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Advanced

This section contains advanced documents for CVAT users

1 - Projects page

Creating and exporting projects in CVAT.

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

Properties Supported values Description
Assignee username Assignee is the user who is working on the project, task or job.
(is specified on task page)
Owner username The user who owns the project, task, or job
Last updated last modified date and time (or value range) The date can be entered in the dd.MM.yyyy HH:mm format
or by selecting the date in the window that appears
when you click on the input field
ID number or range of job ID
Name name On the tasks page - name of the task,
on the project page - name of the project

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 - Organization

Using organization in CVAT.

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

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.

Field Description
Short name A name of the organization that will be displayed in the CVAT menu.
Full Name Optional. Full name of the organization.
Description Optional. Description of organization.
Email Optional. Your email.
Phone number Optional. Your phone number.
Location Optional. Organization address.

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: menu and roles

Invite members form is available from Organization page.

It has the following fields:

Field Description
Email Specifies the email address of the user who is being added to the Organization.
Role drop-down list Defines the role of the user which sets the level of access within the Organization:
  • Worker: Has access only to the tasks, projects, and jobs assigned to them.
  • Supervisor: Can create and assign jobs, tasks, and projects to the Organization members.
  • Maintainer: Has the same capabilities as the Supervisor, but with additional visibility over all tasks and projects created by other members, complete access to Cloud Storages, and the ability to modify members and their roles.
  • Owner: role assigned to the creator of the organization by default. Has maximum capabilities and cannot be changed or assigned to the other user.
    		•
    Invite more Button to add another user to the Organization.

    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.

    Inviting members to Organization

    To invite 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.

    4. The person being invited will receive an email with the link.

    5. Person must click the link and:

      1. If the invitee does not have the CVAT account, then set up an account.
      2. If the invitee has a CVAT account, then log in to the account.

    Invitations list

    User can see the list of active invitations.

    To see the list, Go to Username > Organization > Invitations.

    You will see the page with the list of invitations.

    You will also see pop-up notification the link to the page with invitations list.

    Resending and removing invitations

    The organization owner and maintainers can remove members, by clicking on the three dots, and selecting Remove invitation

    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.

    3 - Search

    Overview of available search options.

    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.

    4 - Shape mode (advanced)

    Advanced operations available during annotation in shape mode.

    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.

    5 - Single Shape

    Guide to annotating tasks using Single Shape mode

    The CVAT Single Shape annotation mode accelerates the annotation process and enhances workflow efficiency for specific scenarios.

    By using this mode you can label objects with a chosen annotation shape and label when an image contains only a single object. By eliminating the necessity to select tools from the sidebar and facilitating quicker navigation between images without the reliance on hotkeys, this feature makes the annotation process significantly faster.

    See:

    Single Shape mode annotation interface

    A set of controls in the interface of the Single Shape annotation mode may vary depends on different settings.

    Images below displays the complete interface, featuring all available fields; as mentioned above, certain fields may be absent depending on the scenario.

    For instance, when annotating with rectangles, the Number of points field will not appear, and if annotating a single class, the Labels selector will be omitted.

    To access Single Shape mode, open the job, navigate to the top right corner, and from the drop-down menu, select Single Shape.

    Single Shape Annotation Mode Interface

    The interface will be different if the shape type was set to Any in the label Constructor:

    Single Shape Annotation Mode Interface

    The Single Shape annotation mode has the following fields:

    Feature Explanation
    Prompt for Shape and Label Displays the selected shape and label for the annotation task, for example: “Annotate cat on the image using rectangle”.
    Skip Button Enables moving to the next frame without annotating the current one, particularly useful when the frame does not have anything to be annotated.
    List of Hints Offers guidance on using the interface effectively, including:
    - Click Skip for frames without required annotations.
    - Hold the Alt button to avoid unintentional drawing (e.g. when you want only move the image).
    - Use the Ctrl+Z combination to undo the last action if needed.
    - Use the Esc button to completely reset the current drawing progress.
    Label selector Allows for the selection of different labels (cat, or dog in our example) for annotation within the interface.
    Label type selector A drop-down list to select type of the label (rectangle, ellipse, etc). Only visible when the type of the shape is Any.
    Options to Enable or Disable Provides configurable options to streamline the annotation process, such as:
    - Automatically go to the next frame.
    - Automatically save when finish.
    - Navigate only empty frames.
    - Predefined number of points - Specific to polyshape annotations, enabling this option auto-completes a shape once a predefined number of points is reached. Otherwise, pressing N is required to finalize the shape.
    Number of Points Applicable for polyshape annotations, indicating the number of points to use for image annotation.

    Annotating in Single Shape mode

    To annotate in Single Shape mode, follow these steps:

    1. Open the job and switch to Single Shape mode.
    2. Annotate the image based on the selected shape. For more information on shapes, see Annotation Tools.
    3. (Optional) If the image does not contain any objects to annotate, click Skip at the top of the right panel.
    4. Submit your work.

    Query parameters

    Also, we introduced additional query parameters, which you may append to the job link, to initialize the annotation process and automate workflow:

    Query Parameter Possible Values Explanation
    defaultWorkspace Workspace identifier (e.g., single_shape, tags, review, attributes) Specifies the workspace to be used initially, streamlining the setup for different annotation tasks.
    defaultLabel A string representation of a label (label name) Sets a default label for the annotation session, facilitating consistency across similar tasks.
    defaultPointsCount Integer - number of points for polyshapes Defines a preset number of points for polyshape annotations, optimizing the annotation process.

    You can combine these parameters to customize the workspace for an annotator, for example:

    /tasks/<tid>/jobs/<jid>?defaultWorkspace=single_shape&defaultLabel=dog&defaultPointsCount=10

    Will open the following job:

    Query Example

    Video tutorial

    For a better understanding of how Single Shape mode operates, we recommend watching the following tutorial.

    6 - CVAT User roles

    CVAT offers two distinct types of roles:

    • Global Roles: These are universal roles that apply to the entire system. Anyone who logs into the CVAT.ai platform is automatically assigned a global role. It sets the basic permissions that every registered user has across CVAT.ai, regardless of their specific tasks or responsibilities.
    • Organization Roles: These roles determine what a user can do within the Organization, allowing for more tailored access based on the user’s specific duties and responsibilities.

    Organization roles complement global roles by determining the visibility of different resources for example, tasks or jobs.

    Limits: Limits are applicable to all users of the CVAT.ai Cloud Platform using the Free plan and can be lifted upon choosing a subscription.

    All roles are predefined and cannot be modified through the user interface. However, within the self-hosted solution, roles can be adjusted using .rego files stored in cvat/apps/*/rules/. Rego is a declarative language employed for defining OPA (Open Policy Agent) policies, and its syntax is detailed in the OPA documentation.

    Note: Once you’ve made changes to the .rego files, you must rebuild and restart the Docker Compose for those changes to be applied. In this scenario, be sure to include the docker-compose.dev.yml compose configuration file when executing the Docker Compose command.

    See:

    Global roles in CVAT.ai

    Note: Global roles can be adjusted only on self-hosted solution.

    CVAT has implemented three Global roles, categorized as user Groups. These roles are:

    Role Description
    Administrator An administrator possesses unrestricted access to the CVAT instance and all activities within this instance. The administrator has visibility over all tasks and projects, with the ability to modify or manage each comprehensively. This role is exclusive to self-hosted instances, ensuring comprehensive oversight and control.
    User
    (default role)    		 A User is a default role who is assigned to any user who is registered in CVAT*. Users can view and manage all tasks and projects within their registered accounts, but their activities are subject to specific limitations, see Free plan.

    * If a user, that did not have a CVAT account, has been invited to the organization by the organization owner or maintainer, it will be automatically assigned the Organization role and will be subject to the role’s limitations when operating within the Organization.
    Worker Workers are limited to specific functionalities and do not have the permissions to create tasks, assign roles, or perform other administrative actions. Their activities are primarily focused on viewing and interacting with the content within the boundaries of their designated roles (validation or annotation of the jobs).

    Organization roles in CVAT.ai

    Organization Roles are available only within the CVAT Organization.

    Organization Roles

    Organization roles are assigned when users are invited to the Organization.

    Organization Roles

    There are the following roles available in CVAT:

    Role Description
    Owner The Owner is the person who created the Organization. The Owner role is assigned to the creator of the organization by default. This role has maximum capabilities and cannot be changed or assigned to the other user.

    The Owner has no extra restrictions in the organization and is only limited by the chosen organization plan (see Free and Team plans).

    Owners can invite other users to the Organization and assign roles to the invited users so the team can collaborate.
    Maintainer The maintainer is the person who can invite users to organization, create and update tasks and jobs, and see all tasks within the organization. Maintainer has complete access to Cloud Storages, and the ability to modify members and their roles.
    Supervisor The supervisor is a manager role. Supervisor can create and assign jobs, tasks, and projects to the Organization members. Supervisor cannot invite new members and modify members roles.
    Worker Workers’ primary focus is actual annotation and reviews. They are limited to specific functionalities and has access only to the jobs assigned to them.

    Job Stage

    Job Stage can be assigned to any team member.

    Stages are not roles.

    Jobs can have an assigned user (with any role) and that Assignee will perform a Stage specific work which is to annotate, validate, or accept the job.

    Job stage

    Job Stage can be:

    Stage Description
    Annotation Provides access to annotation tools. Assignees will be able to see their assigned jobs and annotate them. By default, assignees with the Annotation stage cannot report annotation errors or issues.
    Validation Grants access to QA tools. Assignees will see their assigned jobs and can validate them while also reporting issues. By default, assignees with the Validation stage cannot correct errors or annotate datasets.
    Acceptance Does not grant any additional access or change the annotator’s interface. It just marks the job as done.

    Any Assignee can modify their assigned Stage specific functions via the annotation interface toolbar:

    Job stage change

    • Standard: switches interface to Annotation mode.
    • Review: switches interface to the Validation mode.

    7 - Track mode (advanced)

    Advanced operations available during annotation in track mode.

    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:

    8 - 3D Object annotation (advanced)

    Overview of advanced operations available when annotating 3D objects.

    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”.

    9 - Attribute annotation mode (advanced)

    Advanced operations available in attribute annotation mode.

    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.

    10 - Annotation with rectangles

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

    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.

    11 - Annotation with polygons

    Guide to creating and editing polygons.

    11.1 - 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.

    11.2 - 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.

    11.3 - 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.

    11.4 - 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.

    11.5 - Creating masks

    Cutting holes in polygons

    Currently, CVAT does not support cutting transparent holes in polygons. However, it is possible 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:

    The editor

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

    Exported masks:

    A class mask An instance mask

    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)
    • Datumaro

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

    A class mask An instance mask

    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.

    12 - Annotation with polylines

    Guide to annotating tasks using 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.

    13 - Annotation with points

    Guide to annotating tasks using single points or shapes containing multiple points.

    13.1 - 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.

    13.2 - 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.

    14 - Annotation with ellipses

    Guide to annotating tasks using 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.

    Annotation with ellipses video tutorial

    15 - Annotation with cuboids

    Guide to creating and editing 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.

    15.1 - 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:

    15.2 - 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

    16 - Annotation with skeletons

    Guide to annotating tasks using Skeletons

    In this guide, we delve into the efficient process of annotating complex structures through the implementation of Skeleton annotations.

