can choose a background color or enter manually (in RGB or HEX format).
• Reset zoom Show every image in full size or zoomed out like previous (it is enabled by default for interpolation mode and disabled for annotation mode).
• Rotate all images checkbox — switch the rotation of all frames or an individual frame.
• Smooth image checkbox — smooth image when zoom-in it.

  smoothed	pixelized

In tab Workspace you can:

• Enable auto save checkbox — turned off by default.

• Auto save interval (min) input box — 15 minutes by default.

• Show all interpolation tracks checkbox — shows hidden objects on the side panel for every interpolated object (turned off by default).

• Always show object details - show text for an object on the canvas not only when the object is activated:

  

• Content of a text - setup of the composition of the object details:

  • ID - object identifier.
  • Attributes - attributes of the object.
  • Label - object label.
  • Source- source of creating of objects MANUAL, AUTO or SEMI-AUTO.
  • Descriptions - description of attributes.

• Position of a text - text positioning mode selection:

  • Auto - the object details will be automatically placed where free space is.
  • Center - the object details will be embedded to a corresponding object if possible.

• Font size of a text - specifies the text size of the object details.

• Automatic bordering - enable automatic bordering for polygons and polylines during drawing/editing. For more information To find out more, go to the section annotation with polygons.

• Intelligent polygon cropping - activates intelligent cropping when editing the polygon (read more in the section edit polygon

• Show tags on frame - shows/hides frame tags on current frame

• Attribute annotation mode (AAM) zoom margin input box — defines margins (in px) for shape in the attribute annotation mode.

• Control points size — defines a size of any interactable points in the tool (polygon’s vertices, rectangle dragging points, etc.)

• Default number of points in polygon approximation With this setting, you can choose the default number of points in polygon. Works for serverless interactors and OpenCV scissors.

• Click Save to save settings (settings will be saved on the server and will not change after the page is refreshed). Click Cancel or press F2 to return to the annotation.

1.16 - Types of shapes

There are several shapes with which you can annotate your images:

• Rectangle or Bounding box
• Polygon
• Polyline
• Points
• Ellipse
• Cuboid
• Cuboid in 3d task
• Skeleton
• Tag

And there is how they all look like:

Tag - has no shape in the workspace, but is displayed in objects sidebar.

1.17 - Shape mode (basics)

Usage examples:

• Create new annotations for a set of images.
• Add/modify/delete objects for existing annotations.

1. You need to select Rectangle on the controls sidebar:

   

   Before you start, select the correct Label (should be specified by you when creating the task) and Drawing Method (by 2 points or by 4 points):

   

2. Creating a new annotation in Shape mode:

   • Create a separate Rectangle by clicking on Shape.

     

   • Choose the opposite points. Your first rectangle is ready!

     

   • To learn more about creating a rectangle read here.

   • It is possible to adjust boundaries and location of the rectangle using a mouse. Rectangle’s size is shown in the top right corner , you can check it by clicking on any point of the shape. You can also undo your actions using Ctrl+Z and redo them with Shift+Ctrl+Z or Ctrl+Y.

3. You can see the Object card in the objects sidebar or open it by right-clicking on the object. You can change the attributes in the details section. You can perform basic operations or delete an object by clicking on the action menu button.

   

4. The following figure is an example of a fully annotated frame with separate shapes.

   

   Read more in the section shape mode (advanced).

1.18 - Track mode (basics)

Usage examples:

• Create new annotations for a sequence of frames.
• Add/modify/delete objects for existing annotations.
• Edit tracks, merge several rectangles into one track.

1. Like in the Shape mode, you need to select a Rectangle on the sidebar, in the appearing form, select the desired Label and the Drawing method.

   

2. Creating a track for an object (look at the selected car as an example):

   • Create a Rectangle in Track mode by clicking on Track.

     

   • In Track mode the rectangle will be automatically interpolated on the next frames.

   • The cyclist starts moving on frame #2270. Let’s mark the frame as a key frame. You can press K for that or click the star button (see the screenshot below).

     

   • If the object starts to change its position, you need to modify the rectangle where it happens. It isn’t necessary to change the rectangle on each frame, simply update several keyframes and the frames between them will be interpolated automatically.

   • Let’s jump 30 frames forward and adjust the boundaries of the object. See an example below:

     

   • After that the rectangle of the object will be changed automatically on frames 2270 to 2300:

     

3. When the annotated object disappears or becomes too small, you need to finish the track. You have to choose Outside Property, shortcut O.

   

4. If the object isn’t visible on a couple of frames and then appears again, you can use the Merge feature to merge several individual tracks into one.

   

   • Create tracks for moments when the cyclist is visible:

     

   • Click Merge button or press key M and click on any rectangle of the first track and on any rectangle of the second track and so on:

     

   • Click Merge button or press M to apply changes.

     

   • The final annotated sequence of frames in Interpolation mode can look like the clip below:

     

     Read more in the section track mode (advanced).

1.19 - 3D Object annotation

Use the 3D Annotation tool for labeling 3D objects and scenes, such as vehicles, buildings, landscapes, and others.

See:

• Navigation
• Annotation with cuboids
  • Annotation with shapes
  • Tracking with cuboids

Navigation

The 3D annotation canvas looks like the following

Note: if you added contextual images to the dataset, the canvas will include them. For more information, see Contextual images

For information on the available tools, see Controls sidebar.

You can navigate, using the mouse, or navigation keys:

You can also use keyboard shortcuts to navigate:

Annotation with cuboids

There are two options available for 3D annotation:

• Shape: for tasks like object detection.
• Track: uses interpolation to predict the position of objects in subsequent frames. A unique ID will be assigned to each object and maintained throughout the sequence of images.

Annotation with shapes

To add a 3D shape, do the following:

1. On the objects pane, select Draw new cuboid > select the label from the drop-down list > Shape.

2. The cursor will be followed by a cuboid. Place the cuboid on the 3D scene.

3. Use projections to adjust the cuboid. Click and hold the left mouse button to edit the label shape on the projection.

4. (Optional) Move one of the four points to change the size of the cuboid.

5. (Optional) To rotate the cuboid, click on the middle point and then drag the cuboid up/down or to left/right.

   

Tracking with cuboids

To track with cuboids, do the following:

1. On the objects pane, select Draw new cuboid > select the label from the drop-down list > Track.

2. The cursor will be followed by a cuboid. Place the cuboid on the 3D scene.

3. Use projections to adjust the cuboid. Click and hold the left mouse button to edit the label shape on the projection.

4. (Optional) Move one of the four points to change the size of the cuboid.

5. (Optional) To rotate the cuboid, click on the middle point and then drag the cuboid up/down or to left/right.

6. Move several frames forward. You will see the cuboid you’ve added in frame 1. Adjust it, if needed.

7. Repeat to the last frame with the presence of the object you are tracking.

For more information about tracking, see Track mode

1.20 - Attribute annotation mode (basics)

• In this mode you can edit attributes with fast navigation between objects and frames using a keyboard. Open the drop-down list in the top panel and select Attribute annotation Mode.

  

• In this mode objects panel change to a special panel :

  

• The active attribute will be red. In this case it is gender . Look at the bottom side panel to see all possible shortcuts for changing the attribute. Press key 2 on your keyboard to assign a value (female) for the attribute or select from the drop-down list.

  

• Press Up Arrow/Down Arrow on your keyboard or click the buttons in the UI to go to the next/previous attribute. In this case, after pressing Down Arrow you will be able to edit the Age attribute.

  

• Use Right Arrow/Left Arrow keys to move to the previous/next image with annotation.

To see all the hot keys available in the attribute annotation mode, press F2. Read more in the section attribute annotation mode (advanced).

1.21 - Vocabulary

Label

Label is a type of an annotated object (e.g. person, car, vehicle, etc.)

Attribute

Attribute is a property of an annotated object (e.g. color, model, quality, etc.). There are two types of attributes:

Unique

Unique immutable and can’t be changed from frame to frame (e.g. age, gender, color, etc.)

Temporary

Temporary mutable and can be changed on any frame (e.g. quality, pose, truncated, etc.)

Track

Track is a set of shapes on different frames which corresponds to one object. Tracks are created in Track mode

Annotation

Annotation is a set of shapes and tracks. There are several types of annotations:

• Manual which is created by a person
• Semi-automatic which is created mainly automatically, but the user provides some data (e.g. interpolation)
• Automatic which is created automatically without a person in the loop

Approximation

Approximation allows you to reduce the number of points in the polygon. Can be used to reduce the annotation file and to facilitate editing polygons.

Trackable

Trackable object will be tracked automatically if the previous frame was a latest keyframe for the object. More details in the section trackers.

Mode

Interpolation

Mode for video annotation, which uses track objects. Only objects on keyframes are manually annotation, and intermediate frames are linearly interpolated.

Related sections:

• Track mode

Annotation

Mode for images annotation, which uses shape objects.

Related sections:

• Shape mode

Dimension

Depends on the task data type that is defined when the task is created.

2D

The data format of 2d tasks are images and videos. Related sections:

• Creating an annotation task

3D

The data format of 3d tasks is a cloud of points. Data formats for a 3D task

Related sections:

• 3D task workspace
• Standard 3D mode
• 3D Object annotation

State

State of the job. The state can be changed by an assigned user in the menu inside the job. There are several possible states: new, in progress, rejected, completed.

Stage

Stage of the job. The stage is specified with the drop-down list on the task page. There are three stages: annotation, validation or acceptance. This value affects the task progress bar.

Subset

A project can have subsets. Subsets are groups for tasks that make it easier to work with the dataset. It could be test, train, validation or custom subset.

Credentials

Under credentials is understood Key & secret key, Account name and token, Anonymous access, Key file. Used to attach cloud storage.

Resource

Under resource is understood bucket name or container name. Used to attach cloud storage.

1.22 - Cloud storages page

The cloud storages page contains elements, each of them relating to a separate cloud storage.  Each element contains: preview, cloud storage name, provider, creation and update info, status, ? button for displaying the description and the actions menu.

Each button in the action menu is responsible for a specific function:

• Update — update this cloud storage
• Delete — delete cloud storage.

This preview will appear when it is impossible to get a real preview (e.g. storage is empty or invalid credentials were used).

In the upper left corner there is a search bar, using which you can find the cloud storage by display name, provider, etc. In the upper right corner there are sorting, quick filters and filter.

Filter

Applying filter disables the quick filter.

The filter works similarly to the filters for annotation, you can create rules from properties, operators and values and group rules into groups. For more details, see the filter section. Learn more about date and time selection.

For clear all filters press Clear filters.

Supported properties for cloud storages list

Click the + button to attach a new cloud storage.

1.23 - Attach cloud storage

In CVAT you can use AWS S3, Azure Blob Container and Google Cloud storages to import and export image datasets for your tasks.

See:

• AWS S3
  • Create a bucket
  • Upload data
  • Access permissions
    • Authorized access
    • Anonymous access
  • Attach AWS S3 storage
  • AWS manifest file
• Google Cloud
  • Create a bucket
  • Upload data
  • Access permissions
    • Authorized access
    • Anonymous access
  • Attach Google Cloud storage
• Microsoft Azure
  • Create a bucket
  • Create a container
  • Upload data
  • SAS token and connection string
  • Personal use
  • Attach Azure Blob Container
• Prepare the dataset

AWS S3

Create a bucket

To create bucket, do the following:

1. Create an AWS account.

2. Go to console AWS-S3, and click Create bucket.

   

3. Specify the name and region of the bucket. You can also copy the settings of another bucket by clicking on the Choose bucket button.

4. Enable Block all public access. For access, you will use access key ID and secret access key.

5. Click Create bucket.

A new bucket will appear on the list of buckets.

Upload data

You need to upload data for annotation and the manifest.jsonl file.

1. Prepare data. For more information, see prepare the dataset.

2. Open the bucket and click Upload.

   

3. Drag the manifest file and image folder on the page and click Upload:

Access permissions

Authorized access

To add access permissions, do the following:

1. Go to IAM and click Add users.

2. Set User name and enable Access key - programmatic access.

   

3. Click Next: Permissions.

4. Click Create group, enter the group name.

5. Use search to find and select:

   • For read-only access: AmazonS3ReadOnlyAccess.
   • For full access: AmazonS3FullAccess.

   

6. (Optional) Add tags for the user and go to the next page.

7. Save Access key ID and Secret access key.

For more information, see Creating an IAM user in your AWS account

Anonymous access

On how to grant public access to the bucket, see Configuring block public access settings for your S3 buckets

Attach AWS S3 storage

To attach storage, do the following:

1. Log into CVAT and in the separate tab open your bucket page.
2. In the CVAT, on the top menu select Cloud storages > on the opened page click +.

Fill in the following fields:

After filling in all the fields, click Submit.

AWS manifest file

To prepare the manifest file, do the following:

1. Go to AWS cli and run script for prepare manifest file.
2. Perform the installation, following the aws-shell manual,
   You can configure credentials by running aws configure.
   You will need to enter Access Key ID and Secret Access Key as well as the region.

aws configure
Access Key ID: <your Access Key ID>
Secret Access Key: <your Secret Access Key>

3. Copy the content of the bucket to a folder on your computer:

aws s3 cp <s3://bucket-name> <yourfolder> --recursive

4. After copying the files, you can create a manifest file as described in preapair manifest file section:

python <cvat repository>/utils/dataset_manifest/create.py --output-dir <yourfolder> <yourfolder>

5. When the manifest file is ready, upload it to aws s3 bucket:

• For read and write permissions when you created the user, run:

aws s3 cp <yourfolder>/manifest.jsonl <s3://bucket-name>

• For read-only permissions, use the download through the browser, click upload, drag the manifest file to the page and click upload.