    Skeletons serve as annotation templates for annotating complex objects with a consistent structure, such as human pose estimation or facial landmarks.

    A Skeleton is composed of numerous points (also referred to as elements), which may be connected by edges. Each point functions as an individual object, possessing unique attributes and properties like color, occlusion, and visibility.

    Skeletons can be exported in two formats: CVAT for image and COCO Keypoints.

    Note: that skeletons’ labels cannot be imported in a label-less project by importing a dataset. You need to define the labels manually before the import.

    See:

    Adding Skeleton manually

    To start annotating using skeletons, you need to set up a Skeleton task in Configurator:

    To open Configurator, when creating a task, click on the Setup skeleton button if you want to set up the skeleton manually, or From model if you want to add skeleton labels from a model.

    Skeleton Configurator

    The skeleton Configurator is a tool to build skeletons for annotation. It has the following fields:

    Number Name Description
    1 Upload background image (Optional) Use it to upload a background image, to draw a skeleton on top of it.
    2 Add point Use it to add Skeleton points to the Drawing area (8).
    3 Click and drag Use it to move points across the Drawing area (8).
    4 Add edge Use it to add edge on the Drawing area (8) to connect the points (2).
    5 Remove point Use it to remove points. Click on Remove point and then on any point (2) on the Drawing area (8) to delete the point.
    6 Download skeleton Use it to download created skeleton in .SVG format.
    7 Upload skeleton Use it to upload skeleton in .SVG format.
    8 Drawing area Use it as a canvas to draw a skeleton.

    Configuring Skeleton points

    You can name labels, set attributes, and change the color of each point of the skeleton.

    To do this, right-click on the skeleton point and select Configure:

    In the opened menu, you can change the point setting. It is similar to adding labels and attributes of the regular task:

    A Skeleton point can only exist within its parent Skeleton.

    Note that you cannot change the skeleton configuration for an existing task/project.

    You can copy/insert skeleton configuration from the Raw tab of the label configurator.

    Adding Skeleton labels manually

    To create the Skeleton task, do the following:

    1. Open Configurator.
    2. (Optional) Upload background image.
    3. In the Label name field, enter the name of the label.
    4. (Optional) Add attribute
      Note: you can add attributes exclusively to each point, for more information, see Configuring Skeleton points
    5. Use Add point to add points to the Drawing area.
    6. Use Add edge to add edges between points.
    7. Upload files.
    8. Click:
      • Submit & Open to create and open the task.
      • Submit & Continue to submit the configuration and start creating a new task.

    Adding Skeleton labels from the model

    To add points from the model, and annotate do the following:

    1. Open Basic configurator.

    2. On the Constructor tab, click From model.

    3. From the Select a model to pick labels select the Human pose estimation model or others if available.

    4. Click on the model’s labels, you want to use.
      Selected labels will become gray.

    5. (Optional) If you want to adjust labels, within the label, click the Update attributes icon.
      The Skeleton configurator will open, where you can configure the skeleton.
      Note: Labels cannot be adjusted after the task/project is created.

    6. Click Done. The labels, that you selected, will appear in the labels window.

    7. Upload data.

    8. Click:

      • Submit & Open to create and open the task.
      • Submit & Continue to submit the configuration and start creating a new task.

    Annotation with Skeletons

    To annotate with Skeleton, do the following

    1. Open job.

    2. On the tools panel select Draw new skeleton.

    3. Select Track or Shape to annotate. without tracking.

    4. Draw a skeleton on the image.

    Automatic annotation with Skeletons

    To automatically annotate with Skeleton, do the following

    1. Open the job and on the tools panel select AI Tools > Detectors

    2. From the drop-down list select the model. You will see a list of points to match and the name of the skeleton on the top of the list.

    3. (Optional) By clicking on the Bin icon, you can remove any mapped item:

      • A skeleton together with all points.
      • Certain points from two mapped skeletons.

    4. Click Annotate.

    Editing skeletons on the canvas

    A drawn skeleton is encompassed within a bounding box, it allows you to manipulate the skeleton as a regular bounding box, enabling actions such as dragging, resizing, or rotating:

    Upon repositioning a point, the bounding box adjusts automatically, without affecting other points:

    Additionally, Shortcuts are applicable to both the skeleton as a whole and its elements:

    • To use a shortcut to the entire skeleton, hover over the bounding box and push the shortcut keyboard key. This action is applicable for shortcuts like the lock, occluded, pinned, keyframe, and outside for skeleton tracks.
    • To use a shortcut to a specific skeleton point, hover over the point and push the shortcut keyboard key. The same list of shortcuts is available, with the addition of outside, which is also applicable to individual skeleton shape elements.

    Editing skeletons on the sidebar

    In CVAT, the sidebar offers an alternative method for setting up skeleton properties and attributes.

    This approach is similar to that used for other object types supported by CVAT, but with a few specific alterations:

    An additional collapsible section is provided for users to view a comprehensive list of skeleton parts.

    Skeleton points can have properties like Outside, Occluded, and Hidden.

    Both Outside and Hidden make a skeleton point invisible.

    • Outside property is part of annotations. Use it when part of the object is out of frame borders.

    • Hidden makes a point hidden only for the annotator’s convenience, this property will not be saved between different sessions.

    • Occluded keeps the point visible on the frame and usually means that the point is still on a frame, just hidden behind another object.

    17 - Annotation with brush tool

    Guide to annotating tasks using brush tools.

    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

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

    BT Menu

    It has the following elements:

    Element Description
    Tick icon Save mask saves the created mask. The saved mask will appear on the object sidebar
    Save mask and continue Save mask and continue adds a new mask to the object sidebar and allows you to draw a new one immediately.
    Brush Brush adds new mask/ new regions to the previously added mask).
    Eraser Eraser removes part of the mask.
    Add poly Polygon selection tool. Selection will become a mask.
    Remove poly Remove polygon selection subtracts part of the polygon selection.
    Brush size Brush size in pixels.
    Note: Visible only when Brush or Eraser are selected.
    Brush shape Brush shape with two options: circle and square.
    Note: Visible only when Brush or Eraser are selected.
    Pixel remove 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.
    Hide mask Hide mask. When drawing or editing a mask, you can enable this feature to temporarily hide the mask, allowing you to see the objects underneath more clearly.
    Label Label that will be assigned to the newly created mask
    Move 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 Brush icon.

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

      BT context menu

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

      Brushing

    4. After you applied the mask, on the top menu bar click Save mask Tick icon
      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 Save.

    Annotation with polygon-to-mask

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

    1. From the controls sidebar, select Brush Brush icon.

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

      BT context menu

    3. In the brush tool menu, select Polygon Add poly.

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

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

      Brushing

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

    7. Click Save mask Tick icon 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 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.

    Remove pixel

    AI Tools

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

    Save

    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:

    Remove pixel

    18 - 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.

    19 - 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:
    • Description - brief description of the model
    • Labels - list of the supported labels (only for the models of the detectors type)

    20 - CVAT Analytics and QA in Cloud

    Analytics and quality assessment in CVAT Cloud

    20.1 - Automated QA, Review & Honeypots

    Guidelines for assessing annotation quality in CVAT automatically

    In CVAT, it’s possible to evaluate the quality of annotation through the creation of a validation subset of images. 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 quality estimation is currently available for tasks and jobs. Quality estimation in projects is not supported.

    CVAT has the following features for automated quality control of annotations:

    • Validation set configuration for a task
    • Job validation on job finish (“Immediate feedback”)
    • Review mode for problems found
    • Quality analytics

    Basics

    There are several approaches to quality estimation used in the industry. In CVAT, we can use a method known as Ground Truth or Honeypots. The method assumes there are Ground Truth annotations for images in the dataset. This method is statistical, which means that we can use only a small portion of the whole dataset to estimate quality on the full dataset, so we don’t need to annotate the whole dataset twice. Here we assume that the images in the dataset are similar (represent the same task).

    We will call the validation portion of the whole dataset (or a task in CVAT) a validation set. In practice, it is typically expected that annotations in the validation set are carefully validated and curated. It means that they are more expensive - creating them might require expert annotators or just several iterations of annotation and validation. It means that it’s desirable to keep the validation set small enough. At the same time, it must be representative enough to provide reliable estimations. To achieve this, it’s advised that the validation set images are sampled randomly and independently from the full dataset. That is, for the quality assurance to function correctly, the validation set must have some portion of the task frames, and the frames must be chosen randomly.

    Depending on the dataset size, data variance, and task complexity, 5-15% of the data is typically good enough for quality estimation, while keeping extra annotation overhead for the Ground Truth 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, as data variance is higher.

    Ground truth jobs

    A Ground Truth job (GT job) is a way to represent the validation set in a CVAT task. This job is similar to regular annotation jobs - you can edit the annotations manually, use auto-annotation features, and import annotations in this job. There can be no more than 1 Ground Truth job in a task.

    To enable quality estimation in a task, you need to create a Ground truth job in the task, annotate it, switch the job stage to acceptance, and set the job state to completed. Once the Ground Truth job is configured, CVAT will start using this job for quality estimation.

    Read more about Ground Truth management here.

    Configuring quality estimation

    Quality estimation is configured on the Task level.

    1. Go to the task creation page
    2. Configure basic and advanced parameters according to your requirements, and attach a dataset to be annotated
    3. Scroll down to the Quality Control section below
    4. Select one of the validation modes available

    Create task with validation mode

    1. Create the task and open the task page
    2. Upload or create Ground Truth annotations in the Ground Truth job in the task
    3. Switch the Ground Truth job into the acceptance stage and completed state

    Set job status

    For already existing tasks only the Ground Truth validation mode is available. If you want to use Honeypots for your task, you will need to recreate the task.

    1. Open the task page
    2. Click +.

    Create job

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

    Configure job parameters

    • 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 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 Job API create().
    1. Click Submit.

    The Ground truth job will appear in the jobs list.

    Ground Truth job

    1. Annotate frames and save your work or upload annotations.
    2. Switch the Ground Truth job into the acceptance stage and completed state

    Set job status

    A Ground truth job is considered configured if it is at the acceptance stage and in the completed state.

    A configured Ground Truth job is required for all quality computations in CVAT.

    Validation modes

    Currently, there are 2 validation modes available for tasks: Ground Truth and Honeypots. These names are often used interchangeably, but in CVAT they have some differences. Both modes rely on the use of Ground Truth annotations in a task, stored in a Ground Truth job, where they can be managed.

    Ground Truth

    In this mode some of the task frames are selected into the validation set, represented as a separate Ground Truth job. The regular annotation jobs in the task are not affected in any way.

    Ground Truth jobs can be created at the task creation automatically or manually at any moment later. They can also be removed manually at any moment. This validation mode is available for any tasks and annotations.

    This is a flexible mode that can be enabled or disabled at any moment without any disruptions to the annotation process.

    Frame selection

    This validation mode can use several frame selection methods.

    Random

    This is a simple method that selects frames into the validation set randomly, representing the basic approach, described above.

    Parameters:

    • frame count - the number or percent of the task frames to be used for validation. Can be specified as an absolute number in the Frame count field or a percent in the Quantity field. If there are both fields on the page, they are linked, which means changing one of them will adjust the other one automatically.
    • random seed - a number to be used to initialize the random number generator. Can be useful if you want to create a reproducible sequence of frames.
    Random per job

    This method selects frames into the validation set randomly from each annotation job in the task.

    It solves one of the issues with the simple Random method that some of the jobs can get no validation frames, which makes it impossible to estimate quality in such jobs. Note that using this method can result in increased total size of the validation set.

    Parameters:

    • frame count per job - the percent of the job frames to be used for validation. This method uses segment size of the task to select the same number of validation frames in each job, if possible. Can be specified as an absolute number in the Frame count field or a percent in the Quantity per job field. If there are both fields on the page, they are linked, which means changing one of them will adjust the other one automatically.
    • random seed - a number to be used to initialize the random number generator. Can be useful if you want to create a reproducible sequence of frames.

    Honeypots

    In this mode some random frames of the task are selected into the validation set. Then, validation frames are randomly mixed into regular annotation jobs. This mode can also be called “Ground Truth pool”, reflecting the way validation frames are used. This mode can only be used at task creation and cannot be changed later.

    The mode has some limitations on the compatible tasks:

    • It’s not possible to use it for an already existing task, the task has to be recreated.
    • This mode assumes random frame ordering, so it is only available for image annotation tasks and not for ordered sequences like videos.
    • Tracks are not supported in such tasks.

    The validation set can be managed after the task is created - annotations can be edited, frames can be excluded and restored, and honeypot frames in the regular jobs can be changed. However, it’s not possible to select new validation frames after the task is created. The Ground truth job created for this validation mode cannot be deleted.

    Parameters:

    • frame count per job (%) - the percent of job frames (segment size) to be added into each annotation job from the validation set. Can be specified in the Overhead per job field.
    • total frame count (%) - the percent of the task frames to be included into the validation set. This value must result in at least frame count per job * segment size frames. Can be specified in the Total honeypots field.

    Mode summary

    Here is a brief comparison of the validation modes:

    Aspect Ground Truth Honeypots
    When can be used any time at task creation only
    Frame management options exclude, restore exclude, restore, change honeypots in jobs
    Ground Truth job management options create, delete create
    Task frame requirements - random ordering only
    Annotations any tracks are not supported
    Minimum validation frames count - manual and random_uniform - any
     (but some jobs can get no validation frames)
    - random_per_job - jobs count * GT frames per job    		 not less than honeypots count per job
    Task annotation import GT annotations and regular annotations do not affect each other Annotations are imported both into the GT job and regular jobs. Annotations for validation frames are copied into corresponding honeypot frames.
    Task annotation export GT annotations and regular annotations do not affect each other Annotations for non-validation frames are exported as is. Annotations for validation frames are taken from the GT frames. Honeypot frames are skipped.

    Choosing the right mode

    Here are some examples on how to choose between these options. The general advice is to use Ground Truth for better flexibility, but keep in mind that it can require more resources for validation set annotation. Honeypots, on the other hand, can be beneficial if you want to minimize the number of validation images required, but the downside here is that there are some limitations on where this mode can be used.

    Example: a video annotation with tracks. In this case there is only 1 option - the Ground Truth mode, so just use it.

    Example: an image dataset annotation, image order is not important. Here you can use both options. You can choose Ground Truth for better flexibility in validation. This way, you will have the full control of validation frames in the task, annotation options won’t be limited, and the regular jobs will not be affected in any way. However, if you have a limited budget for the validation (for instance, you have only a small number of validation frames) or you want to allow more scalability (with this approach the number of validation frames doesn’t depend on the number of regular annotation jobs), it makes sense to consider using Honeypots instead.

    Quality management

    If a task has a validation configured, there are several options to manage validation set images. With any of the validation modes, there will be a special Ground Truth (GT) job in the task.

    Validation set management

    Validation frames can be managed on the task Quality Management page. Here it’s possible to check the number of validation frames, current validation mode and review the frame details. For each frame you can see the number of uses in the task. When in the Ground Truth mode, this number will be 1 for all frames. With Honeypots, these numbers can be 0, 1 or more.

    Frame changes

    In both validation modes it’s possible to exclude some of the validation frames from being used for validation. This can be useful if you find that some of the validation frames are “bad”, extra, or if they have incorrect annotations, which you don’t want to fix. Once a frame is marked “excluded”, it will not be used for validation. There is also an option to restore a previously excluded frame if you decide so.

    There is an option to exclude or restore frames in bulk mode. To use it, select the frames needed using checkboxes, and click one of the buttons next to the table header.

    Ground Truth job management

    In the Ground Truth validation mode, there will be an option to remove the Ground Truth job from the task. It can be useful if you want to change validation set frames completely, add more frames, or remove some of the frames for any reason. This is available in the job Actions menu.

    In the Honeypots mode, it’s not possible to add or remove the GT job, so it’s not possible to add more validation frames.

    Ground truth job actions

    Create

    A Ground Truth job can be added manually in a task without a selected validation mode or in a task with the Ground Truth validation mode, after the existing Ground Truth job is deleted manually.

    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.

    Note: The Ground truth job in the “Honeypots” task validation mode cannot be deleted.

    Import annotations

    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 annotations

    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.

    Annotation management

    Annotations for validation frames can be displayed and edited in a special Ground Truth job in the task. You can edit the annotations manually, use auto-annotation features, import and export annotations in this job.

    In the Ground Truth task validation mode, annotations of the ground Truth job do not affect other jobs in any way. The Ground Truth job is just a separate job, which can only be changed directly. 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.

    In the Honeypots task validation mode, the annotations of the GT job also do not affect other jobs in any way. However, import and export of task annotations works differently. When importing task annotations, annotations for validation frames will be copied both into GT job frames and into corresponding honeypot frames in annotation jobs. When exporting task annotations, honeypot frames in annotation jobs will be ignored, and validation frames in the resulting dataset will get annotations from the GT job.

    Note that it means that exporting from a task with honeypots and importing the results back will result in changed annotations on the honeypot frames. If you want to backup annotations, use a task backup or export job annotations instead.

    Import and export of Ground Truth job annotations works the same way in both modes.

    Ground Truth jobs are included in task backups, so can be saved and restored this way.

    Import, Export, and Delete options are available from the Ground Truth job Actions menu. Read more.

    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.

    Quality settings page

    Annotation quality settings have the following parameters:

    Parameter Description
    General reporting
    Target metric The primary metric used for quality estimation. It affects which metric is displayed in the UI and used for overall quality estimation.
    Immediate feedback
    Max validations per job Configures maximum job validations per assignment for the Immediate feedback feature.
    Target metric threshold Defines the minimal quality requirements in terms of the selected target metric. Serves as an acceptance threshold for the Immediate feedback feature.
    Shape matching
    Min overlap threshold Min overlap threshold used for the distinction between matched and unmatched shapes. Used to match all types of annotations. It corresponds to the Intersection over union (IoU) for spatial annotations, such as bounding boxes and masks.
    Low overlap threshold Low overlap threshold used for the distinction between strong and weak matches. Only affects Low overlap warnings. It’s supposed that Min similarity threshold <= Low overlap threshold.
    Match empty frames Consider frames matched if there are no annotations both on GT and regular job frames
    Point and Skeleton matching
    OKS Sigma Relative size of points. The percent of the bbox side, used as the radius of the circle around the GT point, where the checked point is expected to be. For boxes with different width and height, the “side” is computed as a geometric mean of the width and height.
    Point matching
    Point size base When comparing point annotations (including both separate points and point groups), the OKS sigma parameter defines a matching area for each GT point based on the object size. The point size base parameter allows configuring how to determine the object size. If set to image_size, the image size is used. Useful if each point annotation represents a separate object or boxes grouped with points do not represent object boundaries. If set to group_bbox_size, the object size is based on the point group bounding box size. Useful if each point group represents an object or there is a bbox grouped with points, representing the object size.
    Polyline matching
    Relative thickness Thickness of polylines, relative to the (image area) ^ 0.5. The distance to the boundary around the GT line inside of which the checked line points should be.
    Check orientation Indicates that polylines have direction. Used to produce Mismatching direction warnings
    Min similarity gain (%) The minimal gain in IoU between the given and reversed line directions to consider the line inverted. Only useful with the Check orientation parameter.
    Group matching
    Compare groups Enables or disables annotation group checks. This check will produce Group mismatch warnings for grouped annotations, if the annotation groups do not match with the specified threshold. Each annotation within a group is expected to match with a corresponding annotation in a GT group.
    Min group match threshold Minimal IoU for groups to be considered matching, used when Compare groups is enabled.
    Mask and polygon matching
    Check object visibility Check for partially-covered annotations. Masks and polygons will be compared to each other.
    Min visibility threshold Minimal visible area percent of the mask annotations (polygons, masks). Used for reporting Covered annotation warnings, useful with the Check object visibility option.
    Match only visible parts Use only the visible part of the masks and polygons in comparisons.

    Comparisons

    Tags

    The equality is used for matching.

    Shapes

    A pair of shapes is considered matching, if both their shapes and labels match. For each shape, spatial parameters are matched first, then labels are matched.

    Each shape type can have their own spatial matching details. Specifically:

    • bounding box - IoU (including rotation). For example, for a pair of bounding boxes it can be visualized this way:

      Bbox IoU


      IoU = intersection area / union area.
      The green part is the intersection, and green, yellow and red ones together are the union.

    • polygons, masks - IoU. Polygons and masks are considered interchangeable, which means a mask can be matched with a polygon and vice versa. Polygons and masks in groups are merged into a single object first. If the Match only visible parts option is enabled, objects will be cut to only the visible (non-covered) parts only, which is determined by the shape z order.

    • skeletons - The OKS metric from the COCO dataset is used. Briefly, each skeleton point gets a circular area around, determined by the object size (bounding box side) and relative point size (sigma) values, where this point can be matched with the specified probability. If a bounding box is grouped with the skeleton, it is used for object size computation, otherwise a bounding box of visible points of the skeleton is used.

      For example, consider a skeleton with 6 points and a square bounding box attached:

      Skeleton OKS

      In this example, the Sigma parameter is 0.05 (5%) of the bbox side. Areas shown in the green color cover ~68.2% (1 sigma) of the points, corresponding to each GT point. A point on the boundary of such an area will have ~88% of probability to be correct. The blue-colored zone contains ~95% (2 sigma) of the correct points for the corresponding GT point. A point on the boundary of such an area will have ~60% of probability to be correct. These probabilities are then averaged over the visible points of the skeleton, and the resulting values are compared against the Min similarity threshold to determine whether the skeletons are matching. Sigma corresponds to one from the normal distribution.

    • points - The OKS metric is used for each point group annotation. Same as for skeletons, OKS Sigma determines relative point sizes. The Point size base setting allows configuring whether points in point groups should use the group bounding box or the image space. Using image space for object size can be useful if you want to treat each point as a separate annotation.

    • polylines - A pair of lines is considered matching if all the points of one line lie within a “hull” of the other one. The “hull” is determined as the area around the polyline, such as if the line had some “thickness”. For example, the black polyline can have a hull shown in the green color:

      Polyline thickness and hull

      The line thickness can be configured via the Relative thickness setting. The value is relative to the image side and determines a half of the hull width.