Google Cloud

Create a bucket

To create bucket, do the following:

1. Create Google account and log into it.
2. On the Google Cloud page, click Start Free, then enter the required data and accept the terms of service.

   Note: Google requires to add payment, you will need a bank card to accomplish step 2.

3. Create a Bucket with the following parameters:
   • Name your bucket: Unique name.
   • Choose where to store your data: Set up a location nearest to you.
   • Choose a storage class for your data: Set a default class > Standard.
   • Choose how to control access to objects: Enforce public access prevention on this bucket > Uniform (default).
   • How to protect data: None

You will be forwarded to the bucket.

Upload data

You need to upload data for annotation and the manifest.jsonl file.

1. Prepare data. For more information, see prepare the dataset.
2. Open the bucket and from the top menu select Upload files or Upload folder (depends on how your files are organized).

Access permissions

To access Google Cloud Storage get a Project ID from cloud resource manager page

And follow instructions below based on the preferable type of access.

Authorized access

For authorized access you need to create a service account and key file.

To create a service account:

1. In Google Cloud platform, go to IAM & Admin > Service Accounts and click +Create Service Account.
2. Enter your account name and click Create And Continue.
3. Select a role, for example Basic > Viewer, and click Continue.
4. (Optional) Give access rights to the service account.
5. Click Done.

To create a key:

1. Go to IAM & Admin > Service Accounts > click on account name > Keys.
2. Click Add key and select Create new key > JSON
3. Click Create. The key file will be downloaded automatically.

For more information about keys, see Learn more about creating keys.

Anonymous access

To configure anonymous access:

1. Open the bucket and go to the Permissions tab.
2. Сlick + Grant access to add new principals.
3. In the New principals field specify allUsers, select roles: Cloud Storage Legacy > Storage Legacy Bucket Reader.
4. Click Save.

Now you can attach new Azure Blob container into CVAT.

Attach Google Cloud storage

To attach storage, do the following:

1. Log into CVAT and in the separate tab open your bucket page.
2. In the CVAT, on the top menu select Cloud storages > on the opened page click +.

Fill in the following fields:

After filling in all the fields, click Submit.

Microsoft Azure

Create a bucket

To create bucket, do the following:

1. Create an Microsoft Azure account and log into it.

2. Go to Azure portal, hover over the resource , and in the pop-up window click Create.

   

3. Enter a name for the group and click Review + create, check the entered data and click Create.

4. Go to the resource groups page, navigate to the group that you created and click Create resources.

5. On the marketplace page, use search to find Storage account.

   

6. Click on Storage account and on the next page click Create.

7. On the Basics tab, fill in the following fields:

   • Storage account name: to access container from CVAT.
   • Select a region closest to you.
   • Select Performance > Standart.
   • Select Local-redundancy storage (LRS).
   • Click next: Advanced>.

   

8. On the Advanced page, fill in the following fields:

   • (Optional) Disable Allow enabling public access on containers to prohibit anonymous access to the container.
   • Click Next > Networking.

9. On the Networking tab, fill in the following fields:

   • If you want to change public access, enable Public access from all networks.

   • Click Next>Data protection.

     You do not need to change anything in other tabs until you need some specific setup.

10. Click Review and wait for the data to load.

11. Click Create. Deployment will start.

12. After deployment is over, click Go to resource.

Create a container

To create container, do the following:

1. Go to the containers section and on the top menu click +Container

3. Enter the name of the container.
4. (Optional) In the Public access level drop-down, select type of the access.
   Note: this field will inactive if you disabled Allow enabling public access on containers.
5. Click Create.

Upload data

You need to upload data for annotation and the manifest.jsonl file.

1. Prepare data. For more information, see prepare the dataset.
2. Go to container and click Upload.
3. Click Browse for files and select images.

   Note: If images are in folder, specify folder in the Advanced settings > Upload to folder.

4. Click Upload.

SAS token and connection string

Use the SAS token or connection string to grant secure access to the container.

To configure the credentials:

1. Go to Home > Resourse groups > You resource name > Your storage account.
2. On the left menu, click Shared access signature.
3. Change the following fields:
   • Allowed services: Enable Blob . Disable all other fields.
   • Allowed resource types: Enable Container and Object. Disable all other fields.
   • Allowed permissions: Enable Read, Write, and List. Disable all other fields.
   • Start and expiry date: Set up start and expiry dates.
   • Allowed protocols: Select HTTPS and HTTP
   • Leave all other fields with default parameters.
4. Click Generate SAS and connection string and copy SAS token or Connection string.

Personal use

For personal use, you can use the Access Key from your storage account in the CVAT SAS Token field.

To get the Access Key:

1. In the Azure Portal, go to the Security + networking > Access Keys
2. Click Show and copy the key.

Attach Azure Blob Container

To attach storage, do the following:

1. Log into CVAT and in the separate tab open your bucket page.
2. In the CVAT, on the top menu select Cloud storages > on the opened page click +.

Fill in the following fields:

After filling in all the fields, click Submit.

Prepare the dataset

For example, the dataset is The Oxford-IIIT Pet Dataset:

1. Download the archive with images.
2. Unpack the archive into the prepared folder.
3. Create a manifest. For more information, see Dataset manifest:

python <cvat repository>/utils/dataset_manifest/create.py --output-dir <your_folder> <your_folder>

2 - Advanced

2.1 - Projects page

Projects page

On this page you can create a new project, create a project from a backup, and also see the created projects.

In the upper left corner there is a search bar, using which you can find the project by project name, assignee etc. In the upper right corner there are sorting, quick filters and filter.

Filter

Applying filter disables the quick filter.

The filter works similarly to the filters for annotation, you can create rules from properties, operators and values and group rules into groups. For more details, see the filter section. Learn more about date and time selection.

For clear all filters press Clear filters.

Supported properties for projects list

Create a project

At CVAT, you can create a project containing tasks of the same type. All tasks related to the project will inherit a list of labels.

To create a project, go to the projects section by clicking on the Projects item in the top menu. On the projects page, you can see a list of projects, use a search, or create a new project by clicking on the + button and select Create New Project.

Note that the project will be created in the organization that you selected at the time of creation. Read more about organizations.

You can change: the name of the project, the list of labels (which will be used for tasks created as parts of this project) and a skeleton if it’s necessary. In advanced configuration also you can specify: a link to the issue, source and target storages. Learn more about creating a label list, creating the skeleton and attach cloud storage.

To save and open project click on Submit & Open button. Also you can click on Submit & Continue button for creating several projects in sequence

Once created, the project will appear on the projects page. To open a project, just click on it.

Here you can do the following:

1. Change the project’s title.

2. Open the Actions menu. Each button is responsible for a specific function in the Actions menu:

   • Export dataset/Import dataset - download/upload annotations or annotations and images in a specific format. More information is available in the export/import datasets section.
   • Backup project - make a backup of the project read more in the backup section.
   • Delete - remove the project and all related tasks.

3. Change issue tracker or open issue tracker if it is specified.

4. Change labels and skeleton. You can add new labels or add attributes for the existing labels in the Raw mode or the Constructor mode. You can also change the color for different labels. By clicking Setup skeleton you can create a skeleton for this project.

5. Assigned to — is used to assign a project to a person. Start typing an assignee’s name and/or choose the right person out of the dropdown list.

6. Tasks — is a list of all tasks for a particular project, with the ability to search, sort and filter for tasks in the project. Read more about search. Read more about sorting and filter It is possible to choose a subset for tasks in the project. You can use the available options (Train, Test, Validation) or set your own.

2.2 - Organization

Organization is a feature for teams of several users who work together on projects and share tasks.

Create an Organization, invite your team members, and assign roles to make the team work better on shared tasks.

See:

• Personal workspace
• Create new organization
  • Switching between organizations
• Organization page
  • Invite members into organization
  • Delete organization

Personal workspace

The account’s default state is activated when no Organization is selected.

If you do not select an Organization, the system links all new resources directly to your personal account, that inhibits resource sharing with others.

When Personal workspace is selected, it will be marked with a tick in the menu.

Create new organization

To create an organization, do the following:

1. Log in to the CVAT.

2. On the top menu, click your Username > Organization > + Create.

   

3. Fill in the following fields and click Submit.

   

Upon creation, the organization page will open automatically.

For future access to your organization, navigate to Username > Organization

Note, that if you’ve created more than 10 organizations, a Switch organization line will appear in the drop-down menu.

Switching between organizations

If you have more than one Organization, it is possible to switch between these Organizations at any given time.

Follow these steps:

1. In the top menu, select your Username > Organization.
2. From the drop-down menu, under the Personal space section, choose the desired Organization.

Note, that if you’ve created more than 10 organizations, a Switch organization line will appear in the drop-down menu.

Click on it to see the Select organization dialog, and select organization from drop-down list.

Organization page

Organization page is a place, where you can edit the Organization information and manage Organization members.

Note that in order to access the organization page, you must first activate the organization (see Switching between organizations). Without activation, the organization page will remain inaccessible.
An organization is considered activated when it’s ticked in the drop-down menu and its name is visible in the top-right corner under the username.

To go to the Organization page, do the following:

1. On the top menu, click your Username > Organization.
2. In the drop-down menu, select Organization.
3. In the drop-down menu, click Settings.

Invite members into organization

To add members to Organization do the following:

1. Go to the Organization page, and click Invite members.

2. Fill in the form (see below).

   

3. Click Ok.

The Invite Members form has the following fields:

Members of Organization will appear on the Organization page.

The member of the organization can leave the organization by going to Organization page > Leave organization.

The organization owner can remove members, by clicking on the Bin icon.

Delete organization

You can remove an organization that you created.

Note: Removing an organization will delete all related resources (annotations, jobs, tasks, projects, cloud storage, and so on).

To remove an organization, do the following:

1. Go to the Organization page.
2. In the top-right corner click Actions > Remove organization.
3. Enter the short name of the organization in the dialog field.
4. Click Remove.

2.3 - Search

There are several options how to use the search.

• Search within all fields (owner, assignee, task name, task status, task mode). To execute enter a search string in search field.
• Search for specific fields. How to perform:
  • owner: admin - all tasks created by the user who has the substring “admin” in his name
  • assignee: employee - all tasks which are assigned to a user who has the substring “employee” in his name
  • name: training - all tasks with the substring “training” in their names
  • mode: annotation or mode: interpolation - all tasks with images or videos.
  • status: annotation or status: validation or status: completed - search by status
  • id: 5 - task with id = 5.
• Multiple filters. Filters can be combined (except for the identifier) ​​using the keyword AND:
  • mode: interpolation AND owner: admin
  • mode: annotation and status: annotation

The search is case insensitive.

2.4 - Shape mode (advanced)

Basic operations in the mode were described in section shape mode (basics).

Occluded Occlusion is an attribute used if an object is occluded by another object or isn’t fully visible on the frame. Use Q shortcut to set the property quickly.

Example: the three cars on the figure below should be labeled as occluded.

If a frame contains too many objects and it is difficult to annotate them due to many shapes placed mostly in the same place, it makes sense to lock them. Shapes for locked objects are transparent, and it is easy to annotate new objects. Besides, you can’t change previously annotated objects by accident. Shortcut: L.

2.5 - Track mode (advanced)

Basic operations in the mode were described in section track mode (basics).

Shapes that were created in the track mode, have extra navigation buttons.

• These buttons help to jump to the previous/next keyframe.

  

• The button helps to jump to the initial frame and to the last keyframe.

  

You can use the Split function to split one track into two tracks:

2.6 - 3D Object annotation (advanced)

As well as 2D-task objects, 3D-task objects support the ability to change appearance, attributes, properties and have an action menu. Read more in objects sidebar section.

Moving an object

If you hover the cursor over a cuboid and press Shift+N, the cuboid will be cut, so you can paste it in other place (double-click to paste the cuboid).

Copying

As well as in 2D task you can copy and paste objects by Ctrl+C and Ctrl+V, but unlike 2D tasks you have to place a copied object in a 3D space (double click to paste).

Image of the projection window

You can copy or save the projection-window image by left-clicking on it and selecting a “save image as” or “copy image”.

2.7 - Attribute annotation mode (advanced)

Basic operations in the mode were described in section attribute annotation mode (basics).

It is possible to handle lots of objects on the same frame in the mode.

It is more convenient to annotate objects of the same type. In this case you can apply the appropriate filter. For example, the following filter will hide all objects except person: label=="Person".

To navigate between objects (person in this case), use the following buttons switch between objects in the frame on the special panel:

or shortcuts:

• Tab — go to the next object
• Shift+Tab — go to the previous object.

In order to change the zoom level, go to settings (press F3) in the workspace tab and set the value Attribute annotation mode (AAM) zoom margin in px.

2.8 - Annotation with rectangles

To learn more about annotation using a rectangle, see the sections:

• Shape mode (basics)
• Track mode (basics)
• Shape mode (advanced)
• Track mode (advanced)

Rotation rectangle

To rotate the rectangle, pull on the rotation point. Rotation is done around the center of the rectangle. To rotate at a fixed angle (multiple of 15 degrees), hold shift. In the process of rotation, you can see the angle of rotation.

Annotation with rectangle by 4 points

It is an efficient method of bounding box annotation, proposed here. Before starting, you need to make sure that the drawing method by 4 points is selected.

Press Shape or Track for entering drawing mode. Click on four extreme points: the top, bottom, left- and right-most physical points on the object. Drawing will be automatically completed right after clicking the fourth point. Press Esc to cancel editing.

2.9 - Annotation with polygons

2.9.1 - Manual drawing

It is used for semantic / instance segmentation.