    • ellipses - IoU, described in more detail above.

    Note: 2d cuboids are not supported

    Tracks

    Tracks are split into separate shapes and compared on the per-frame basis with other tracks and shapes.

    Quality Analytics

    Note: quality analytics is a paid feature. Please check how to get access to this functionality in the Paid features section of the site.

    Once the quality estimation is enabled in a task and the Ground Truth job is configured, quality analytics becomes available for the task and its jobs.

    When you open the Quality Analytics page, it displays quality metrics from the most recent quality estimation. If it’s your first time accessing the page, no quality report will be available yet. The date of the last computation is shown next to the report download button.

    If you want to request updating of quality metrics in a task (e.g. after the settings were changed), you can do this by pressing the Refresh button on the task Quality Management > Analytics page.

    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.

    Quality Analytics page - refresh button

    Once quality metrics are computed, they are available for detailed review on this page. Conflicts can be reviewed in the Review mode of jobs. A job must have at least 1 validation frame (shown in the Frame intersection column) to be included in quality computation.

    Analytics page contents

    The Analytics page has the following elements:

    Quality Analytics page

    Field Description
    Mean annotation quality Displays the average quality of annotations, which includes: counts of the accurate annotations, total task annotations, ground truth annotations, accuracy, precision, and recall. The currently selected Target metric is displayed as the primary score.
    GT Conflicts Conflicts identified during quality assessment, including extra or missing annotations. Mouse over the ? icon for a detailed conflict report on your dataset.
    Issues Number of opened issues. If no issues were reported, 0 will be shown.
    Quality report Quality report in JSON format.
    Ground truth job data Information about ground truth job, including date, time, and number of issues.
    List of jobs List of all the jobs in the task

    Jobs list

    Problem Reporting

    CVAT reports 2 possible error types: errors and warnings. Errors affect the resulting quality scores and highlight significant problems in annotations. Warnings do not affect the resulting quality metrics, but they still can highlight significant problems, depending on the project requirements.

    Problem Type Description
    Missing annotation error No matching annotation found in the regular job annotations. Configured by Min overlap threshold and shape type-specific parameters.
    Extra annotation error No matching annotation found in the GT job annotations. Configured by Min overlap threshold and shape type-specific parameters.
    Mismatching label error A GT and a regular job annotations match, but their labels are different.
    Low overlap warning A GT and a regular job annotations match, but the similarity is low. Configured by Low overlap threshold.
    Mismatching direction warning A GT and a regular lines match, but the lines have different direction. Configured by Compare orientation.
    Mismatching attributes warning A GT and a regular annotations match, but their attributes are different. Configured by Compare attributes.
    Mismatching groups warning A GT and a regular annotation groups do not match. Configured by Compare groups.
    Covered annotation warning The visible part of a regular mask or polygon annotation is too small. The visibility is determined by arranging mask and polygon shapes on the frame in the specified z order. Configured by Check object visibility.

    Quality Reports

    For each job included in quality computation there is a quality report downloading button on the Analytics page. There is also a button to download the aggregated task quality report. These buttons provide an option to download a Quality Report for a task or job in JSON format. Such reports can be useful if you want to process quality reported by CVAT automatically in your scripts etc.

    Download report

    Quality Reports contain quality metrics and conflicts, and include all the information available on the quality analytics page. You can find additional quality metrics in these reports, such as mean_iou for shapes, confusion matrices, per-label and per-frame quality estimations.

    Additional information on how to compute and use various metrics for dataset quality estimation is available here.

    Reviewing GT conflicts

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

    GT conflicts review - enable

    Ground Truth annotations are displayed with a dotted-line border. The associated label and the (Ground Truth) marker are shown on hovering.

    Upon hovering over an issue on the right-side panel with your mouse, the corresponding annotations are 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.

    GT conflicts review - create issue

    Annotation quality & Honeypot video tutorial

    This video demonstrates the process:

    20.2 - Manual QA and Review

    Guidelines on evaluating annotation quality in CVAT manually

    In the demanding process of annotation, ensuring accuracy is paramount.

    CVAT introduces a specialized Review mode, designed to streamline the validation of annotations by pinpointing errors or discrepancies in annotation.

    Note: The Review mode is not applicable for 3D tasks.

    See:

    Review and report issues: review only mode

    Review mode is a user interface (UI) setting where a specialized Issue tool is available. This tool allows you to identify and describe issues with objects or areas within the frame.

    Note: While in review mode, all other tools will be hidden.

    Review mode screen looks like the following:

    Review mode screen

    Assigning reviewer

    Note: Reviewers can be assigned by project or task owner, assignee, and maintainer.

    To assign a reviewer to the job, do the following:

    1. Log in to the Owner or Maintainer account.

    2. (Optional) If the person you wish to assign as a reviewer is not a member of Organization, you need to Invite this person to the Organization.

    3. Click on the Assignee field and select the reviewer.

    4. From the Stage drop-down list, select Validation.

      Assigning reviewer

    Reporting issues

    To report an issue, do the following:

    1. Log in to the reviewer’s account.

    2. On the Controls sidebar, click Open and issue ().

    3. Click on the area of the frame where the issue is occurring, and the Issue report popup will appear.

      Issue report window

    4. In the text field of the Issue report popup, enter the issue description.

    5. Click Submit.

    Quick issue

    The Quick issue function streamlines the review process. It allows reviewers to efficiently select from a list of previously created issues or add a new one, facilitating a faster and more organized review.

    Quick issue

    To create a Quick issue do the following:

    1. Right-click on the area of the frame where the issue is occurring.

    2. From the popup menu select one of the following:

      • Open an issue…: to create new issue.
      • Quick issue: incorrect position: to report incorrect position of the label.
      • Quick issue: incorrect attribute: to report incorrect attribute of the label.
      • Quick issue…: to open the list of issues that were reported by you before.

    Assigning corrector

    Note: Only project owners and maintainers can assign reviewers.

    To assign a corrector to the job, do the following:

    1. Log in to the Owner or Maintainer account.

    2. (Optional) If the person you wish to assign as a corrector is not a member of Organization, you need to Invite this person to the Organization.

    3. Click on the Assignee field and select the reviewer.

    4. From the Stage drop-down list, select Annotation.

      Assigning corrector

    Correcting reported issues

    To correct the reported issue, do the following:

    1. Log in to the corrector account.

    2. Go to the reviewed job and open it.

    3. Click on the issue report, to see details of what needs to be corrected.

      Issue report label

    4. Correct annotation.

    5. Add a comment to the issue report and click Resolve.

      Issue report

    6. After all issues are fixed save work, go to the Menu select the Change the job state and change state to Complete.

      Change job status

    Review and report issues: review and correct mode

    The person, assigned as assigned as reviewer can switch to correction mode and correct all annotation issues.

    To correct annotation issues as a reviewer, do the following:

    1. Log in to the reviewer account.

    2. Go to the assigned job and open it.

    3. In the top right corner, from the drop-down list, select Standard.

      Change job status

    Issues navigation and interface

    This section describes navigation, interface and comments section.

    Issues tab

    The created issue will appear on the Objects sidebar, in the Issues tab.

    It has has the following elements:

    Element Description
    Arrows You can switch between issues by clicking on arrows
    Hide all issues Click on the eye icon to hide all issues
    Hide resolved issues Click on the check mark to hide only resolved issues
    Ground truth Show ground truth annotations and objects

    Issues workspace

    In the workspace, you can click on the issue, and add a comment on the issue, remove (Remove) it, or resolve (Resolve) it.

    To reopen the resolved issue, click Reopen.

    You can easily access multiple issues created in one location by hovering over an issue and scrolling the mouse wheel.

    Issues comments

    You can add as many comments as needed to the issue.

    In the Objects toolbar, only the first and last comments will be displayed

    You can copy and paste comments text.

    Manual QA complete video tutorial

    This video demonstrates the process:

    20.3 - CVAT Team Performance & Monitoring

    How to monitor team activity and performance in CVAT

    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

    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 (Open menu).
    3. From the menu, select View analytics > Performance tab.

    Open menu

    The following dashboard will open:

    Open menu

    The Performance dashboard has the following elements:

    Element Description
    Analytics for Object/ Task/ Job number.
    Created Time when the dashboard was updated last time.
    Objects Graph, showing the number of annotated, updated, and deleted objects by day.
    Annotation speed (objects per hour) Number of objects annotated per hour.
    Time A drop-down list with various periods for the graph. Currently affects only the histogram data.
    Annotation time (hours) Shows for how long the Project/Task/Job is in In progress state.
    Total objects count Shows the total objects count in the task. Interpolated objects are counted.
    Total annotation speed (objects per hour) Shows the annotation speed in the Project/Task/Job. Interpolated objects are counted.

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

    Performance video tutorial

    This video demonstrates the process:

    21 - OpenCV and AI Tools

    Overview of semi-automatic and automatic annotation tools available in CVAT.

    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

    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 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 OpenCVOpenCV 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

    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
    		•  int scissors

    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 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

    Model Description
    Mask RCNN The model generates polygons for each instance of an object in the image.

    For more information, see:
  • GitHub: Mask RCNN
  • Paper: Mask RCNN
    		•
    Faster RCNN The model generates bounding boxes for each instance of an object in the image.
    In this model, RPN and Fast R-CNN are combined into a single network.

    For more information, see:
  • GitHub: Faster RCNN
  • Paper: Faster RCNN
    		•
    YOLO v3 YOLO v3 is a family of object detection architectures and models pre-trained on the COCO dataset.

    For more information, see:
  • GitHub: YOLO v3
  • Site: YOLO v3
  • Paper: YOLO v3
    		•
    Semantic segmentation for ADAS This is a segmentation network to classify each pixel into 20 classes.

    For more information, see:
  • Site: ADAS
    		•
    Mask RCNN with Tensorflow Mask RCNN version with Tensorflow. The model generates polygons for each instance of an object in the image.

    For more information, see:
  • GitHub: Mask RCNN
  • Paper: Mask RCNN
    		•
    Faster RCNN with Tensorflow Faster RCNN version with Tensorflow. The model generates bounding boxes for each instance of an object in the image.
    In this model, RPN and Fast R-CNN are combined into a single network.

    For more information, see:
  • Site: Faster RCNN with Tensorflow
  • Paper: Faster RCNN
    		•
    RetinaNet Pytorch implementation of RetinaNet object detection.


    For more information, see:
  • Specification: RetinaNet
  • Paper: RetinaNet
  • Documentation: RetinaNet
    		•
    Face Detection Face detector based on MobileNetV2 as a backbone for indoor and outdoor scenes shot by a front-facing camera.


    For more information, see:
  • Site: Face Detection 0205
    		•

    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 Magic wand, and go to the Trackers tab.


      Start tracking an object

    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 OpenCVOpenCV and wait for the library to load.


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


      Start tracking an object

    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.

      Tracker switcher

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

      Tracker indication

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

      Tracker pop-up window

    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
    		•  Annotation using a tracker
    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
    		•  Annotation using a tracker
    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
    		•  Annotation using a tracker

    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.