Before starting, you need to select Polygon on the controls sidebar and choose the correct Label.

• Click Shape to enter drawing mode. There are two ways to draw a polygon: either create points by clicking or by dragging the mouse on the screen while holding Shift.

Clicking points	Holding Shift+Dragging

• When Shift isn’t pressed, you can zoom in/out (when scrolling the mouse wheel) and move (when clicking the mouse wheel and moving the mouse), you can also delete the previous point by right-clicking on it.
• You can use the Selected opacity slider in the Objects sidebar to change the opacity of the polygon. You can read more in the Objects sidebar section.
• Press N again or click the Done button on the top panel for completing the shape.
• After creating the polygon, you can move the points or delete them by right-clicking and selecting Delete point or clicking with pressed Alt key in the context menu.

2.9.2 - Drawing using automatic borders

You can use auto borders when drawing a polygon. Using automatic borders allows you to automatically trace the outline of polygons existing in the annotation.

• To do this, go to settings -> workspace tab and enable Automatic Bordering or press Ctrl while drawing a polygon.

  

• Start drawing / editing a polygon.

• Points of other shapes will be highlighted, which means that the polygon can be attached to them.

• Define the part of the polygon path that you want to repeat.

  

• Click on the first point of the contour part.

  

• Then click on any point located on part of the path. The selected point will be highlighted in purple.

  

• Click on the last point and the outline to this point will be built automatically.

  

Besides, you can set a fixed number of points in the Number of points field, then drawing will be stopped automatically. To enable dragging you should right-click inside the polygon and choose Switch pinned property.

Below you can see results with opacity and black stroke:

If you need to annotate small objects, increase Image Quality to 95 in Create task dialog for your convenience.

2.9.3 - Edit polygon

To edit a polygon you have to click on it while holding Shift, it will open the polygon editor.

• In the editor you can create new points or delete part of a polygon by closing the line on another point.

• When Intelligent polygon cropping option is activated in the settings, CVAT considers two criteria to decide which part of a polygon should be cut off during automatic editing.

  • The first criteria is a number of cut points.
  • The second criteria is a length of a cut curve.

  If both criteria recommend to cut the same part, algorithm works automatically, and if not, a user has to make the decision. If you want to choose manually which part of a polygon should be cut off, disable Intelligent polygon cropping in the settings. In this case after closing the polygon, you can select the part of the polygon you want to leave.

  

• You can press Esc to cancel editing.

  

2.9.4 - Track mode with polygons

Polygons in the track mode allow you to mark moving objects more accurately other than using a rectangle (Tracking mode (basic); Tracking mode (advanced)).

1. To create a polygon in the track mode, click the Track button.

   

2. Create a polygon the same way as in the case of Annotation with polygons. Press N or click the Done button on the top panel to complete the polygon.

3. Pay attention to the fact that the created polygon has a starting point and a direction, these elements are important for annotation of the following frames.

4. After going a few frames forward press Shift+N, the old polygon will disappear and you can create a new polygon. The new starting point should match the starting point of the previously created polygon (in this example, the top of the left mirror). The direction must also match (in this example, clockwise). After creating the polygon, press N and the intermediate frames will be interpolated automatically.

   

5. If you need to change the starting point, right-click on the desired point and select Set starting point. To change the direction, right-click on the desired point and select switch orientation.

   

There is no need to redraw the polygon every time using Shift+N, instead you can simply move the points or edit a part of the polygon by pressing Shift+Click.

2.9.5 - Creating masks

Cutting holes in polygons

Currently, CVAT does not support cutting transparent holes in polygons. However, it is poissble to generate holes in exported instance and class masks. To do this, one needs to define a background class in the task and draw holes with it as additional shapes above the shapes needed to have holes:

The editor window:

Remember to use z-axis ordering for shapes by [-] and [+, =] keys.

Exported masks:

Notice that it is currently impossible to have a single instance number for internal shapes (they will be merged into the largest one and then covered by “holes”).

Creating masks

There are several formats in CVAT that can be used to export masks:

• Segmentation Mask (PASCAL VOC masks)
• CamVid
• MOTS
• ICDAR
• COCO (RLE-encoded instance masks, guide)
• TFRecord (over Datumaro, guide):
• Datumaro

An example of exported masks (in the Segmentation Mask format):

Important notices:

• Both boxes and polygons are converted into masks
• Grouped objects are considered as a single instance and exported as a single mask (label and attributes are taken from the largest object in the group)

Class colors

All the labels have associated colors, which are used in the generated masks. These colors can be changed in the task label properties:

Label colors are also displayed in the annotation window on the right panel, where you can show or hide specific labels (only the presented labels are displayed):

A background class can be:

• A default class, which is implicitly-added, of black color (RGB 0, 0, 0)
• background class with any color (has a priority, name is case-insensitive)
• Any class of black color (RGB 0, 0, 0)

To change background color in generated masks (default is black), change background class color to the desired one.

2.10 - Annotation with polylines

It is used for road markup annotation etc.

Before starting, you need to select the Polyline. You can set a fixed number of points in the Number of points field, then drawing will be stopped automatically.

Click Shape to enter drawing mode. There are two ways to draw a polyline — you either create points by clicking or by dragging a mouse on the screen while holding Shift. When Shift isn’t pressed, you can zoom in/out (when scrolling the mouse wheel) and move (when clicking the mouse wheel and moving the mouse), you can delete previous points by right-clicking on it. Press N again or click the Done button on the top panel to complete the shape. You can delete a point by clicking on it with pressed Ctrl or right-clicking on a point and selecting Delete point. Click with pressed Shift will open a polyline editor. There you can create new points(by clicking or dragging) or delete part of a polygon closing the red line on another point. Press Esc to cancel editing.

2.11 - Annotation with points

2.11.1 - Points in shape mode

It is used for face, landmarks annotation etc.

Before you start you need to select the Points. If necessary you can set a fixed number of points in the Number of points field, then drawing will be stopped automatically.

Click Shape to entering the drawing mode. Now you can start annotation of the necessary area. Points are automatically grouped — all points will be considered linked between each start and finish. Press N again or click the Done button on the top panel to finish marking the area. You can delete a point by clicking with pressed Ctrl or right-clicking on a point and selecting Delete point. Clicking with pressed Shift will open the points shape editor. There you can add new points into an existing shape. You can zoom in/out (when scrolling the mouse wheel) and move (when clicking the mouse wheel and moving the mouse) while drawing. You can drag an object after it has been drawn and change the position of individual points after finishing an object.

2.11.2 - Linear interpolation with one point

You can use linear interpolation for points to annotate a moving object:

1. Before you start, select the Points.

2. Linear interpolation works only with one point, so you need to set Number of points to 1.

3. After that select the Track.

   

4. Click Track to enter the drawing mode left-click to create a point and after that shape will be automatically completed.

   

5. Move forward a few frames and move the point to the desired position, this way you will create a keyframe and intermediate frames will be drawn automatically. You can work with this object as with an interpolated track: you can hide it using the Outside, move around keyframes, etc.

   

6. This way you’ll get linear interpolation using the Points.

   

2.12 - Annotation with ellipses

It is used for road sign annotation etc.

First of all you need to select the ellipse on the controls sidebar.

Choose a Label and click Shape or Track to start drawing. An ellipse can be created the same way as a rectangle, you need to specify two opposite points, and the ellipse will be inscribed in an imaginary rectangle. Press N or click the Done button on the top panel to complete the shape.

You can rotate ellipses using a rotation point in the same way as rectangles.

2.13 - Annotation with cuboids

It is used to annotate 3 dimensional objects such as cars, boxes, etc… Currently the feature supports one point perspective and has the constraint where the vertical edges are exactly parallel to the sides.

2.13.1 - Creating the cuboid

Before you start, you have to make sure that Cuboid is selected and choose a drawing method ”from rectangle” or “by 4 points”.

Drawing cuboid by 4 points

Choose a drawing method “by 4 points” and click Shape to enter the drawing mode. There are many ways to draw a cuboid. You can draw the cuboid by placing 4 points, after that the drawing will be completed automatically. The first 3 points determine the plane of the cuboid while the last point determines the depth of that plane. For the first 3 points, it is recommended to only draw the 2 closest side faces, as well as the top and bottom face.

A few examples:

Drawing cuboid from rectangle

Choose a drawing method “from rectangle” and click Shape to enter the drawing mode. When you draw using the rectangle method, you must select the frontal plane of the object using the bounding box. The depth and perspective of the resulting cuboid can be edited.

Example:

2.13.2 - Editing the cuboid

The cuboid can be edited in multiple ways: by dragging points, by dragging certain faces or by dragging planes. First notice that there is a face that is painted with gray lines only, let us call it the front face.

You can move the cuboid by simply dragging the shape behind the front face. The cuboid can be extended by dragging on the point in the middle of the edges. The cuboid can also be extended up and down by dragging the point at the vertices.

To draw with perspective effects it should be assumed that the front face is the closest to the camera. To begin simply drag the points on the vertices that are not on the gray/front face while holding Shift. The cuboid can then be edited as usual.

If you wish to reset perspective effects, you may right click on the cuboid, and select Reset perspective to return to a regular cuboid.

The location of the gray face can be swapped with the adjacent visible side face. You can do it by right clicking on the cuboid and selecting Switch perspective orientation. Note that this will also reset the perspective effects.

Certain faces of the cuboid can also be edited, these faces are: the left, right and dorsal faces, relative to the gray face. Simply drag the faces to move them independently from the rest of the cuboid.

You can also use cuboids in track mode, similar to rectangles in track mode (basics and advanced) or Track mode with polygons

2.14 - Annotation with skeletons

Skeletons should be used as annotations templates when you need to annotate complex objects sharing the same structure (e.g. human pose estimation, facial landmarks, etc.). A skeleton consist of any number of points (also called as elements), joined or not joined by edges. Any point itself is considered like an individual object with its own attributes and properties (like color, occluded, outside, etc). At the same time a skeleton point can exist only within the parent skeleton.

Any skeleton elements can be hidden (by marking them outside) if necessary (for example if a part is out of a frame). Currently there are two formats which support exporting skeletons: CVAT & COCO.

2.14.1 - Creating the skeleton

Initial skeleton setup

Unlike other CVAT objects, to start annotating using skeletons, first of all you need to setup a skeleton. You can do that in the label configurator during creating a task/project, or later in created instances.

So, start by clicking Setup skeleton option:

Below the regular label form where you need to add a name, and setup attributes if necessary, you will see a drawing area with some buttons aside:

• PUT AN IMAGE AS A BACKGROUND - is a helpful feature you can use to draw a skeleton template easier, seeing an example - object you need to annotate in the future.
• PUT NEW SKELETON POINTS - is activated by default. It is a mode where you can add new skeleton points clicking the drawing area.
• DRAW AN EDGE BETWEEN TWO POINTS - in this mode you can add an edge, clicking any two points, which are not joined yet.
• REMOVE A DRAWN SKELETON POINTS - in this mode clicking a point will remove the point and all attached edges. You can also remove an edge only, it will be highlighted as red on hover.
• DOWNLOAD DRAWN TEMPLATE AS AN .SVG - you can download setup configuration to use it in future
• UPLOAD A TEMPLATE FROM AN .SVG FILE - you can upload previously downloaded configuration

Let’s draw an exampe skeleton - star. After the skeleton is drawn, you can setup each its point. Just hover the point, do right mouse click and click Configure:

Here you can setup a point name, its color and attributes if necessary like for a regular CVAT label:

Press Done button to finish editing the point. Press Continue button to save the skeleton. Continue creating a task/project in a regular way.

For an existing task/project you are not allowed to change a skeleton configuration for now. You can copy/insert skeletons configuration using Raw tab of the label configurator.

Drawing a skeleton from rectangle

In opened job go to left sidebar and find Draw new skeleton control, hover it:

If the control is absent, be sure you have setup at least one skeleton in the corresponding task/project. In a pop-up dropdown you can select between a skeleton Shape and a skeleton Track, depends on your task. Draw a skeleton as a regular bounding box, clicking two points on a canvas:

Well done, you’ve just created the first skeleton.

2.14.2 - Editing the skeleton

Editing skeletons on the canvas

A drawn skeleton is wrapped by a bounding box for a user convenience. Using this wrapper the user can edit the skeleton as a regular bounding box, by dragging, resizing, or rotating:

Moreover, each the skeleton point can be dragged itself. After dragging, the wrapping bounding box is adjusted automatically, other points are not affected:

You can use Shortcuts on both a skeleton itself and its elements.

• Hover the mouse cursor over the bounding box to apply a shortcut on the whole skeleton (like lock, occluded, pinned, keyframe and outside for skeleton tracks)
• Hover the mouse cursor over one of skeleton points to apply a shortcut to this point (the same shortcuts list, but outside is available also for a skeleton shape elements)

Editing skeletons on the sidebar

Using the sidebar is another way to setup skeleton properties, and attributes. It works a similar way, like for other kinds of objects supported by CVAT, but with some changes:

• A user is not allowed to switch a skeleton label
• Outside property is always available for skeleton elements (it does not matter if they are tracks or not)
• Additional collapse is available for a user, to see a list of skeleton parts

2.15 - Annotation with brush tool

With a brush tool, you can create masks for disjoint objects, that have multiple parts, such as a house hiding behind trees, a car behind a pedestrian, or a pillar behind a traffic sign. The brush tool has several modes, for example: erase pixels, change brush shapes, and polygon-to-mask mode.

Use brush tool for Semantic (Panoptic) and Instance Image Segmentation tasks.
For more information about segmentation masks in CVAT, see Creating masks.