    22 - Automatic annotation

    Automatic annotation of tasks

    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:

    The following table describes the available options:

    Self-hosted Cloud
    Price Free See Pricing
    Models You have to add models You can use pre-installed models
    Hugging Face & Roboflow
    integration    		 Not supported Supported

    See:

    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.

    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:

    Model Description
    Attributed face detection Three OpenVINO models work together:

  • Face Detection 0205: face detector based on MobileNetV2 as a backbone with a FCOS head for indoor and outdoor scenes shot by a front-facing camera.
  • Emotions recognition retail 0003: fully convolutional network for recognition of five emotions (‘neutral’, ‘happy’, ‘sad’, ‘surprise’, ‘anger’).
  • Age gender recognition retail 0013: fully convolutional network for simultaneous Age/Gender recognition. The network can recognize the age of people in the [18 - 75] years old range; it is not applicable for children since their faces were not in the training set.
    		•
    RetinaNet R101 RetinaNet is a one-stage object detection model that utilizes a focal loss function to address class imbalance during training. Focal loss applies a modulating term to the cross entropy loss to focus learning on hard negative examples. RetinaNet is a single, unified network composed of a backbone network and two task-specific subnetworks.

    For more information, see:
  • Site: RetinaNET
    		•
    Text detection Text detector based on PixelLink architecture with MobileNetV2, depth_multiplier=1.4 as a backbone for indoor/outdoor scenes.

    For more information, see:
  • Site: OpenVINO Text detection 004
    		•
    YOLO v3 YOLO v3 is a family of object detection architectures and models pre-trained on the COCO dataset.

    For more information, see:
  • Site: YOLO v3
    		•
    YOLO v7 YOLOv7 is an advanced object detection model that outperforms other detectors in terms of both speed and accuracy. It can process frames at a rate ranging from 5 to 160 frames per second (FPS) and achieves the highest accuracy with 56.8% average precision (AP) among real-time object detectors running at 30 FPS or higher on the V100 graphics processing unit (GPU).

    For more information, see:
  • GitHub: YOLO v7
  • Paper: YOLO v7
    		•

    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:

    23 - Specification for annotators

    Learn how to easily create and add specification for annotators using the Guide feature.

    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

    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.

    Project specification

    1. 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

    Editor rights

    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.

      Task specification

    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.

    Editor rights

    Access to specification for annotators

    The specification is opened automatically when the job has new annotation state. It means, that it will open when the assigned user begins working on the first job within a Project or Task.

    The specifications will not automatically reopen if the user moves to another job within the same Project or Task.

    If a Project or Task is reassigned to another annotator, the specifications will automatically be shown when the annotator opens the first job but will not reappear for subsequent jobs.

    To enable the option for specifications to always open automatically, append the ?openGuide parameter to the end of the job URL you share with the annotator:

    /tasks/<task_id>/jobs/<job_id>?openGuide

    For example:

    https://app.cvat.ai/tasks/taskID/jobs/jobID?openGuide

    To open specification manually, do the following:

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

    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.

    Markdown editor

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

    Markdown editor

    Element Description
    1 Text formatting: bold, cursive, and strikethrough.
    2 Insert a horizontal rule (horizontal line).
    3 Add a title, heading, or subheading. It provides a drop-down list to select the title level (from 1 to 6).
    4 Add a link.
    Note: If you left-click on the link, it will open in the same window.
    5 Add a quote.
    6 Add a single line of code.
    7 Add a block of code.
    8 Add a comment. The comment is only visible to Guide editors and remains invisible to annotators.
    9 Add a picture. To use this option, first, upload the picture to an external resource and then add the link in the editor. Alternatively, you can drag and drop a picture into the editor, which will upload it to the CVAT server and add it to the specification.
    10 Add a list: bullet list, numbered list, and checklist.
    11 Hide the editor pane: options to hide the right pane, show both panes or hide the left pane.
    12 Enable full-screen mode.

    Specification for annotators’ video tutorial

    Video tutorial on how to use the Guide feature.

    24 - 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:

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

    25 - Frame deleting

    This section explains how to delete and restore a frame from a task.

    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.

    Configure 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.

    26 - Join and slice tools

    This section explains how to slice or join several labels

    In CVAT you can modify shapes by either joining multiple shapes into a single label or slicing a single label into several shapes.

    This document provides guidance on how to perform these operations effectively.

    See:

    Joining masks

    The Join masks tool (Join masks tool icon), is specifically designed to work with mask annotations.

    This tool is useful in scenarios where a single object in an image is annotated with multiple shapes, and there is a need to merge these shapes into a single one.

    Join masks

    To join masks, do the following:

    1. From the Edit block, select Join masks Join masks tool icon.
    2. Click on the canvas area, to select masks that you want to join.
    3. (Optional) To remove the selection click the mask one more time.
    4. Click again on Join masksJoin masks tool icon (J) to execute the join operation.

    Upon completion, the selected masks will be joined into a single mask.

    Join masks gif

    Slicing polygons and masks

    The Slice mask/polygon (Slicing tool icon) is compatible with both mask and polygon annotations.

    This tool is useful in scenarios where multiple objects in an image are annotated with one shape, and there is a need to slice this shape into multiple parts.

    Note: The shape can be sliced only in two parts at a time. Use the slice tool several times to split a shape to as many parts as you need.

    Slicing tool

    To slice mask or polygon, do the following:

    1. From the Edit block, select Slice mask/polygon Slicing tool icon.
    2. Click on the shape you intend to slice. A black contour will appear around the selected shape.
    3. Set an initial point for slicing by clicking on the contour.
    4. Draw a line across the shape to define the slicing path.
      Hold Shift to add points automatically on cursor movement.
      Note: The line cannot cross itself.
      Note: The line cannot cross the contour more than twice.
    5. (Optional)> Right-click to cancel the latest point.
    6. Click on the contour (Alt+J) (outside the contour) to finalize the slicing.

    Slicing tool

    27 - Import datasets and upload annotation

    This section explains how to download and upload datasets (including annotation, images, and metadata) of projects, tasks, and jobs.

    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:


    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.

    28 - Export annotations and data from CVAT

    List of data export formats formats supported by 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

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

    Format Type Computer Vision Task Models Shapes Attributes Video Tracks
    CamVid 1.0 .txt
    .png    		 Semantic
    Segmentation    		 U-Net, SegNet, DeepLab,
    PSPNet, FCN, Mask R-CNN,
    ICNet, ERFNet, HRNet,
    V-Net, and others.    		 Polygons Not supported Not supported
    Cityscapes 1.0 .txt
    .png    		 Semantic
    Segmentation    		 U-Net, SegNet, DeepLab,
    PSPNet, FCN, ERFNet,
    ICNet, Mask R-CNN, HRNet,
    ENet, and others.    		 Polygons Specific attributes Not supported
    COCO 1.0 JSON Detection, Semantic
    Segmentation    		 YOLO (You Only Look Once),
    Faster R-CNN, Mask R-CNN, SSD (Single Shot MultiBox Detector),
    RetinaNet, EfficientDet, UNet,
    DeepLabv3+, CenterNet, Cascade R-CNN, and others.    		 Bounding Boxes, Polygons Specific attributes Not supported
    COCO Keypoints 1.0 .xml Keypoints OpenPose, PoseNet, AlphaPose,
    SPM (Single Person Model),
    Mask R-CNN with Keypoint Detection:, and others.    		 Skeletons Specific attributes Not supported
    CVAT for images 1.1 .xml Any in 2D except for Video Tracking Any model that can decode the format. Bounding Boxes, Polygons,
    Polylines, Points, Cuboids,
    Skeletons, Ellipses, Masks, Tags.    		 All attributes Not supported
    CVAT for video 1.1 .xml Any in 2D except for Classification Any model that can decode the format. Bounding Boxes, Polygons,
    Polylines, Points, Cuboids,
    Skeletons, Ellipses, Masks.    		 All attributes Supported
    Datumaro 1.0 JSON Any Any model that can decode the format.
    Main format in Datumaro framework    		 Bounding Boxes, Polygons,
    Polylines, Points, Cuboids,
    Skeletons, Ellipses, Masks, Tags.    		 All attributes Supported
    ICDAR
    Includes ICDAR Recognition 1.0,
    ICDAR Detection 1.0,
    and ICDAR Segmentation 1.0
    descriptions.    		 .txt Text recognition,
    Text detection,
    Text segmentation    		 EAST: Efficient and Accurate
    Scene Text Detector, CRNN, Mask TextSpotter, TextSnake,
    and others.    		 Tag, Bounding Boxes, Polygons Specific attributes Not supported
    ImageNet 1.0 .jpg
    .txt    		 Semantic Segmentation,
    Classification,
    Detection    		 VGG (VGG16, VGG19), Inception, YOLO, Faster R-CNN , U-Net, and others Tags No attributes Not supported
    KITTI 1.0 .txt
    .png    		 Semantic Segmentation, Detection, 3D PointPillars, SECOND, AVOD, YOLO, DeepSORT, PWC-Net, ORB-SLAM, and others. Bounding Boxes, Polygons Specific attributes Not supported
    LabelMe 3.0 .xml Compatibility,
    Semantic Segmentation    		 U-Net, Mask R-CNN, Fast R-CNN,
    Faster R-CNN, DeepLab, YOLO,
    and others.    		 Bounding Boxes, Polygons Supported (Polygons) Not supported
    LFW 1.0 .txt Verification,
    Face recognition    		 OpenFace, VGGFace & VGGFace2,
    FaceNet, ArcFace,
    and others.    		 Tags, Skeletons Specific attributes Not supported
    Market-1501 1.0 .txt Re-identification Triplet Loss Networks,
    Deep ReID models, and others.    		 Bounding Boxes Specific attributes Not supported
    MOT 1.0 .txt Video Tracking,
    Detection    		 SORT, MOT-Net, IOU Tracker,
    and others.    		 Bounding Boxes Specific attributes Supported
    MOTS PNG 1.0 .png
    .txt    		 Video Tracking,
    Detection    		 SORT, MOT-Net, IOU Tracker,
    and others.    		 Bounding Boxes, Masks Specific attributes Supported
    Open Images 1.0 .csv Detection,
    Classification,
    Semantic Segmentation    		 Faster R-CNN, YOLO, U-Net,
    CornerNet, and others.    		 Bounding Boxes, Tags, Polygons Specific attributes Not supported
    PASCAL VOC 1.0 .xml Classification, Detection Faster R-CNN, SSD, YOLO,
    AlexNet, and others.    		 Bounding Boxes, Tags, Polygons Specific attributes Not supported
    Segmentation Mask 1.0 .txt Semantic Segmentation Faster R-CNN, SSD, YOLO,
    AlexNet, and others.    		 Polygons No attributes Not supported
    VGGFace2 1.0 .csv Face recognition VGGFace, ResNet, Inception,
    and others.    		 Bounding Boxes, Points No attributes Not supported
    WIDER Face 1.0 .txt Detection SSD (Single Shot MultiBox Detector), Faster R-CNN, YOLO,
    and others.    		 Bounding Boxes, Tags Specific attributes Not supported
    YOLO 1.0 .txt Detection YOLOv1, YOLOv2 (YOLO9000),
    YOLOv3, YOLOv4, and others.    		 Bounding Boxes No attributes Not supported
    YOLOv8 Detection 1.0 .txt Detection YOLOv8 Bounding Boxes No attributes Not supported
    YOLOv8 Segmentation 1.0 .txt Instance Segmentation YOLOv8 Polygons, Masks No attributes Not supported
    YOLOv8 Pose 1.0 .txt Keypoints YOLOv8 Skeletons No attributes Not supported
    YOLOv8 Oriented Bounding Boxes 1.0 .txt Detection YOLOv8 Bounding Boxes No attributes Not supported
    YOLOv8 Classification 1.0 .jpg Classification YOLOv8 Tags No attributes Not supported

    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.