See:

• Brush tool menu
• Annotation with brush
• Annotation with polygon-to-mask
• Remove underlying pixels
• AI Tools
• Import and export

Brush tool menu

The brush tool menu appears on the top of the screen after you click Shape:

It has the following elements:

Element	Description
	Save mask saves the created mask. The saved mask will appear on the object sidebar
	Save mask and continue adds a new mask to the object sidebar and allows you to draw a new one immediately.
	Brush adds new mask/ new regions to the previously added mask).
	Eraser removes part of the mask.
	Polygon selection tool. Selection will become a mask.
	Remove polygon selection subtracts part of the polygon selection.
	Brush size in pixels. Note: Visible only when Brush or Eraser are selected.
	Brush shape with two options: circle and square. Note: Visible only when Brush or Eraser are selected.
	Remove underlying pixels. When you are drawing or editing a mask with this tool, pixels on other masks that are located at the same positions as the pixels of the current mask are deleted.
	Label that will be assigned to the newly created mask
	Move. Click and hold to move the menu bar to the other place on the screen

Annotation with brush

To annotate with brush, do the following:

1. From the controls sidebar, select Brush .

2. In the Draw new mask menu, select label for your mask, and click Shape.
   The Brush tool will be selected by default.

   

3. With the brush, draw a mask on the object you want to label.
   To erase selection, use Eraser

   

4. After you applied the mask, on the top menu bar click Save mask
   to finish the process (or N on the keyboard).

5. Added object will appear on the objects sidebar.

To add the next object, repeat steps 1 to 5. All added objects will be visible on the image and the objects sidebar.

To save the job with all added objects, on the top menu click Save .

Annotation with polygon-to-mask

To annotat with polygon-to-mask, do the following:

1. From the controls sidebar, select Brush .

2. In the Draw new mask menu, select label for your mask, and click Shape.

   

3. In the brush tool menu, select Polygon .

4. With the Polygon tool, draw a mask for the object you want to label.
   To correct selection, use Remove polygon selection .

5. Use Save mask (or N on the keyboard)
   to switch between add/remove polygon tools:

   

6. After you added the polygon selection, on the top menu bar click Save mask
   to finish the process (or N on the keyboard).

7. Click Save mask again (or N on the keyboard).
   The added object will appear on the objects sidebar.

To add the next object, repeat steps 1 to 5.

All added objects will be visible on the image and the objects sidebar.

To save the job with all added objects, on the top menu click Save .

Remove underlying pixels

Use Remove underlying pixels tool when you want to add a mask and simultaneously delete the pixels of
other masks that are located at the same positions. It is a highly useful feature to avoid meticulous drawing edges twice between two different objects.

AI Tools

You can convert AI tool masks to polygons. To do this, use the following AI tool menu:

1. Go to the Detectors tab.
2. Switch toggle Masks to polygons to the right.
3. Add source and destination labels from the drop-down lists.
4. Click Annotate.

Import and export

For export, see Export dataset

Import follows the general import dataset procedure, with the additional option of converting masks to polygons.

Note: This option is available for formats that work with masks only.

To use it, when uploading the dataset, switch the Convert masks to polygon toggle to the right:

2.16 - Annotation with tags

It is used to annotate frames, tags are not displayed in the workspace. Before you start, open the drop-down list in the top panel and select Tag annotation.

The objects sidebar will be replaced with a special panel for working with tags. Here you can select a label for a tag and add it by clicking on the Plus button. You can also customize hotkeys for each label.

If you need to use only one label for one frame, then enable the Automatically go to the next frame checkbox, then after you add the tag the frame will automatically switch to the next.

Tags will be shown in the top left corner of the canvas. You can show/hide them in the settings.

2.17 - Models

To deploy the models, you will need to install the necessary components using Semi-automatic and Automatic Annotation guide. To learn how to deploy the model, read Serverless tutorial.

The Models page contains a list of deep learning (DL) models deployed for semi-automatic and automatic annotation. To open the Models page, click the Models button on the navigation bar. The list of models is presented in the form of a table. The parameters indicated for each model are the following:

• Framework the model is based on
• model Name
• model Type:
  • detector - used for automatic annotation (available in detectors and automatic annotation)
  • interactor - used for semi-automatic shape annotation (available in interactors)
  • tracker - used for semi-automatic track annotation (available in trackers)
  • reid - used to combine individual objects into a track (available in automatic annotation)
• Description - brief description of the model
• Labels - list of the supported labels (only for the models of the detectors type)

2.18 - CVAT Analytics and quality assessment in Cloud

2.18.1 - Annotation quality & Honeypot

In CVAT, it’s possible to evaluate the quality of annotation through the creation of a Ground truth job, referred to as a Honeypot. To estimate the task quality, CVAT compares all other jobs in the task against the established Ground truth job, and calculates annotation quality based on this comparison.

Note that quality estimation only supports 2d tasks. It supports all the annotation types except 2d cuboids.

Note that tracks are considered separate shapes and compared on a per-frame basis with other tracks and shapes.

See:

• Ground truth job
• Managing Ground Truth jobs: Import, Export, and Deletion
  • Import
  • Export
  • Delete
• Assessing data quality with Ground truth jobs
  • Quality data
  • Annotation quality settings
  • GT conflicts in the CVAT interface
• Annotation quality & Honeypot video tutorial

Ground truth job

A Ground truth job is a way to tell CVAT where to store and get the “correct” annotations for task quality estimation.

To estimate task quality, you need to create a Ground truth job in the task, and annotate it. You don’t need to annotate the whole dataset twice, the annotation quality of a small part of the data shows the quality of annotation for the whole dataset.

For the quality assurance to function correctly, the Ground truth job must have a small portion of the task frames and the frames must be chosen randomly. Depending on the dataset size and task complexity, 5-15% of the data is typically good enough for quality estimation, while keeping extra annotation overhead acceptable.

For example, in a typical task with 2000 frames, selecting just 5%, which is 100 extra frames to annotate, is enough to estimate the annotation quality. If the task contains only 30 frames, it’s advisable to select 8-10 frames, which is about 30%.

It is more than 15% but in the case of smaller datasets, we need more samples to estimate quality reliably.

To create a Ground truth job, do the following:

1. Create a task, and open the task page.

2. Click +.

   

3. In the Add new job window, fill in the following fields:

   

   • Job type: Use the default parameter Ground truth.
   • Frame selection method: Use the default parameter Random.
   • Quantity %: Set the desired percentage of frames for the Ground truth job.
     Note that when you use Quantity %, the Frames field will be autofilled.
   • Frame count: Set the desired number of frames for the “ground truth” job.
     Note that when you use Frames, the Quantity % field will be will be autofilled.
   • Seed: (Optional) If you need to make the random selection reproducible, specify this number. It can be any integer number, the same value will yield the same random selection (given that the frame number is unchanged).
     Note that if you want to use a custom frame sequence, you can do this using the server API instead, see Jobs API #create.

4. Click Submit.

5. Annotate frames, save your work.

6. Change the status of the job to Completed.

7. Change Stage to Accepted.

The Ground truth job will appear in the jobs list.

Managing Ground Truth jobs: Import, Export, and Deletion

Annotations from Ground truth jobs are not included in the dataset export, they also cannot be imported during task annotations import or with automatic annotation for the task.

Import, export, and delete options are available from the job’s menu.

Import

If you want to import annotations into the Ground truth job, do the following.

1. Open the task, and find the Ground truth job in the jobs list.
2. Click on three dots to open the menu.
3. From the menu, select Import annotations.
4. Select import format, and select file.
5. Click OK.

Note that if there are imported annotations for the frames that exist in the task, but are not included in the Ground truth job, they will be ignored. This way, you don’t need to worry about “cleaning up” your Ground truth annotations for the whole dataset before importing them. Importing annotations for the frames that are not known in the task still raises errors.

Export

To export annotations from the Ground truth job, do the following.

1. Open the task, and find a job in the jobs list.
2. Click on three dots to open the menu.
3. From the menu, select Export annotations.

Delete

To delete the Ground truth job, do the following.

1. Open the task, and find the Ground truth job in the jobs list.
2. Click on three dots to open the menu.
3. From the menu, select Delete.

Assessing data quality with Ground truth jobs

Once you’ve established the Ground truth job, proceed to annotate the dataset.

CVAT will begin the quality comparison between the annotated task and the Ground truth job in this task once it is finished (on the acceptance stage and in the completed state).

Note that the process of quality calculation may take up to several hours, depending on the amount of data and labeled objects, and is not updated immediately after task updates.

To view results go to the Task > Actions > View analytics> Performance tab.

Quality data

The Analytics page has the following fields:

Annotation quality settings

If you need to tweak some aspects of comparisons, you can do this from the Annotation Quality Settings menu.

You can configure what overlap should be considered low or how annotations must be compared.

The updated settings will take effect on the next quality update.

To open Annotation Quality Settings, find Quality report and on the right side of it, click on three dots.

The following window will open. Hover over the ? marks to understand what each field represents.

Annotation quality settings have the following parameters:

GT conflicts in the CVAT interface

To see GT Conflicts in the CVAT interface, go to Review > Issues > Show ground truth annotations and conflicts.

The ground truth (GT) annotation is depicted as a dotted-line box with an associated label.

Upon hovering over an issue on the right-side panel with your mouse, the corresponding GT Annotation gets highlighted.

Use arrows in the Issue toolbar to move between GT conflicts.

To create an issue related to the conflict, right-click on the bounding box and from the menu select the type of issue you want to create.

Annotation quality & Honeypot video tutorial

This video demonstrates the process:

2.18.2 - CVAT Performance & Monitoring

In CVAT Cloud, you can track a variety of metrics reflecting the team’s productivity and the pace of annotation with the Performance feature.

See:

• Performance dashboard
• Performance video tutorial

Performance dashboard

To open the Performance dashboard, do the following:

1. In the top menu click on Projects/ Tasks/ Jobs.
2. Select an item from the list, and click on three dots ().
3. From the menu, select View analytics > Performance tab.

The following dashboard will open:

The Performance dashboard has the following elements:

You can rearrange elements of the dashboard by dragging and dropping each of them.

Performance video tutorial

This video demonstrates the process:

2.19 - OpenCV and AI Tools

Label and annotate your data in semi-automatic and automatic mode with the help of AI and OpenCV tools.

While interpolation is good for annotation of the videos made by the security cameras, AI and OpenCV tools are good for both: videos where the camera is stable and videos, where it moves together with the object, or movements of the object are chaotic.

See:

• Interactors
  • AI tools: annotate with interactors
  • AI tools: add extra points
  • AI tools: delete points
  • OpenCV: intelligent scissors
  • Settings
  • Interactors models
• Detectors
  • Labels matching
  • Annotate with detectors
  • Detectors models
• Trackers
  • AI tools: annotate with trackers
  • OpenCV: annotate with trackers
  • When tracking
  • Trackers models
• OpenCV: histogram equalization

Interactors

Interactors are a part of AI and OpenCV tools.

Use interactors to label objects in images by creating a polygon semi-automatically.

When creating a polygon, you can use positive points or negative points (for some models):

• Positive points define the area in which the object is located.
• Negative points define the area in which the object is not located.

AI tools: annotate with interactors

To annotate with interactors, do the following:

1. Click Magic wand , and go to the Interactors tab.
2. From the Label drop-down, select a label for the polygon.
3. From the Interactor drop-down, select a model (see Interactors models).
   Click the Question mark to see information about each model:
   
4. (Optional) If the model returns masks, and you need to convert masks to polygons, use the Convert masks to polygons toggle.
5. Click Interact.
6. Use the left click to add positive points and the right click to add negative points.
   Number of points you can add depends on the model.
7. On the top menu, click Done (or Shift+N, N).

AI tools: add extra points

Note: More points improve outline accuracy, but make shape editing harder. Fewer points make shape editing easier, but reduce outline accuracy.

Each model has a minimum required number of points for annotation. Once the required number of points is reached, the request is automatically sent to the server. The server processes the request and adds a polygon to the frame.

For a more accurate outline, postpone request to finish adding extra points first:

1. Hold down the Ctrl key.
   On the top panel, the Block button will turn blue.
2. Add points to the image.
3. Release the Ctrl key, when ready.

In case you used Mask to polygon when the object is finished, you can edit it like a polygon.

You can change the number of points in the polygon with the slider:

AI tools: delete points

To delete a point, do the following:

1. With the cursor, hover over the point you want to delete.
2. If the point can be deleted, it will enlarge and the cursor will turn into a cross.
3. Left-click on the point.

OpenCV: intelligent scissors

To use Intelligent scissors, do the following:

1. On the menu toolbar, click OpenCV and wait for the library to load.

   
   

2. Go to the Drawing tab, select the label, and click on the Intelligent scissors button.

   

3. Add the first point on the boundary of the allocated object.
   You will see a line repeating the outline of the object.

4. Add the second point, so that the previous point is within the restrictive threshold.
   After that a line repeating the object boundary will be automatically created between the points.

5. To finish placing points, on the top menu click Done (or N on the keyboard).

As a result, a polygon will be created.

You can change the number of points in the polygon with the slider:

To increase or lower the action threshold, hold Ctrl and scroll the mouse wheel.

During the drawing process, you can remove the last point by clicking on it with the left mouse button.

Settings

• On how to adjust the polygon, see Objects sidebar.

• For more information about polygons in general, see Annotation with polygons.

Interactors models

Model	Tool	Description	Example
Segment Anything Model (SAM)	AI Tools	The Segment Anything Model (SAM) produces high quality object masks, and it can be used to generate masks for all objects in an image. It has been trained on a dataset of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks. For more information, see: GitHub: Segment Anything Site: Segment Anything Paper: Segment Anything
Deep extreme cut (DEXTR)	AI Tool	This is an optimized version of the original model, introduced at the end of 2017. It uses the information about extreme points of an object to get its mask. The mask is then converted to a polygon. For now this is the fastest interactor on the CPU. For more information, see: GitHub: DEXTR-PyTorch Site: DEXTR-PyTorch Paper: DEXTR-PyTorch
Feature backpropagating refinement scheme (f-BRS)	AI Tool	The model allows to get a mask for an object using positive points (should be left-clicked on the foreground), and negative points (should be right-clicked on the background, if necessary). It is recommended to run the model on GPU, if possible. For more information, see: GitHub: f-BRS Paper: f-BRS
High Resolution Net (HRNet)	AI Tool	The model allows to get a mask for an object using positive points (should be left-clicked on the foreground), and negative points (should be right-clicked on the background, if necessary). It is recommended to run the model on GPU, if possible. For more information, see: GitHub: HRNet Paper: HRNet
Inside-Outside-Guidance (IOG)	AI Tool	The model uses a bounding box and inside/outside points to create a mask. First of all, you need to create a bounding box, wrapping the object. Then you need to use positive and negative points to say the model where is a foreground, and where is a background. Negative points are optional. For more information, see: GitHub: IOG Paper: IOG
Intelligent scissors	OpenCV	Intelligent scissors is a CV method of creating a polygon by placing points with the automatic drawing of a line between them. The distance between the adjacent points is limited by the threshold of action, displayed as a red square that is tied to the cursor. For more information, see: Site: Intelligent Scissors Specification

Detectors

Detectors are a part of AI tools.

Use detectors to automatically identify and locate objects in images or videos.

Labels matching

Each model is trained on a dataset and supports only the dataset’s labels.

For example:

• DL model has the label car.
• Your task (or project) has the label vehicle.

To annotate, you need to match these two labels to give DL model a hint, that in this case car = vehicle.

If you have a label that is not on the list of DL labels, you will not be able to match them.

For this reason, supported DL models are suitable only for certain labels.
To check the list of labels for each model, see Detectors models.

Annotate with detectors

To annotate with detectors, do the following:

1. Click Magic wand , and go to the Detectors tab.

2. From the Model drop-down, select model (see Detectors models).

3. From the left drop-down select the DL model label, from the right drop-down select the matching label of your task.

   

4. (Optional) If the model returns masks, and you need to convert masks to polygons, use the Convert masks to polygons toggle.

5. Click Annotate.

This action will automatically annotate one frame. For automatic annotation of multiple frames, see Automatic annotation.

Detectors models

Trackers

Trackers are part of AI and OpenCV tools.

Use trackers to identify and label objects in a video or image sequence that are moving or changing over time.

AI tools: annotate with trackers

To annotate with trackers, do the following:

1. Click Magic wand , and go to the Trackers tab.

   
   

2. From the Label drop-down, select the label for the object.

3. From Tracker drop-down, select tracker.

4. Click Track, and annotate the objects with the bounding box in the first frame.

5. Go to the top menu and click Next (or the F on the keyboard) to move to the next frame.
   All annotated objects will be automatically tracked.

OpenCV: annotate with trackers

To annotate with trackers, do the following:

1. On the menu toolbar, click OpenCV and wait for the library to load.

   
   

2. Go to the Tracker tab, select the label, and click Tracking.

   
   

3. From the Label drop-down, select the label for the object.

4. From Tracker drop-down, select tracker.

5. Click Track.

6. To move to the next frame, on the top menu click the Next button (or F on the keyboard).

All annotated objects will be automatically tracked when you move to the next frame.

When tracking

• To enable/disable tracking, use Tracker switcher on the sidebar.

  

• Trackable objects have an indication on canvas with a model name.

  

• You can follow the tracking by the messages appearing at the top.

  

Trackers models

Model	Tool	Description	Example
TrackerMIL	OpenCV	TrackerMIL model is not bound to labels and can be used for any object. It is a fast client-side model designed to track simple non-overlapping objects. For more information, see: Article: Object Tracking using OpenCV
SiamMask	AI Tools	Fast online Object Tracking and Segmentation. The trackable object will be tracked automatically if the previous frame was the latest keyframe for the object. For more information, see: GitHub: SiamMask Paper: SiamMask
Transformer Tracking (TransT)	AI Tools	Simple and efficient online tool for object tracking and segmentation. If the previous frame was the latest keyframe for the object, the trackable object will be tracked automatically. This is a modified version of the PyTracking Python framework based on Pytorch For more information, see: GitHub: TransT Paper: TransT

OpenCV: histogram equalization

Histogram equalization improves the contrast by stretching the intensity range.

It increases the global contrast of images when its usable data is represented by close contrast values.

It is useful in images with backgrounds and foregrounds that are bright or dark.

To improve the contrast of the image, do the following:

1. In the OpenCV menu, go to the Image tab.
2. Click on Histogram equalization button.
   

Histogram equalization will improve contrast on current and following frames.

Example of the result:

To disable Histogram equalization, click on the button again.

2.20 - Automatic annotation

Automatic annotation in CVAT is a tool that you can use to automatically pre-annotate your data with pre-trained models.

CVAT can use models from the following sources:

• Pre-installed models.
• Models integrated from Hugging Face and Roboflow.
• Self-hosted models deployed with Nuclio.

The following table describes the available options:

See:

• Running Automatic annotation
• Labels matching
• Models
• Adding models from Hugging Face and Roboflow

Running Automatic annotation

To start automatic annotation, do the following:

1. On the top menu, click Tasks.

2. Find the task you want to annotate and click Action > Automatic annotation.

   

3. In the Automatic annotation dialog, from the drop-down list, select a model.

4. Match the labels of the model and the task.

5. (Optional) In case you need the model to return masks as polygons, switch toggle Return masks as polygons.

6. (Optional) In case you need to remove all previous annotations, switch toggle Clean old annotations.

   

7. Click Annotate.

CVAT will show the progress of annotation on the progress bar.

You can stop the automatic annotation at any moment by clicking cancel.

Labels matching

Each model is trained on a dataset and supports only the dataset’s labels.

For example:

• DL model has the label car.
• Your task (or project) has the label vehicle.

To annotate, you need to match these two labels to give CVAT a hint that, in this case, car = vehicle.

If you have a label that is not on the list of DL labels, you will not be able to match them.

For this reason, supported DL models are suitable only for certain labels.

To check the list of labels for each model, see Models papers and official documentation.

Models

Automatic annotation uses pre-installed and added models.

For self-hosted solutions, you need to install Automatic Annotation first and add models.

List of pre-installed models:

Adding models from Hugging Face and Roboflow

In case you did not find the model you need, you can add a model of your choice from Hugging Face or Roboflow.

Note, that you cannot add models from Hugging Face and Roboflow to self-hosted CVAT.

For more information, see Streamline annotation by integrating Hugging Face and Roboflow models.

This video demonstrates the process:

2.21 - Specification for annotators

The Guide feature provides a built-in markdown editor that allows you to create specification for annotators.

Once you create and submit the specification, it will be accessible from the annotation interface (see below).

You can attach the specification to Projects or to Tasks.

The attachment procedure is the same for individual users and organizations.

See:

• Adding specification to Project
  • Editing rights
• Adding specification to Task
  • Editing rights
• Access to specification for annotators
• Markdown editor guide
• Specification for annotators' video tutorial

Adding specification to Project

To add specification to the Projects, do the following:

1. Go to the Projects page and click on the project to which you want to add specification.
2. Under the Project description, click Edit.

3. Add instruction to the Markdown editor, and click Submit.

Editing rights

• For individual users: only the project owner and the project assignee can edit the specification.
• For organizations: specification additionally can be edited by the organization owner and maintainer

Adding specification to Task

To add specification to the Task, do the following:

1. Go to the Tasks page and click on the task to which you want to add specification.

2. Under the Task description, click Edit.

   

3. Add instruction to the Markdown editor, and click Submit.

Editing rights

• For individual users: only the task owner and task assignee can edit the specification.
• For organizations: only the task owner, maintainer, and task assignee can edit the specification.

Access to specification for annotators

To open specification, do the following:

1. Open the job to see the annotation interface.
2. In the top right corner, click Guide button().

Markdown editor guide

The markdown editor for Guide has two panes. Add instructions to the left pane, and the editor will immediately show the formatted result on the right.

You can write in raw markdown or use the toolbar on the top of the editor.

Specification for annotators' video tutorial

Video tutorial on how to use the Guide feature.

2.22 - Backup Task and Project

Overview

In CVAT you can backup tasks and projects. This can be used to backup a task or project on your PC or to transfer to another server.

Create backup

To backup a task or project, open the action menu and select Backup Task or Backup Project.

You can backup a project or a task locally on your PC or using an attached cloud storage.

(Optional) Specify the name in the Custom name text field for backup, otherwise the file of backup name will be given by the mask project_<project_name>_backup_<date>_<time>.zip for the projects and task_<task_name>_backup_<date>_<time>.zip for the tasks.

If you want to save a backup to a specific attached cloud storage, you should additionally turn off the switch Use default settings, select the Cloud storage value in the Target storage and select this storage in the list of the attached cloud storages.

Create backup APIs

• endpoints:
  • /tasks/{id}/backup
  • /projects/{id}/backup
• method: GET
• responses: 202, 201 with zip archive payload

Upload backup APIs

• endpoints:
  • /api/tasks/backup
  • /api/projects/backup
• method: POST
• Content-Type: multipart/form-data
• responses: 202, 201 with json payload

Create from backup

To create a task or project from a backup, go to the tasks or projects page, click the Create from backup button and select the archive you need.

As a result, you’ll get a task containing data, parameters, and annotations of the previously exported task.

Backup file structure

As a result, you’ll get a zip archive containing data, task or project and task specification and annotations with the following structure:

• Task Backup Structure
• Project Backup Structure

    .
    ├── data
    │   └── {user uploaded data}
    ├── task.json
    └── annotations.json
  

    .
    ├── task_{id}
    │   ├── data
    │   │   └── {user uploaded data}
    │   ├── task.json
    │   └── annotations.json
    └── project.json
  

2.23 - Frame deleting

Delete frame

You can delete the current frame from a task. This frame will not be presented either in the UI or in the exported annotation. Thus, it is possible to mark corrupted frames that are not subject to annotation.

1. Go to the Job annotation view and click on the Delete frame button (Alt+Del).

   Note: When you delete with the shortcut, the frame will be deleted immediately without additional confirmation.

   

2. After that you will be asked to confirm frame deleting.

   Note: all annotations from that frame will be deleted, unsaved annotations will be saved and the frame will be invisible in the annotation view (Until you make it visible in the settings). If there is some overlap in the task and the deleted frame falls within this interval, then this will cause this frame to become unavailable in another job as well.

3. When you delete a frame in a job with tracks, you may need to adjust some tracks manually. Common adjustments are:

   • Add keyframes at the edges of the deleted interval for the interpolation to look correct;
   • Move the keyframe start or end keyframe to the correct side of the deleted interval.

Configurate deleted frames visibility and navigation

If you need to enable showing the deleted frames, you can do it in the settings.

1. Go to the settings and chose Player settings.

   

2. Click on the Show deleted frames checkbox. And close the settings dialog.

   

3. Then you will be able to navigate through deleted frames. But annotation tools will be unavailable. Deleted frames differ in the corresponding overlay.

4. There are view ways to navigate through deleted frames without enabling this option:

   • Go to the frame via direct navigation methods: navigation slider or frame input field,
   • Go to the frame via the direct link.

5. Navigation with step will not count deleted frames.

Restore deleted frame

You can also restore deleted frames in the task.

1. Turn on deleted frames visibility, as it was told in the previous part, and go to the deleted frame you want to restore.

   

2. Click on the Restore icon. The frame will be restored immediately.

2.24 - Import datasets and upload annotation

Export dataset

You can export a dataset to a project, task or job.

1. To download the latest annotations, you have to save all changes first. Click the Save button. There is a Ctrl+S shortcut to save annotations quickly.

   

2. After that, click the Menu button. Exporting and importing of task and project datasets takes place through the Action menu.

3. Press the Export task dataset button.

   

4. Choose the format for exporting the dataset. Exporting and importing is available in:

   • Standard CVAT formats:

     • CVAT for video choose if the task is created in interpolation mode.

     • CVAT for images choose if a task is created in annotation mode.

       
       

   • And also in formats from the list of annotation formats supported by CVAT.

   • For 3D tasks, the following formats are available:

     • Kitti Raw Format 1.0
     • Sly Point Cloud Format 1.0 - Supervisely Point Cloud dataset
   
   

5. To download images with the dataset, enable the Save images option.

6. (Optional) To name the resulting archive, use the Custom name field.

7. You can choose a storage for dataset export by selecting a target storage Local or Cloud storage. The default settings are the settings that had been selected when the project was created (for example, if you specified a local storage when you created the project, then by default, you will be prompted to export the dataset to your PC). You can find out the default value by hovering the mouse over the ?. Learn more about attach cloud storage.

Import dataset

You can import dataset only to a project. In this case, the data will be split into subsets. To import a dataset, do the following on the Project page:

• Open the Actions menu.
• Press the Import dataset button.
• Select the dataset format (if you did not specify a custom name during export, the format will be in the archive name).
• Drag the file to the file upload area or click on the upload area to select the file through the explorer.

• You can also import a dataset from an attached cloud storage. Here you should select the annotation format, then select a cloud storage from the list or use default settings if you have already specified required cloud storage for task or project and specify a zip archive to the text field File name.

During the import process, you will be able to track the progress of the import.