      Save images option

    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.

      Export 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.

      Save images option

    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:

    28.1 - CVAT for image

    How to export and import data in CVAT for image format

    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:

    CVAT for image export

    Applicable for all computer vision tasks in 2D except for Video Tracking.

    For export of images:

    • Supported annotations: Bounding Boxes, Polygons, Polylines, Points, Cuboids, Ellipses, Skeletons, Tags, Masks.
    • Attributes: Supported.
    • Tracks: Can be exported, but track id will be lost.

    The downloaded file is a zip archive with following structure:

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

    CVAT for video export

    Applicable for all computer vision tasks in 2D except for Classification

    For export of images:

    • Supported annotations: Bounding Boxes, Polygons, Polylines, Points, Cuboids, Ellipses, Skeletons,Masks.
    • 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.

    28.2 - Datumaro

    How to export and import data in Datumaro format

    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:

    Export annotations in Datumaro format

    For export of images: any 2D shapes, tags

    • Supported annotations: Bounding Boxes, Polygons, Polylines, Points, Cuboids, Tags, Ellipses, Masks, Skeletons.
    • 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: Bounding Boxes, Polygons, Polylines, Masks, Points, Cuboids, Labels, Skeletons
    • 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
        }
      ]
    }
  ]
}

    28.3 - LabelMe

    How to export and import data in LabelMe format

    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 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)

    28.4 - MOT

    How to export and import data in MOT format

    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 export

    For export of images and videos:

    • Supported annotations: Bounding Boxes.
    • 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:

    archive.zip/
└── gt/
    └── gt.txt
    └── labels.txt # optional, mandatory for non-official labels
    • supported annotations: Rectangle tracks

    28.5 - MOTS

    How to export and import data in MOTS format

    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 export

    For export of images and videos:

    • Supported annotations: Bounding Boxes, Masks
    • 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

    28.6 - COCO

    How to export and import data in COCO format

    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 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

    Upload 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.

    28.7 - COCO Keypoints

    How to export and import data in COCO Keypoints format

    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 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).

    28.8 - Pascal VOC

    How to export and import data in Pascal VOC format

    The 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 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.

    28.9 - Segmentation Mask

    How to export and import data in Segmentation Mask format

    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

    28.10 - YOLO

    How to export and import data in YOLO format

    YOLO, which stands for “You Only Look Once,” is a renowned framework predominantly utilized for real-time object detection tasks. Its efficiency and speed make it an ideal choice for many applications. While YOLO has its unique data format, this format can be tailored to suit other object detection models as well.

    For more information, see:

    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

    3. 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.

    4. 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

    5. 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

    6. Click Upload annotation button, choose YOLO 1.1 and select the zip file with labels from the previous step.

    28.11 - YOLOv8

    How to export and import data in YOLOv8 formats

    YOLOv8 is a format family which consists of four formats:

    Dataset examples:

    YOLOv8 export

    For export of images:

    • Supported annotations
      • Detection: Bounding Boxes
      • Oriented bounding box: Oriented Bounding Boxes
      • Segmentation: Polygons, Masks
      • Pose: Skeletons
    • Attributes: Not supported.
    • Tracks: Not supported.

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

    archive.zip/
   ├── data.yaml  # configuration file
   ├── train.txt  # list of train subset image paths
   │
   ├── images/
   │   ├── train/  # directory with images for train subset
   │   │    ├── image1.jpg
   │   │    ├── image2.jpg
   │   │    ├── image3.jpg
   │   │    └── ...
   ├── labels/
   │   ├── train/  # directory with annotations for train subset
   │   │    ├── image1.txt
   │   │    ├── image2.txt
   │   │    ├── image3.txt
   │   │    └── ...

# train.txt:
images/<subset>/image1.jpg
images/<subset>/image2.jpg
...

# data.yaml:
path:  ./ # dataset root dir
train: train.txt  # train images (relative to 'path')

# YOLOv8 Pose specific field
# First number is the number of points in a skeleton.
# If there are several skeletons with different number of points, it is the greatest number of points
# Second number defines the format of point info in annotation txt files
kpt_shape: [17, 3]

# Classes
names:
  0: person
  1: bicycle
  2: car
  # ...

# <image_name>.txt:
# content depends on format

# YOLOv8 Detection:
# label_id - id from names field of data.yaml
# 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

# YOLOv8 Oriented Bounding Boxes:
# xn, yn - relative coordinates of the n-th point
# label_id x1 y1 x2 y2 x3 y3 x4 y4
1 0.3 0.8 0.1 0.3 0.4 0.5 0.7 0.5
2 0.7 0.2 0.3 0.1 0.4 0.5 0.5 0.6

# YOLOv8 Segmentation:
# xn, yn - relative coordinates of the n-th point
# label_id x1 y1 x2 y2 x3 y3 ...
1 0.3 0.8 0.1 0.3 0.4 0.5
2 0.7 0.2 0.3 0.1 0.4 0.5 0.5 0.6 0.7 0.5

# YOLOv8 Pose:
# cx, cy - relative coordinates of the bbox center
# rw, rh - relative size of the bbox
# xn, yn - relative coordinates of the n-th point
# vn - visibility of n-th point. 2 - visible, 1 - partially visible, 0 - not visible
# if second value in kpt_shape is 3:
# label_id cx cy rw rh x1 y1 v1 x2 y2 v2 x3 y3 v3 ...
1 0.3 0.8 0.1 0.3 0.3 0.8 2 0.1 0.3 2 0.4 0.5 2 0.0 0.0 0 0.0 0.0 0
2 0.3 0.8 0.1 0.3 0.7 0.2 2 0.3 0.1 1 0.4 0.5 0 0.5 0.6 2 0.7 0.5 2

# if second value in kpt_shape is 2:
# label_id cx cy rw rh x1 y1 x2 y2 x3 y3 ...
1 0.3 0.8 0.1 0.3 0.3 0.8 0.1 0.3 0.4 0.5 0.0 0.0 0.0 0.0
2 0.3 0.8 0.1 0.3 0.7 0.2 0.3 0.1 0.4 0.5 0.5 0.6 0.7 0.5

# Note, that if there are several skeletons with different number of points,
# smaller skeletons are padded with points with coordinates 0.0 0.0 and visibility = 0

    All coordinates must be normalized. It can be achieved by dividing x coordinates and widths by image width, and y coordinates and heights by image height.

    Note, that in CVAT you can place an object or some parts of it outside the image, which will cause the coordinates to be outside the [0, 1] range. YOLOv8 framework ignores labels with such coordinates.

    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.

    28.12 - YOLOv8-Classification

    How to export and import data in YOLOv8 Classification format

    For more information, see:

    YOLOv8 Classification 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:

    archive.zip/
├── train
│    ├── labels.json  # CVAT extension. Contains original ids and labels
│    │                # is not needed when using dataset with YOLOv8 framework
│    │                # but is useful when importing it back to CVAT
│    ├── label_0
│    │      ├── <image_name_0>.jpg
│    │      ├── <image_name_1>.jpg
│    │      ├── <image_name_2>.jpg
│    │      ├── ...
│    ├── label_1
│    │      ├── <image_name_0>.jpg
│    │      ├── <image_name_1>.jpg
│    │      ├── <image_name_2>.jpg
│    │      ├── ...
├── ...

    28.13 - ImageNet

    How to export and import data in ImageNet format

    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 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

    28.14 - Wider Face

    How to export and import data in Wider Face format

    The 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 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

    28.15 - CamVid

    How to export and import data in CamVid format

    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 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

    28.16 - VGGFace2

    How to export and import data in VGGFace2 format

    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 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)

    28.17 - Market-1501

    How to export and import data in Market-1501 format

    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 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)

    28.18 - ICDAR13/15

    How to export and import data in ICDAR13/15 format

    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 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

    28.19 - Open Images

    How to export and import data in Open Images format

    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 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.

    28.20 - Cityscapes

    How to export and import data in Cityscapes format

    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 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.

    28.21 - KITTI

    How to export and import data in KITTI format

    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 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.

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

    └─ annotations.zip/
    ├── label_colors.txt # list of pairs r g b label_name
    ├── labels.txt # list of labels
    └── default/
        ├── label_2/ # left color camera label files
        │   ├── <image_name_1>.txt
        │   ├── <image_name_2>.txt
        │   └── ...
        ├── instance/ # instance segmentation masks
        │   ├── <image_name_1>.png
        │   ├── <image_name_2>.png
        │   └── ...
        ├── semantic/ # semantic segmentation masks (labels are encoded by its id)
        │   ├── <image_name_1>.png
        │   ├── <image_name_2>.png
        │   └── ...
        └── semantic_rgb/ # semantic segmentation masks (labels are encoded by its color)
            ├── <image_name_1>.png
            ├── <image_name_2>.png
            └── ...

    KITTI annotations import

    You can upload KITTI annotations in two ways: rectangles for the detection task and masks for the segmentation task.

    For detection tasks the uploading archive should have the following structure:

    └─ annotations.zip/
    ├── labels.txt # optional, labels list for non-original detection labels
    └── <subset_name>/
        ├── label_2/ # left color camera label files
        │   ├── <image_name_1>.txt
        │   ├── <image_name_2>.txt
        │   └── ...

    For segmentation tasks the uploading archive should have the following structure:

    └─ annotations.zip/
    ├── label_colors.txt # optional, color map for non-original segmentation labels
    └── <subset_name>/
        ├── instance/ # instance segmentation masks
        │   ├── <image_name_1>.png
        │   ├── <image_name_2>.png
        │   └── ...
        ├── semantic/ # optional, semantic segmentation masks (labels are encoded by its id)
        │   ├── <image_name_1>.png
        │   ├── <image_name_2>.png
        │   └── ...
        └── semantic_rgb/ # optional, semantic segmentation masks (labels are encoded by its color)
            ├── <image_name_1>.png
            ├── <image_name_2>.png
            └── ...

    All annotation files and masks should have structures that are described in the original format specification.

    28.22 - LFW

    How to export and import data in LFW format

    The Labeled Faces in the Wild (LFW) format is primarily used for face verification and face recognition tasks. The LFW format is designed to be straightforward and is compatible with a variety of machine learning and deep learning frameworks.

    For more information, see:

    Export LFW annotation

    For export of images:

    • Supported annotations: Tags, Skeletons.

    • Attributes:

      • negative_pairs (should be defined for labels as text): list of image names with mismatched persons.
      • positive_pairs (should be defined for labels as text): list of image names with matched persons.

    • Tracks: Not supported.