Upload annotations

In the task or job you can upload an annotation. For this select the item Upload annotation in the menu Action of the task or in the job Menu on the Top panel select the format in which you plan to upload the annotation and select the annotation file or archive via explorer.

Or you can also use the attached cloud storage to upload the annotation file.

2.25 - Export annotations and data from CVAT

In CVAT, you have the option to export data in various formats. The choice of export format depends on the type of annotation as well as the intended future use of the dataset.

See:

• Data export formats
• Exporting dataset in CVAT
  • Exporting dataset from Task
  • Exporting dataset from Job
• Data export video tutorial

Data export formats

The table below outlines the available formats for data export in CVAT.

Exporting dataset in CVAT

Exporting dataset from Task

To export the dataset from the task, follow these steps:

1. Open Task.

2. Go to Actions > Export task dataset.

3. Choose the desired format from the list of available options.

4. (Optional) Toggle the Save images switch if you wish to include images in the export.

   Note: The Save images option is a paid feature.

   

5. Input a name for the resulting .zip archive.

6. Click OK to initiate the export.

Exporting dataset from Job

To export a dataset from Job follow these steps:

1. Navigate to Menu > Export job dataset.

   

2. Choose the desired format from the list of available options.

3. (Optional) Toggle the Save images switch if you wish to include images in the export.

   Note: The Save images option is a paid feature.

   

4. Input a name for the resulting .zip archive.

5. Click OK to initiate the export.

Data export video tutorial

For more information on the process, see the following tutorial:

2.25.1 -

This is CVAT’s native annotation format, which fully supports all of CVAT’s annotation features. It is ideal for creating data backups.

For more information, see:

• Format specification
• Dataset examples

CVAT for image export

For export of images:

• Supported annotations: Bounding Boxes, Polygons, Polylines, Points, Cuboids, Skeletons, Tags, Tracks
• Attributes: Supported.
• Tracks: Supported (tracks are split by frames).

The downloaded file is a zip archive with following structure:

taskname.zip/
├── images/
|   ├── img1.png
|   └── img2.jpg
└── annotations.xml

CVAT for video export

For export of images:

• Supported annotations: Bounding Boxes, Polygons, Polylines, Points, Cuboids, Skeletons, Tags, Tracks
• Attributes: Supported.
• Tracks: Supported (tracks are split by frames).
• Shapes are exported as single-frame tracks

Downloaded file is a zip archive with following structure:

taskname.zip/
├── images/
|   ├── frame_000000.png
|   └── frame_000001.png
└── annotations.xml

CVAT loader

Uploaded file: either an XML file or a .zip file containing the aforementioned structures.

2.25.2 -

Datumaro serves as a versatile format capable of handling complex dataset and annotation transformations, format conversions, dataset statistics, and merging, among other features. It functions as the dataset support provider within CVAT. Essentially, anything you can do in CVAT, you can also achieve in Datumaro, but with the added benefit of specialized dataset operations.

For more information, see:

• Datumaro specification

Export annotations in Datumaro format

For export of images: any 2D shapes, tags

• Supported annotations: Bounding Boxes, Polygons.
• Attributes: Supported.
• Tracks: Supported.

The downloaded file is a zip archive with the following structure:

taskname.zip/
├── annotations/
│   └── default.json # fully description of classes and all dataset items
└── images/ # if the option `save images` was selected
    └── default
        ├── image1.jpg
        ├── image2.jpg
        ├── ...

Import annotations in Datumaro format

• supported annotations: any 2D shapes, labels
• supported attributes: any

Uploaded file: a zip archive of the following structure:

<archive_name>.zip/
└── annotations/
    ├── subset1.json # fully description of classes and all dataset items
    └── subset2.json # fully description of classes and all dataset items

JSON annotations files in the annotations directory should have similar structure:

{
  "info": {},
  "categories": {
    "label": {
      "labels": [
        {
          "name": "label_0",
          "parent": "",
          "attributes": []
        },
        {
          "name": "label_1",
          "parent": "",
          "attributes": []
        }
      ],
      "attributes": []
    }
  },
  "items": [
    {
      "id": "img1",
      "annotations": [
        {
          "id": 0,
          "type": "polygon",
          "attributes": {},
          "group": 0,
          "label_id": 1,
          "points": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
          "z_order": 0
        },
        {
          "id": 1,
          "type": "bbox",
          "attributes": {},
          "group": 1,
          "label_id": 0,
          "z_order": 0,
          "bbox": [1.0, 2.0, 3.0, 4.0]
        },
        {
          "id": 2,
          "type": "mask",
          "attributes": {},
          "group": 1,
          "label_id": 0,
          "rle": {
            "counts": "d0d0:F\\0",
            "size": [10, 10]
          },
          "z_order": 0
        }
      ]
    }
  ]
}

2.25.3 -

The LabelMe format is often used for image segmentation tasks in computer vision. While it may not be specifically tied to any particular models, it’s designed to be versatile and can be easily converted to formats that are compatible with popular frameworks like TensorFlow or PyTorch.

For more information, see:

• LabelMe
• Dataset examples

LabelMe export

For export of images:

• Supported annotations: Bounding Boxes, Polygons.
• Attributes: Supported for Polygons.
• Tracks: Not supported.

The downloaded file is a .zip archive with the following structure:

taskname.zip/
├── img1.jpg
└── img1.xml

LabelMe import

Uploaded file: a zip archive of the following structure:

taskname.zip/
├── Masks/
|   ├── img1_mask1.png
|   └── img1_mask2.png
├── img1.xml
├── img2.xml
└── img3.xml

• supported annotations: Rectangles, Polygons, Masks (as polygons)

2.25.4 -

The MOT (Multiple Object Tracking) sequence format is widely used for evaluating multi-object tracking algorithms, particularly in the domains of pedestrian tracking, vehicle tracking, and more. The MOT sequence format essentially contains frames of video along with annotations that specify object locations and identities over time.

For more information, see:

• MOT sequence paper
• Dataset examples

MOT export

For export of images and videos:

• Supported annotations: Bounding Boxes, Tracks.
• Attributes: visibility (number), ignored (checkbox)
• Tracks: Supported.

The downloaded file is a .zip archive with the following structure:

taskname.zip/
├── img1/
|   ├── image1.jpg
|   └── image2.jpg
└── gt/
    ├── labels.txt
    └── gt.txt

# labels.txt
cat
dog
person
...

# gt.txt
# frame_id, track_id, x, y, w, h, "not ignored", class_id, visibility, <skipped>
1,1,1363,569,103,241,1,1,0.86014
...

MOT import

Uploaded file: a zip archive of the structure above or:

taskname.zip/
├── labels.txt # optional, mandatory for non-official labels
└── gt.txt

• supported annotations: Rectangle tracks

2.25.5 -

The MOT (Multiple Object Tracking) sequence format is widely used for evaluating multi-object tracking algorithms, particularly in the domains of pedestrian tracking, vehicle tracking, and more. The MOT sequence format essentially contains frames of video along with annotations that specify object locations and identities over time.

This version encoded as .png. Supports masks.

For more information, see:

• MOTS PNG Specification
• Dataset examples

MOTS PNG export

For export of images and videos:

• Supported annotations: Bounding Boxes, Tracks.
• Attributes: visibility (number), ignored (checkbox).
• Tracks: Supported.

The downloaded file is a .zip archive with the following structure:

taskname.zip/
└── <any_subset_name>/
    |   images/
    |   ├── image1.jpg
    |   └── image2.jpg
    └── instances/
        ├── labels.txt
        ├── image1.png
        └── image2.png

# labels.txt
cat
dog
person
...

• supported annotations: Rectangle and Polygon tracks

MOTS PNG import

Uploaded file: a zip archive of the structure above

• supported annotations: Polygon tracks

2.25.6 -

A widely-used machine learning structure, the COCO dataset is instrumental for tasks involving object identification and image segmentation. This format is compatible with projects that employ bounding boxes or polygonal image annotations.

For more information, see:

• COCO Object Detection site
• Format specification
• Dataset examples

COCO export

For export of images and videos:

• Supported annotations: Bounding Boxes, Polygons.
• Attributes:
  • is_crowd This can either be a checkbox or an integer (with values of 0 or 1). It indicates that the instance (or group of objects) should include an RLE-encoded mask in the segmentation field. All shapes within the group coalesce into a single, overarching mask, with the largest shape setting the properties for the entire object group.
  • score: This numerical field represents the annotation score.
  • Arbitrary attributes: These will be stored within the attributes section of the annotation.
• Tracks: Not supported.

The downloaded file is a .zip archive with the following structure:

archive.zip/
├── images/
│   ├── train/
│   │   ├── <image_name1.ext>
│   │   ├── <image_name2.ext>
│   │   └── ...
│   └── val/
│       ├── <image_name1.ext>
│       ├── <image_name2.ext>
│       └── ...
└── annotations/
   ├── <task>_<subset_name>.json
   └── ...

When exporting a dataset from a Project, subset names will mirror those used within the project itself. Otherwise, a singular default subset will be created to house all the dataset information. The section aligns with one of the specific COCO tasks, such as instances, panoptic, image_info, labels, captions, or stuff.

COCO import

Uplod format: a single unpacked *.json or a zip archive with the structure described above or here (without images).

• supported annotations: Polygons, Rectangles (if the segmentation field is empty)
• supported tasks: instances, person_keypoints (only segmentations will be imported), panoptic

How to create a task from MS COCO dataset

1. Download the MS COCO dataset.

   For example val images and instances annotations

2. Create a CVAT task with the following labels:

   person bicycle car motorcycle airplane bus train truck boat "traffic light" "fire hydrant" "stop sign" "parking meter" bench bird cat dog horse sheep cow elephant bear zebra giraffe backpack umbrella handbag tie suitcase frisbee skis snowboard "sports ball" kite "baseball bat" "baseball glove" skateboard surfboard "tennis racket" bottle "wine glass" cup fork knife spoon bowl banana apple sandwich orange broccoli carrot "hot dog" pizza donut cake chair couch "potted plant" bed "dining table" toilet tv laptop mouse remote keyboard "cell phone" microwave oven toaster sink refrigerator book clock vase scissors "teddy bear" "hair drier" toothbrush
   

3. Select val2017.zip as data (See Creating an annotation task guide for details)

4. Unpack annotations_trainval2017.zip

5. click Upload annotation button, choose COCO 1.1 and select instances_val2017.json annotation file. It can take some time.

2.25.7 -

The COCO Keypoints format is designed specifically for human pose estimation tasks, where the objective is to identify and localize body joints (keypoints) on a human figure within an image.

This specialized format is used with a variety of state-of-the-art models focused on pose estimation.

For more information, see:

• COCO Keypoint site
• Format specification
• Example of the archive

COCO Keypoints export

For export of images:

• Supported annotations: Skeletons
• Attributes:
  • is_crowd This can either be a checkbox or an integer (with values of 0 or 1). It indicates that the instance (or group of objects) should include an RLE-encoded mask in the segmentation field. All shapes within the group coalesce into a single, overarching mask, with the largest shape setting the properties for the entire object group.
  • score: This numerical field represents the annotation score.
  • Arbitrary attributes: These will be stored within the attributes section of the annotation.
• Tracks: Not supported.

Downloaded file is a .zip archive with the following structure:

archive.zip/
├── images/
│
│   ├── <image_name1.ext>
│   ├── <image_name2.ext>
│   └── ...
├──<annotations>.xml

COCO import

Uploaded file: a single unpacked *.json or a zip archive with the structure described here (without images).

• supported annotations: Skeletons

person_keypoints,

Support for COCO tasks via Datumaro is described here For example, support for COCO keypoints over Datumaro:

1. Install Datumaro pip install datumaro
2. Export the task in the Datumaro format, unzip
3. Export the Datumaro project in coco / coco_person_keypoints formats datum export -f coco -p path/to/project [-- --save-images]

This way, one can export CVAT points as single keypoints or keypoint lists (without the visibility COCO flag).

2.25.8 -

he Pascal VOC (Visual Object Classes) format is one of the earlier established benchmarks for object classification and detection, which provides a standardized image data set for object class recognition.

The export data format is XML-based and has been widely adopted in computer vision tasks.

For more information, see:

• Pascal VOC
• Format specification
• Dataset examples

Pascal VOC export

For export of images:

• Supported annotations: Bounding Boxes (detection), Tags (classification), Polygons (segmentation)
• Attributes:
  • occluded as both UI option and a separate attribute.
  • truncated and difficult must be defined for labels as checkbox.
  • Arbitrary attributes in the attributes section of XML files.
• Tracks: Not supported.

The downloaded file is a .zip archive with the following structure:

taskname.zip/
├── JPEGImages/
│   ├── <image_name1>.jpg
│   ├── <image_name2>.jpg
│   └── <image_nameN>.jpg
├── Annotations/
│   ├── <image_name1>.xml
│   ├── <image_name2>.xml
│   └── <image_nameN>.xml
├── ImageSets/
│   └── Main/
│       └── default.txt
└── labelmap.txt

# labelmap.txt
# label : color_rgb : 'body' parts : actions
background:::
aeroplane:::
bicycle:::
bird:::

Pascal VOC import

Supported attributes: action attributes (import only, should be defined as checkbox -es)

Uploaded file: a zip archive of the structure declared above or the following:

taskname.zip/
├── <image_name1>.xml
├── <image_name2>.xml
└── <image_nameN>.xml

It must be possible for CVAT to match the frame name and file name from annotation .xml file (the filename tag, e. g. <filename>2008_004457.jpg</filename> ).

There are 2 options:

1. full match between frame name and file name from annotation .xml (in cases when task was created from images or image archive).

2. match by frame number. File name should be <number>.jpg or frame_000000.jpg. It should be used when task was created from video.

How to create a task from Pascal VOC dataset

1. Download the Pascal Voc dataset (Can be downloaded from the PASCAL VOC website)

2. Create a CVAT task with the following labels:

   aeroplane bicycle bird boat bottle bus car cat chair cow diningtable
   dog horse motorbike person pottedplant sheep sofa train tvmonitor
   

   You can add ~checkbox=difficult:false ~checkbox=truncated:false attributes for each label if you want to use them.

   Select interesting image files (See Creating an annotation task guide for details)

3. zip the corresponding annotation files

4. click Upload annotation button, choose Pascal VOC ZIP 1.1

   and select the zip file with annotations from previous step. It may take some time.

2.25.9 -

Segmentation masks format is often used in the training of models for tasks like semantic segmentation, instance segmentation, and panoptic segmentation.

Segmentation Mask in CVAT is a format created by CVAT engineers inside the Pascal VOC

Segmentation mask export

For export of images:

• Supported annotations: Bounding Boxes, Polygons.
• Attributes: Not supported.
• Tracks: Not supported.

The downloaded file is a .zip archive with the following structure:

taskname.zip/
├── labelmap.txt # optional, required for non-VOC labels
├── ImageSets/
│   └── Segmentation/
│       └── default.txt # list of image names without extension
├── SegmentationClass/ # merged class masks
│   ├── image1.png
│   └── image2.png
└── SegmentationObject/ # merged instance masks
    ├── image1.png
    └── image2.png

# labelmap.txt
# label : color (RGB) : 'body' parts : actions
background:0,128,0::
aeroplane:10,10,128::
bicycle:10,128,0::
bird:0,108,128::
boat:108,0,100::
bottle:18,0,8::
bus:12,28,0::

The mask is a png image that can have either 1 or 3 channels. Each pixel in the image has a color that corresponds to a specific label. The colors are generated according to the Pascal VOC algorithm. By default, the color (0, 0, 0) is used to represent the background.

Segmentation mask import

Uploaded file: a zip archive of the following structure:

  taskname.zip/
  ├── labelmap.txt # optional, required for non-VOC labels
  ├── ImageSets/
  │   └── Segmentation/
  │       └── <any_subset_name>.txt
  ├── SegmentationClass/
  │   ├── image1.png
  │   └── image2.png
  └── SegmentationObject/
      ├── image1.png
      └── image2.png

It is also possible to import grayscale (1-channel) PNG masks. For grayscale masks provide a list of labels with the number of lines equal to the maximum color index on images. The lines must be in the right order so that line index is equal to the color index. Lines can have arbitrary, but different, colors. If there are gaps in the used color indices in the annotations, they must be filled with arbitrary dummy labels. Example:

q:0,128,0:: # color index 0
aeroplane:10,10,128:: # color index 1
_dummy2:2,2,2:: # filler for color index 2
_dummy3:3,3,3:: # filler for color index 3
boat:108,0,100:: # color index 3
...
_dummy198:198,198,198:: # filler for color index 198
_dummy199:199,199,199:: # filler for color index 199
...
the last label:12,28,0:: # color index 200

• supported shapes: Polygons

2.25.10 -

models, which are designed for real-time object detection tasks. However, the format can also be adapted for use with other object detection models.

For more information, see:

• YOLO Specification
• Format specification
• Dataset examples

YOLO export

For export of images:

• Supported annotations: Bounding Boxes.
• Attributes: Not supported.
• Tracks: Not supported.

The downloaded file is a .zip archive with the following structure:

archive.zip/
├── obj.data
├── obj.names
├── obj_<subset>_data
│   ├── image1.txt
│   └── image2.txt
└── train.txt # list of subset image paths

# the only valid subsets are: train, valid
# train.txt and valid.txt:
obj_<subset>_data/image1.jpg
obj_<subset>_data/image2.jpg

# obj.data:
classes = 3 # optional
names = obj.names
train = train.txt
valid = valid.txt # optional
backup = backup/ # optional

# obj.names:
cat
dog
airplane

# image_name.txt:
# label_id - id from obj.names
# cx, cy - relative coordinates of the bbox center
# rw, rh - relative size of the bbox
# label_id cx cy rw rh
1 0.3 0.8 0.1 0.3
2 0.7 0.2 0.3 0.1

Each annotation file, with the .txt extension, is named to correspond with its associated image file.

For example, frame_000001.txt serves as the annotation for the frame_000001.jpg image.

The structure of the .txt file is as follows: each line describes a label and a bounding box in the format label_id cx cy w h. The file obj.names contains an ordered list of label names.

YOLO import

Uploaded file: a zip archive of the same structure as above It must be possible to match the CVAT frame (image name) and annotation file name. There are 2 options:

1. full match between image name and name of annotation *.txt file (in cases when a task was created from images or archive of images).

2. match by frame number (if CVAT cannot match by name). File name should be in the following format <number>.jpg . It should be used when task was created from a video.

How to create a task from YOLO formatted dataset (from VOC for example)

1. Follow the official guide(see Training YOLO on VOC section) and prepare the YOLO formatted annotation files.

2. Zip train images

zip images.zip -j -@ < train.txt

1. Create a CVAT task with the following labels:

   aeroplane bicycle bird boat bottle bus car cat chair cow diningtable dog
   horse motorbike person pottedplant sheep sofa train tvmonitor
   

   Select images. zip as data. Most likely you should use share functionality because size of images. zip is more than 500Mb. See Creating an annotation task guide for details.

2. Create obj.names with the following content:

   aeroplane
   bicycle
   bird
   boat
   bottle
   bus
   car
   cat
   chair
   cow
   diningtable
   dog
   horse
   motorbike
   person
   pottedplant
   sheep
   sofa
   train
   tvmonitor
   

3. Zip all label files together (we need to add only label files that correspond to the train subset)

   cat train.txt | while read p; do echo ${p%/*/*}/labels/${${p##*/}%%.*}.txt; done | zip labels.zip -j -@ obj.names
   

4. Click Upload annotation button, choose YOLO 1.1 and select the zip

   file with labels from the previous step.

2.25.11 -

The TFRecord format is tightly integrated with TensorFlow and is commonly used for training models within the TensorFlow ecosystem.

TFRecord is an incredibly flexible data format. We strive to align our implementation with the format employed by the TensorFlow Object Detection API, making only minimal changes as necessary.

For more information, see:

• TFRecord
• Dataset examples

This format does not have a fixed structure, so in CVAT the following structure is used:

image_feature_description = {
    'image/filename': tf.io.FixedLenFeature([], tf.string),
    'image/source_id': tf.io.FixedLenFeature([], tf.string),
    'image/height': tf.io.FixedLenFeature([], tf.int64),
    'image/width': tf.io.FixedLenFeature([], tf.int64),
    # Object boxes and classes.
    'image/object/bbox/xmin': tf.io.VarLenFeature(tf.float32),
    'image/object/bbox/xmax': tf.io.VarLenFeature(tf.float32),
    'image/object/bbox/ymin': tf.io.VarLenFeature(tf.float32),
    'image/object/bbox/ymax': tf.io.VarLenFeature(tf.float32),
    'image/object/class/label': tf.io.VarLenFeature(tf.int64),
    'image/object/class/text': tf.io.VarLenFeature(tf.string),
}

TFRecord export

For export of images:

• Supported annotations: Bounding Boxes, Polygons (as masks, manually over Datumaro)
• Attributes: Not supported.
• Tracks: Not supported.

The downloaded file is a .zip archive with the following structure:

taskname.zip/
├── default.tfrecord
└── label_map.pbtxt

# label_map.pbtxt
item {
	id: 1
	name: 'label_0'
}
item {
	id: 2
	name: 'label_1'
}
...

How to export masks:

1. Export annotations in Datumaro format.

2. Apply polygons_to_masks and boxes_to_masks transforms:

   datum transform -t polygons_to_masks -p path/to/proj -o ptm
   datum transform -t boxes_to_masks -p ptm -o btm
   

3. Export in the TF Detection API format:

   datum export -f tf_detection_api -p btm [-- --save-images]
   

TFRecord import

Uploaded file: a zip archive of following structure:

taskname.zip/
└── <any name>.tfrecord

• supported annotations: Rectangles

How to create a task from TFRecord dataset (from VOC2007 for example)

1. Create label_map.pbtxt file with the following content:

item {
    id: 1
    name: 'aeroplane'
}
item {
    id: 2
    name: 'bicycle'
}
item {
    id: 3
    name: 'bird'
}
item {
    id: 4
    name: 'boat'
}
item {
    id: 5
    name: 'bottle'
}
item {
    id: 6
    name: 'bus'
}
item {
    id: 7
    name: 'car'
}
item {
    id: 8
    name: 'cat'
}
item {
    id: 9
    name: 'chair'
}
item {
    id: 10
    name: 'cow'
}
item {
    id: 11
    name: 'diningtable'
}
item {
    id: 12
    name: 'dog'
}
item {
    id: 13
    name: 'horse'
}
item {
    id: 14
    name: 'motorbike'
}
item {
    id: 15
    name: 'person'
}
item {
    id: 16
    name: 'pottedplant'
}
item {
    id: 17
    name: 'sheep'
}
item {
    id: 18
    name: 'sofa'
}
item {
    id: 19
    name: 'train'
}
item {
    id: 20
    name: 'tvmonitor'
}

1. Use create_pascal_tf_record.py

to convert VOC2007 dataset to TFRecord format. As example:

python create_pascal_tf_record.py --data_dir <path to VOCdevkit> --set train --year VOC2007 --output_path pascal.tfrecord --label_map_path label_map.pbtxt

1. Zip train images

   cat <path to VOCdevkit>/VOC2007/ImageSets/Main/train.txt | while read p; do echo <path to VOCdevkit>/VOC2007/JPEGImages/${p}.jpg  ; done | zip images.zip -j -@
   

2. Create a CVAT task with the following labels:

   aeroplane bicycle bird boat bottle bus car cat chair cow diningtable dog horse motorbike person pottedplant sheep sofa train tvmonitor
   

   Select images. zip as data. See Creating an annotation task guide for details.

3. Zip pascal.tfrecord and label_map.pbtxt files together

   zip anno.zip -j <path to pascal.tfrecord> <path to label_map.pbtxt>
   

4. Click Upload annotation button, choose TFRecord 1.0 and select the zip file

   with labels from the previous step. It may take some time.

2.25.12 -

The ImageNet is typically used for a variety of computer vision tasks, including but not limited to image classification, object detection, and segmentation.

It is widely recognized and used in the training and benchmarking of various machine learning models.

For more information, see:

• ImageNet site
• Dataset examples

ImageNet export

For export of images:

• Supported annotations: Tags.
• Attributes: Not supported.
• Tracks: Not supported.

The downloaded file is a .zip archive with the following structure:

# if we save images:
taskname.zip/
├── label1/
|   ├── label1_image1.jpg
|   └── label1_image2.jpg
└── label2/
    ├── label2_image1.jpg
    ├── label2_image3.jpg
    └── label2_image4.jpg

# if we keep only annotation:
taskname.zip/
├── <any_subset_name>.txt
└── synsets.txt

ImageNet import

Uploaded file: a zip archive of the structure above

• supported annotations: Labels

2.25.13 -

he WIDER Face dataset is widely used for face detection tasks. Many popular models for object detection and face detection specifically are trained on this dataset for benchmarking and deployment.

For more information, see:

• WIDER Face Specification
• Dataset examples

WIDER Face export

For export of images:

• Supported annotations: Bounding Boxes (with attributes), Tags.
• Attributes:
  • blur, expression, illumination, pose, invalid
  • occluded (both the annotation property & an attribute).
• Tracks: Not supported.

The downloaded file is a .zip archive with the following structure:

taskname.zip/
├── labels.txt # optional
├── wider_face_split/
│   └── wider_face_<any_subset_name>_bbx_gt.txt
└── WIDER_<any_subset_name>/
    └── images/
        ├── 0--label0/
        │   └── 0_label0_image1.jpg
        └── 1--label1/
            └── 1_label1_image2.jpg

WIDER Face import

Uploaded file: a zip archive of the structure above

• supported annotations: Rectangles (with attributes), Labels
• supported attributes:
  • blur, expression, illumination, occluded, pose, invalid

2.25.14 -

The CamVid (Cambridge-driving Labeled Video Database) format is most commonly used in the realm of semantic segmentation tasks. It is particularly useful for training and evaluating models for autonomous driving and other vision-based robotics applications.

For more information, see:

• CamVid Specification
• Dataset examples

CamVid export

For export of images and videos:

• Supported annotations: Bounding Boxes, Polygons.
• Attributes: Not supported.
• Tracks: Not supported.

The downloaded file is a .zip archive with the following structure:

taskname.zip/
├── label_colors.txt # optional, required for non-CamVid labels
├── <any_subset_name>/
|   ├── image1.png
|   └── image2.png
├── <any_subset_name>annot/
|   ├── image1.png
|   └── image2.png
└── <any_subset_name>.txt

# label_colors.txt (with color value type)
# if you want to manually set the color for labels, configure label_colors.txt as follows:
# color (RGB) label
0 0 0 Void
64 128 64 Animal
192 0 128 Archway
0 128 192 Bicyclist
0 128 64 Bridge

# label_colors.txt (without color value type)
# if you do not manually set the color for labels, it will be set automatically:
# label
Void
Animal
Archway
Bicyclist
Bridge

A mask in the CamVid dataset is typically a .png image with either one or three channels.

In this image, each pixel is assigned a specific color that corresponds to a particular label.