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

    <archive_name>.zip/
    └── images/ # if the option save images was selected
    │    ├── name1/
    │    │   ├── name1_0001.jpg
    │    │   ├── name1_0002.jpg
    │    │   ├── ...
    │    ├── name2/
    │    │   ├── name2_0001.jpg
    │    │   ├── name2_0002.jpg
    │    │   ├── ...
    │    ├── ...
    ├── landmarks.txt
    ├── pairs.txt
    └── people.txt

    Import LFW annotation

    The uploaded annotations file should be a zip file with the following structure:

    <archive_name>.zip/
    └── annotations/
        ├── landmarks.txt # list with landmark points for each image
        ├── pairs.txt # list of matched and mismatched pairs of person
        └── people.txt # optional file with a list of persons name

    Full information about the content of annotation files is available here

    Example: create task with images and upload LFW annotations into it

    This is one of the possible ways to create a task and add LFW annotations for it.

    • On the task creation page:
      • Add labels that correspond to the names of the persons.
      • For each label define text attributes with names positive_pairs and negative_pairs
      • Add images using zip archive from local repository:
    images.zip/
    ├── name1_0001.jpg
    ├── name1_0002.jpg
    ├── ...
    ├── name1_<N>.jpg
    ├── name2_0001.jpg
    ├── ...
    • On the annotation page: Upload annotation -> LFW 1.0 -> choose archive with structure that described in the import section.

    29 - XML annotation format

    When you want to download annotations from Computer Vision Annotation Tool (CVAT) you can choose one of several data formats. The document describes XML annotation format. Each format has X.Y version (e.g. 1.0). In general the major version (X) is incremented when the data format has incompatible changes and the minor version (Y) is incremented when the data format is slightly modified (e.g. it has one or several extra fields inside meta information). The document will describe all changes for all versions of XML annotation format.

    Version 1.1

    There are two different formats for images and video tasks at the moment. The both formats have a common part which is described below. From the previous version flipped tag was added. Also original_size tag was added for interpolation mode to specify frame size. In annotation mode each image tag has width and height attributes for the same purpose.

    For what is rle, see Run-length encoding

    <?xml version="1.0" encoding="utf-8"?>
<annotations>
  <version>1.1</version>
  <meta>
    <task>
      <id>Number: id of the task</id>
      <name>String: some task name</name>
      <size>Number: count of frames/images in the task</size>
      <mode>String: interpolation or annotation</mode>
      <overlap>Number: number of overlapped frames between segments</overlap>
      <bugtracker>String: URL on an page which describe the task</bugtracker>
      <flipped>Boolean: were images of the task flipped? (True/False)</flipped>
      <created>String: date when the task was created</created>
      <updated>String: date when the task was updated</updated>
      <labels>
        <label>
          <name>String: name of the label (e.g. car, person)</name>
          <type>String: any, bbox, cuboid, cuboid_3d, ellipse, mask, polygon, polyline, points, skeleton, tag</type>
          <attributes>
            <attribute>
              <name>String: attribute name</name>
              <mutable>Boolean: mutable (allow different values between frames)</mutable>
              <input_type>String: select, checkbox, radio, number, text</input_type>
              <default_value>String: default value</default_value>
              <values>String: possible values, separated by newlines
ex. value 2
ex. value 3</values>
            </attribute>
          </attributes>
          <svg>String: label representation in svg, only for skeletons</svg>
          <parent>String: label parent name, only for skeletons</parent>
        </label>
      </labels>
      <segments>
        <segment>
          <id>Number: id of the segment</id>
          <start>Number: first frame</start>
          <stop>Number: last frame</stop>
          <url>String: URL (e.g. http://cvat.example.com/?id=213)</url>
        </segment>
      </segments>
      <owner>
        <username>String: the author of the task</username>
        <email>String: email of the author</email>
      </owner>
      <original_size>
        <width>Number: frame width</width>
        <height>Number: frame height</height>
      </original_size>
    </task>
    <dumped>String: date when the annotation was dumped</dumped>
  </meta>
  ...
</annotations>

    Annotation

    Below you can find description of the data format for images tasks. On each image it is possible to have many different objects. Each object can have multiple attributes. If an annotation task is created with z_order flag then each object will have z_order attribute which is used to draw objects properly when they are intersected (if z_order is bigger the object is closer to camera). In previous versions of the format only box shape was available. In later releases mask, polygon, polyline, points, skeletons and tags were added. Please see below for more details:

    <?xml version="1.0" encoding="utf-8"?>
<annotations>
  ...
  <image id="Number: id of the image (the index in lexical order of images)" name="String: path to the image"
    width="Number: image width" height="Number: image height">
    <box label="String: the associated label" xtl="Number: float" ytl="Number: float" xbr="Number: float" ybr="Number: float" occluded="Number: 0 - False, 1 - True" z_order="Number: z-order of the object">
      <attribute name="String: an attribute name">String: the attribute value</attribute>
      ...
    </box>
    <polygon label="String: the associated label" points="x0,y0;x1,y1;..." occluded="Number: 0 - False, 1 - True"
    z_order="Number: z-order of the object">
      <attribute name="String: an attribute name">String: the attribute value</attribute>
      ...
    </polygon>
    <polyline label="String: the associated label" points="x0,y0;x1,y1;..." occluded="Number: 0 - False, 1 - True"
    z_order="Number: z-order of the object">
      <attribute name="String: an attribute name">String: the attribute value</attribute>
      ...
    </polyline>
    <polyline label="String: the associated label" points="x0,y0;x1,y1;..." occluded="Number: 0 - False, 1 - True"
    z_order="Number: z-order of the object">
      <attribute name="String: an attribute name">String: the attribute value</attribute>
      ...
    </polyline>
    <points label="String: the associated label" points="x0,y0;x1,y1;..." occluded="Number: 0 - False, 1 - True"
    z_order="Number: z-order of the object">
      <attribute name="String: an attribute name">String: the attribute value</attribute>
      ...
    </points>
    <tag label="String: the associated label" source="manual or auto">
      <attribute name="String: an attribute name">String: the attribute value</attribute>
      ...
    </tag>
    <skeleton label="String: the associated label" z_order="Number: z-order of the object">
      <points label="String: the associated label" occluded="Number: 0 - False, 1 - True" outside="Number: 0 - False, 1 - True" points="x0,y0;x1,y1">
        <attribute name="String: an attribute name">String: the attribute value</attribute>
      </points>
      ...
      <attribute name="String: an attribute name">String: the attribute value</attribute>
      ...
    </skeleton>
    <mask label="String: the associated label" source="manual or auto" occluded="Number: 0 - False, 1 - True" rle="RLE mask" left="Number: left coordinate of the image where the mask begins" top="Number: top coordinate of the image where the mask begins" width="Number: width of the mask" height="Number: height of the mask" z_order="Number: z-order of the object">
    </mask>
    ...
  </image>
  ...
</annotations>

    Example:

    <?xml version="1.0" encoding="utf-8"?>
<annotations>
  <version>1.1</version>
  <meta>
    <task>
      <id>4</id>
      <name>segmentation</name>
      <size>27</size>
      <mode>annotation</mode>
      <overlap>0</overlap>
      <bugtracker></bugtracker>
      <flipped>False</flipped>
      <created>2018-09-25 11:34:24.617558+03:00</created>
      <updated>2018-09-25 11:38:27.301183+03:00</updated>
      <labels>
        <label>
          <name>car</name>
          <attributes>
          </attributes>
        </label>
        <label>
          <name>traffic_line</name>
          <attributes>
          </attributes>
        </label>
        <label>
          <name>wheel</name>
          <attributes>
          </attributes>
        </label>
        <label>
          <name>plate</name>
          <attributes>
          </attributes>
        </label>
        <label>
          <name>s1</name>
          <type>skeleton</type>
          <attributes>
          </attributes>
          <svg>&lt;line x1="36.87290954589844" y1="47.732025146484375" x2="86.87290954589844" y2="10.775501251220703" stroke="black" data-type="edge" data-node-from="2" stroke-width="0.5" data-node-to="3"&gt;&lt;/line&gt;&lt;line x1="25.167224884033203" y1="22.64841079711914" x2="36.87290954589844" y2="47.732025146484375" stroke="black" data-type="edge" data-node-from="1" stroke-width="0.5" data-node-to="2"&gt;&lt;/line&gt;&lt;circle r="1.5" stroke="black" fill="#b3b3b3" cx="25.167224884033203" cy="22.64841079711914" stroke-width="0.1" data-type="element node" data-element-id="1" data-node-id="1" data-label-name="1"&gt;&lt;/circle&gt;&lt;circle r="1.5" stroke="black" fill="#b3b3b3" cx="36.87290954589844" cy="47.732025146484375" stroke-width="0.1" data-type="element node" data-element-id="2" data-node-id="2" data-label-name="2"&gt;&lt;/circle&gt;&lt;circle r="1.5" stroke="black" fill="#b3b3b3" cx="86.87290954589844" cy="10.775501251220703" stroke-width="0.1" data-type="element node" data-element-id="3" data-node-id="3" data-label-name="3"&gt;&lt;/circle&gt;</svg>
        </label>
        <label>
          <name>1</name>
          <type>points</type>
          <attributes>
          </attributes>
          <parent>s1</parent>
        </label>
        <label>
          <name>2</name>
          <type>points</type>
          <attributes>
          </attributes>
          <parent>s1</parent>
        </label>
        <label>
          <name>3</name>
          <type>points</type>
          <attributes>
          </attributes>
          <parent>s1</parent>
        </label>
      </labels>
      <segments>
        <segment>
          <id>4</id>
          <start>0</start>
          <stop>26</stop>
          <url>http://localhost:8080/?id=4</url>
        </segment>
      </segments>
      <owner>
        <username>admin</username>
        <email></email>
      </owner>
    </task>
    <dumped>2018-09-25 11:38:28.799808+03:00</dumped>
  </meta>
  <image id="0" name="filename000.jpg" width="1600" height="1200">
    <box label="plate" xtl="797.33" ytl="870.92" xbr="965.52" ybr="928.94" occluded="0" z_order="4">
    </box>
    <polygon label="car" points="561.30,916.23;561.30,842.77;554.72,761.63;553.62,716.67;565.68,677.20;577.74,566.45;547.04,559.87;536.08,542.33;528.40,520.40;541.56,512.72;559.10,509.43;582.13,506.14;588.71,464.48;583.23,448.03;587.61,434.87;594.19,431.58;609.54,399.78;633.66,369.08;676.43,294.52;695.07,279.17;703.84,279.17;735.64,268.20;817.88,264.91;923.14,266.01;997.70,274.78;1047.04,283.55;1063.49,289.04;1090.90,330.70;1111.74,371.27;1135.86,397.59;1147.92,428.29;1155.60,435.97;1157.79,451.32;1156.69,462.28;1159.98,491.89;1163.27,522.59;1173.14,513.82;1199.46,516.01;1224.68,521.49;1225.77,544.52;1207.13,568.64;1181.91,576.32;1178.62,582.90;1177.53,619.08;1186.30,680.48;1199.46,711.19;1206.03,733.12;1203.84,760.53;1197.26,818.64;1199.46,840.57;1203.84,908.56;1192.88,930.49;1184.10,939.26;1162.17,944.74;1139.15,960.09;1058.01,976.54;1028.40,969.96;1002.09,972.15;931.91,974.35;844.19,972.15;772.92,972.15;729.06,967.77;713.71,971.06;685.20,973.25;659.98,968.86;644.63,984.21;623.80,983.12;588.71,985.31;560.20,966.67" occluded="0" z_order="1">
    </polygon>
    <polyline label="traffic_line" points="462.10,0.00;126.80,1200.00" occluded="0" z_order="3">
    </polyline>
    <polyline label="traffic_line" points="1212.40,0.00;1568.66,1200.00" occluded="0" z_order="2">
    </polyline>
    <points label="wheel" points="574.90,939.48;1170.16,907.90;1130.69,445.26;600.16,459.48" occluded="0" z_order="5">
    </points>
    <tag label="good_frame" source="manual">
    </tag>
    <skeleton label="s1" source="manual" z_order="0">
      <points label="1" occluded="0" source="manual" outside="0" points="54.47,94.81">
      </points>
      <points label="2" occluded="0" source="manual" outside="0" points="68.02,162.34">
      </points>
      <points label="3" occluded="0" source="manual" outside="0" points="125.87,62.85">
      </points>
    </skeleton>
    <mask label="car" source="manual" occluded="0" rle="3, 5, 7, 7, 5, 9, 3, 11, 2, 11, 2, 12, 1, 12, 1, 26, 1, 12, 1, 12, 2, 11, 3, 9, 5, 7, 7, 5, 3" left="707" top="888" width="13" height="15" z_order="0">
    </mask>
  </image>
</annotations>