By default, the color (0, 0, 0)—or black—is used to represent the background.

CamVid import

For import of images:

• Uploaded file: a .zip archive of the structure above
• supported annotations: Polygons

2.25.15 -

The VGGFace2 is primarily designed for face recognition tasks and is most commonly used with deep learning models specifically designed for face recognition, verification, and similar tasks.

For more information, see:

• VGGFace2 Github
• Dataset examples

VGGFace2 export

For export of images:

• Supported annotations: Bounding Boxes, Points (landmarks - groups of 5 points).
• Attributes: Not supported.
• Tracks: Not supported.

The downloaded file is a .zip archive with the following structure:

taskname.zip/
├── labels.txt # optional
├── <any_subset_name>/
|   ├── label0/
|   |   └── image1.jpg
|   └── label1/
|       └── image2.jpg
└── bb_landmark/
    ├── loose_bb_<any_subset_name>.csv
    └── loose_landmark_<any_subset_name>.csv
# labels.txt
# n000001 car
label0 <class0>
label1 <class1>

VGGFace2 import

Uploaded file: a zip archive of the structure above

• supported annotations: Rectangles, Points (landmarks - groups of 5 points)

2.25.16 -

The Market-1501 dataset is widely used for person re-identification tasks. It is a challenging dataset that has gained significant attention in the computer vision community.

For more information, see:

• Market-1501
• Dataset examples

Market-1501 export

For export of images:

• Supported annotations: Bounding Boxes
• Attributes: query (checkbox), person_id (number), camera_id(number).
• Tracks: Not supported.

Th downloaded file is a .zip archive with the following structure:

taskname.zip/
├── bounding_box_<any_subset_name>/
│   └── image_name_1.jpg
└── query
    ├── image_name_2.jpg
    └── image_name_3.jpg
# if we keep only annotation:
taskname.zip/
└── images_<any_subset_name>.txt
# images_<any_subset_name>.txt
query/image_name_1.jpg
bounding_box_<any_subset_name>/image_name_2.jpg
bounding_box_<any_subset_name>/image_name_3.jpg
# image_name = 0001_c1s1_000015_00.jpg
0001 - person id
c1 - camera id (there are totally 6 cameras)
s1 - sequence
000015 - frame number in sequence
00 - means that this bounding box is the first one among the several

Market-1501 import

Uploaded file: a zip archive of the structure above

• supported annotations: Label market-1501 with attributes (query, person_id, camera_id)

2.25.17 -

ICDAR 13/15 formats are typically used for text detection and recognition tasks and OCR (Optical Character Recognition).

These formats are usually paired with specialized text detection and recognition models.

For more information, see:

• ICDAR13/15
• Dataset examples

ICDAR13/15 export

For export of images:

• ICDAR Recognition 1.0 (Text recognition):
  • Supported annotations: Tag icdar
  • Attributes: caption.
• ICDAR Detection 1.0 (Text detection):
  • Supported annotations: Bounding Boxes, Polygons with lavel icdar added in constructor.
  • Attributes: text.
• ICDAR Segmentation 1.0 (Text segmentation):
  • Supported annotations: Bounding Boxes, Polygons with label icdar added in constructor.
  • Attributes: index, text, color, center
• Tracks: Not supported.

The downloaded file is a .zip archive with the following structure:

# text recognition task
taskname.zip/
└── word_recognition/
    └── <any_subset_name>/
        ├── images
        |   ├── word1.png
        |   └── word2.png
        └── gt.txt
# text localization task
taskname.zip/
└── text_localization/
    └── <any_subset_name>/
        ├── images
        |   ├── img_1.png
        |   └── img_2.png
        ├── gt_img_1.txt
        └── gt_img_1.txt
#text segmentation task
taskname.zip/
└── text_localization/
    └── <any_subset_name>/
        ├── images
        |   ├── 1.png
        |   └── 2.png
        ├── 1_GT.bmp
        ├── 1_GT.txt
        ├── 2_GT.bmp
        └── 2_GT.txt

ICDAR13/15 import

Uploaded file: a zip archive of the structure above

Word recognition task:

• supported annotations: Label icdar with attribute caption

Text localization task:

• supported annotations: Rectangles and Polygons with label icdar and attribute text

Text segmentation task:

• supported annotations: Rectangles and Polygons with label icdar and attributes index, text, color, center

2.25.18 -

The Open Images format is based on a large-scale, diverse dataset that contains object detection, object segmentation, visual relationship, and localized narratives annotations.

Its export data format is compatible with many object detection and segmentation models.

For more information, see:

• Open Images site
• Format specification
• Dataset examples

Open Images export

For export of images:

• Supported annotations: Bounding Boxes (detection), Tags (classification), Polygons (segmentation).

• Supported attributes:

  • Tags: score must be defined for labels as text or number. The confidence level from 0 to 1.
  • Bounding boxes:
    score must be defined for labels as text or number. The confidence level from 0 to 1.
    occluded as both UI option and a separate attribute. Whether the object is occluded by another object.
    truncated must be defined for labels as checkbox. Whether the object extends beyond the boundary of the image.
    is_group_of must be defined for labels as checkbox. Whether the object represents a group of objects of the same class.
    is_depiction must be defined for labels as checkbox. Whether the object is a depiction (such as a drawing) rather than a real object.
    is_inside must be defined for labels as checkbox. Whether the object is seen from the inside.
  • Masks:
    box_id must be defined for labels as text. An identifier for the bounding box associated with the mask.
    predicted_iou must be defined for labels as text or number. Predicted IoU value with respect to the ground truth.

• Tracks: Not supported.

The downloaded file is a .zip archive with the following structure:

└─ taskname.zip/
    ├── annotations/
    │   ├── bbox_labels_600_hierarchy.json
    │   ├── class-descriptions.csv
    |   ├── images.meta  # additional file with information about image sizes
    │   ├── <subset_name>-image_ids_and_rotation.csv
    │   ├── <subset_name>-annotations-bbox.csv
    │   ├── <subset_name>-annotations-human-imagelabels.csv
    │   └── <subset_name>-annotations-object-segmentation.csv
    ├── images/
    │   ├── subset1/
    │   │   ├── <image_name101.jpg>
    │   │   ├── <image_name102.jpg>
    │   │   └── ...
    │   ├── subset2/
    │   │   ├── <image_name201.jpg>
    │   │   ├── <image_name202.jpg>
    │   │   └── ...
    |   ├── ...
    └── masks/
        ├── subset1/
        │   ├── <mask_name101.png>
        │   ├── <mask_name102.png>
        │   └── ...
        ├── subset2/
        │   ├── <mask_name201.png>
        │   ├── <mask_name202.png>
        │   └── ...
        ├── ...

Open Images import

Uploaded file: a zip archive of the following structure:

└─ upload.zip/
    ├── annotations/
    │   ├── bbox_labels_600_hierarchy.json
    │   ├── class-descriptions.csv
    |   ├── images.meta  # optional, file with information about image sizes
    │   ├── <subset_name>-image_ids_and_rotation.csv
    │   ├── <subset_name>-annotations-bbox.csv
    │   ├── <subset_name>-annotations-human-imagelabels.csv
    │   └── <subset_name>-annotations-object-segmentation.csv
    └── masks/
        ├── subset1/
        │   ├── <mask_name101.png>
        │   ├── <mask_name102.png>
        │   └── ...
        ├── subset2/
        │   ├── <mask_name201.png>
        │   ├── <mask_name202.png>
        │   └── ...
        ├── ...

Image ids in the <subset_name>-image_ids_and_rotation.csv should match with image names in the task.

2.25.19 -

The Cityscapes format is a widely-used standard in the field of computer vision, particularly for tasks involving semantic and instance segmentation in urban scenes. This dataset format typically comprises high-resolution images of cityscapes along with detailed pixel-level annotations.

Each pixel is labeled with a category such as “road,” “pedestrian,” or “vehicle,” making it a valuable resource for training and validating machine learning models aimed at understanding urban environments. It’s a go-to choice for researchers and professionals working on autonomous vehicles, robotics, and smart cities.

For more information, see:

• Cityscapes site
• Cityscapes format specification
• Cityscapes dataset examples

Cityscapes export

For export of images:

• Supported annotations: Polygons (segmentation), Bounding Boxes.
• Attributes:
  • is_crowd boolean, should be defined for labels as checkbox. Specifies if the annotation label can distinguish between different instances. If False, the annotation id field encodes the instance id.
• Tracks: Not supported.

The downloaded file is a .zip archive with the following structure:

.
├── label_color.txt
├── gtFine
│   ├── <subset_name>
│   │   └── <city_name>
│   │       ├── image_0_gtFine_instanceIds.png
│   │       ├── image_0_gtFine_color.png
│   │       ├── image_0_gtFine_labelIds.png
│   │       ├── image_1_gtFine_instanceIds.png
│   │       ├── image_1_gtFine_color.png
│   │       ├── image_1_gtFine_labelIds.png
│   │       ├── ...
└── imgsFine  # if saving images was requested
    └── leftImg8bit
        ├── <subset_name>
        │   └── <city_name>
        │       ├── image_0_leftImg8bit.png
        │       ├── image_1_leftImg8bit.png
        │       ├── ...

• label_color.txt a file that describes the color for each label

# label_color.txt example
# r g b label_name
0 0 0 background
0 255 0 tree
...

• *_gtFine_color.png class labels encoded by its color.
• *_gtFine_labelIds.png class labels are encoded by its index.
• *_gtFine_instanceIds.png class and instance labels encoded by an instance ID. The pixel values encode class and the individual instance: the integer part of a division by 1000 of each ID provides class ID, the remainder is the instance ID. If a certain annotation describes multiple instances, then the pixels have the regular ID of that class

Cityscapes annotations import

Uploaded file: a zip archive with the following structure:

.
├── label_color.txt # optional
└── gtFine
    └── <city_name>
        ├── image_0_gtFine_instanceIds.png
        ├── image_1_gtFine_instanceIds.png
        ├── ...

Creating task with Cityscapes dataset

Create a task with the labels you need or you can use the labels and colors of the original dataset. To work with the Cityscapes format, you must have a black color label for the background.

Original Cityscapes color map:

[
    {"name": "unlabeled", "color": "#000000", "attributes": []},
    {"name": "egovehicle", "color": "#000000", "attributes": []},
    {"name": "rectificationborder", "color": "#000000", "attributes": []},
    {"name": "outofroi", "color": "#000000", "attributes": []},
    {"name": "static", "color": "#000000", "attributes": []},
    {"name": "dynamic", "color": "#6f4a00", "attributes": []},
    {"name": "ground", "color": "#510051", "attributes": []},
    {"name": "road", "color": "#804080", "attributes": []},
    {"name": "sidewalk", "color": "#f423e8", "attributes": []},
    {"name": "parking", "color": "#faaaa0", "attributes": []},
    {"name": "railtrack", "color": "#e6968c", "attributes": []},
    {"name": "building", "color": "#464646", "attributes": []},
    {"name": "wall", "color": "#66669c", "attributes": []},
    {"name": "fence", "color": "#be9999", "attributes": []},
    {"name": "guardrail", "color": "#b4a5b4", "attributes": []},
    {"name": "bridge", "color": "#966464", "attributes": []},
    {"name": "tunnel", "color": "#96785a", "attributes": []},
    {"name": "pole", "color": "#999999", "attributes": []},
    {"name": "polegroup", "color": "#999999", "attributes": []},
    {"name": "trafficlight", "color": "#faaa1e", "attributes": []},
    {"name": "trafficsign", "color": "#dcdc00", "attributes": []},
    {"name": "vegetation", "color": "#6b8e23", "attributes": []},
    {"name": "terrain", "color": "#98fb98", "attributes": []},
    {"name": "sky", "color": "#4682b4", "attributes": []},
    {"name": "person", "color": "#dc143c", "attributes": []},
    {"name": "rider", "color": "#ff0000", "attributes": []},
    {"name": "car", "color": "#00008e", "attributes": []},
    {"name": "truck", "color": "#000046", "attributes": []},
    {"name": "bus", "color": "#003c64", "attributes": []},
    {"name": "caravan", "color": "#00005a", "attributes": []},
    {"name": "trailer", "color": "#00006e", "attributes": []},
    {"name": "train", "color": "#005064", "attributes": []},
    {"name": "motorcycle", "color": "#0000e6", "attributes": []},
    {"name": "bicycle", "color": "#770b20", "attributes": []},
    {"name": "licenseplate", "color": "#00000e", "attributes": []}
]

Upload images when creating a task:

images.zip/
    ├── image_0.jpg
    ├── image_1.jpg
    ├── ...

After creating the task, upload the Cityscapes annotations as described in the previous section.

2.25.20 -

The KITTI format is widely used for a range of computer vision tasks related to autonomous driving, including but not limited to 3D object detection, multi-object tracking, and scene flow estimation. Given its special focus on automotive scenes, the KITTI format is generally used with models that are designed or adapted for these types of tasks.

For more information, see:

• KITTI site
• Format specification for KITTI detection
• Format specification for KITTI segmentation
• Dataset examples

KITTI annotations export

For export of images:

• Supported annotations: Bounding Boxes (detection), Polygons (segmentation).
• Supported attributes:
  • occluded (Available both as a UI option and a separate attribute) Denotes that a major portion of the object within the bounding box is obstructed by another object.
  • truncated (Only applicable to bounding boxes) Must be represented as checkboxes for labels. Suggests that the bounding box does not encompass the entire object; some part is cut off.
  • is_crowd (Only valid for polygons). Should be indicated using checkboxes for labels. Signifies that the annotation encapsulates multiple instances of the same object class.
• Tracks: Not supported.