    Interpolation

    Below you can find description of the data format for video tasks. The annotation contains tracks. Each track corresponds to an object which can be presented on multiple frames. The same object cannot be presented on the same frame in multiple locations. Each location of the object can have multiple attributes even if an attribute is immutable for the object it will be cloned for each location (a known redundancy).

    <?xml version="1.0" encoding="utf-8"?>
<annotations>
  ...
  <track id="Number: id of the track (doesn't have any special meeting)" label="String: the associated label" source="manual or auto">
    <box frame="Number: frame" xtl="Number: float" ytl="Number: float" xbr="Number: float" ybr="Number: float" outside="Number: 0 - False, 1 - True" occluded="Number: 0 - False, 1 - True" keyframe="Number: 0 - False, 1 - True">
      <attribute name="String: an attribute name">String: the attribute value</attribute>
      ...
    </box>
    <polygon frame="Number: frame" points="x0,y0;x1,y1;..." outside="Number: 0 - False, 1 - True" occluded="Number: 0 - False, 1 - True" keyframe="Number: 0 - False, 1 - True">
      <attribute name="String: an attribute name">String: the attribute value</attribute>
    </polygon>
    <polyline frame="Number: frame" points="x0,y0;x1,y1;..." outside="Number: 0 - False, 1 - True" occluded="Number: 0 - False, 1 - True" keyframe="Number: 0 - False, 1 - True">
      <attribute name="String: an attribute name">String: the attribute value</attribute>
    </polyline>
    <points frame="Number: frame" points="x0,y0;x1,y1;..." outside="Number: 0 - False, 1 - True" occluded="Number: 0 - False, 1 - True" keyframe="Number: 0 - False, 1 - True">
      <attribute name="String: an attribute name">String: the attribute value</attribute>
    </points>
    <mask frame="Number: frame" outside="Number: 0 - False, 1 - True" occluded="Number: 0 - False, 1 - True" rle="RLE mask" left="Number: left coordinate of the image where the mask begins" top="Number: top coordinate of the image where the mask begins" width="Number: width of the mask" height="Number: height of the mask" z_order="Number: z-order of the object">
    </mask>
    ...
  </track>
  <track id="Number: id of the track (doesn't have any special meeting)" label="String: the associated label" source="manual or auto">
    <skeleton frame="Number: frame" keyframe="Number: 0 - False, 1 - True">
      <points label="String: the associated label" outside="Number: 0 - False, 1 - True" occluded="Number: 0 - False, 1 - True" keyframe="Number: 0 - False, 1 - True" points="x0,y0;x1,y1">
      </points>
      ...
    </skeleton>
    ...
  </track>
  ...
</annotations>

    Example:

    <?xml version="1.0" encoding="utf-8"?>
<annotations>
  <version>1.1</version>
  <meta>
    <task>
      <id>5</id>
      <name>interpolation</name>
      <size>4620</size>
      <mode>interpolation</mode>
      <overlap>5</overlap>
      <bugtracker></bugtracker>
      <flipped>False</flipped>
      <created>2018-09-25 12:32:09.868194+03:00</created>
      <updated>2018-09-25 16:05:05.619841+03:00</updated>
      <labels>
        <label>
          <name>person</name>
          <attributes>
          </attributes>
        </label>
        <label>
          <name>car</name>
          <attributes>
          </attributes>
        </label>
        <label>
          <name>s1</name>
          <type>skeleton</type>
          <attributes>
          </attributes>
          <svg>&lt;line x1="36.87290954589844" y1="47.732025146484375" x2="86.87290954589844" y2="10.775501251220703" stroke="black" data-type="edge" data-node-from="2" stroke-width="0.5" data-node-to="3"&gt;&lt;/line&gt;&lt;line x1="25.167224884033203" y1="22.64841079711914" x2="36.87290954589844" y2="47.732025146484375" stroke="black" data-type="edge" data-node-from="1" stroke-width="0.5" data-node-to="2"&gt;&lt;/line&gt;&lt;circle r="1.5" stroke="black" fill="#b3b3b3" cx="25.167224884033203" cy="22.64841079711914" stroke-width="0.1" data-type="element node" data-element-id="1" data-node-id="1" data-label-name="1"&gt;&lt;/circle&gt;&lt;circle r="1.5" stroke="black" fill="#b3b3b3" cx="36.87290954589844" cy="47.732025146484375" stroke-width="0.1" data-type="element node" data-element-id="2" data-node-id="2" data-label-name="2"&gt;&lt;/circle&gt;&lt;circle r="1.5" stroke="black" fill="#b3b3b3" cx="86.87290954589844" cy="10.775501251220703" stroke-width="0.1" data-type="element node" data-element-id="3" data-node-id="3" data-label-name="3"&gt;&lt;/circle&gt;</svg>
        </label>
        <label>
          <name>1</name>
          <type>points</type>
          <attributes>
          </attributes>
          <parent>s1</parent>
        </label>
        <label>
          <name>2</name>
          <type>points</type>
          <attributes>
          </attributes>
          <parent>s1</parent>
        </label>
        <label>
          <name>3</name>
          <type>points</type>
          <attributes>
          </attributes>
          <parent>s1</parent>
        </label>
      </labels>
      <segments>
        <segment>
          <id>5</id>
          <start>0</start>
          <stop>4619</stop>
          <url>http://localhost:8080/?id=5</url>
        </segment>
      </segments>
      <owner>
        <username>admin</username>
        <email></email>
      </owner>
      <original_size>
        <width>640</width>
        <height>480</height>
      </original_size>
    </task>
    <dumped>2018-09-25 16:05:07.134046+03:00</dumped>
  </meta>
  <track id="0" label="car">
    <polygon frame="0" points="324.79,213.16;323.74,227.90;347.42,237.37;371.11,217.37;350.05,190.00;318.47,191.58" outside="0" occluded="0" keyframe="1">
    </polygon>
    <polygon frame="1" points="324.79,213.16;323.74,227.90;347.42,237.37;371.11,217.37;350.05,190.00;318.47,191.58" outside="1" occluded="0" keyframe="1">
    </polygon>
    <polygon frame="6" points="305.32,237.90;312.16,207.90;352.69,206.32;355.32,233.16;331.11,254.74" outside="0" occluded="0" keyframe="1">
    </polygon>
    <polygon frame="7" points="305.32,237.90;312.16,207.90;352.69,206.32;355.32,233.16;331.11,254.74" outside="1" occluded="0" keyframe="1">
    </polygon>
    <polygon frame="13" points="313.74,233.16;331.11,220.00;359.53,243.16;333.21,283.16;287.95,274.74" outside="0" occluded="0" keyframe="1">
    </polygon>
    <polygon frame="14" points="313.74,233.16;331.11,220.00;359.53,243.16;333.21,283.16;287.95,274.74" outside="1" occluded="0" keyframe="1">
    </polygon>
  </track>
  <track id="1" label="s1" source="manual">
    <skeleton frame="0" keyframe="1" z_order="0">
      <points label="1" outside="0" occluded="0" keyframe="1" points="112.07,258.59">
      </points>
      <points label="2" outside="0" occluded="0" keyframe="1" points="127.87,333.23">
      </points>
      <points label="3" outside="0" occluded="0" keyframe="1" points="195.37,223.27">
      </points>
    </skeleton>
    <skeleton frame="1" keyframe="1" z_order="0">
      <points label="1" outside="1" occluded="0" keyframe="1" points="112.07,258.59">
      </points>
      <points label="2" outside="1" occluded="0" keyframe="1" points="127.87,333.23">
      </points>
      <points label="3" outside="1" occluded="0" keyframe="1" points="195.37,223.27">
      </points>
    </skeleton>
    <skeleton frame="6" keyframe="1" z_order="0">
      <points label="1" outside="0" occluded="0" keyframe="0" points="120.07,270.59">
      </points>
      <points label="2" outside="0" occluded="0" keyframe="0" points="140.87,350.23">
      </points>
      <points label="3" outside="0" occluded="0" keyframe="0" points="210.37,260.27">
      </points>
    </skeleton>
    <skeleton frame="7" keyframe="1" z_order="0">
      <points label="1" outside="1" occluded="0" keyframe="1" points="120.07,270.59">
      </points>
      <points label="2" outside="1" occluded="0" keyframe="1" points="140.87,350.23">
      </points>
      <points label="3" outside="1" occluded="0" keyframe="1" points="210.37,260.27">
      </points>
    </skeleton>
    <skeleton frame="13" keyframe="0" z_order="0">
      <points label="1" outside="0" occluded="0" keyframe="0" points="112.07,258.59">
      </points>
      <points label="2" outside="0" occluded="0" keyframe="0" points="127.87,333.23">
      </points>
      <points label="3" outside="0" occluded="0" keyframe="0" points="195.37,223.27">
      </points>
    </skeleton>
    <skeleton frame="14" keyframe="1" z_order="0">
      <points label="1" outside="1" occluded="0" keyframe="1" points="112.07,258.59">
      </points>
      <points label="2" outside="1" occluded="0" keyframe="1" points="127.87,333.23">
      </points>
      <points label="3" outside="1" occluded="0" keyframe="1" points="195.37,223.27">
      </points>
    </skeleton>
  </track>
</annotations>

    30 - Shortcuts

    List of available keyboard shortcuts and notes about their customization.

    CVAT provides a wide range of customizable shortcuts, with many UI elements offering shortcut hints when hovered over with the mouse.

    These shortcuts are organized by scopes. Some are global, meaning they work across the entire application, while others are specific to certain sections or workspaces. This approach allows reusing the same shortcuts in different scopes, depending on whether they might conflict. For example, global shortcuts must be unique since they apply across all pages and workspaces. However, similar shortcuts can be used in different workspaces, like having the same shortcuts in both the Standard Workspace and the Standard 3D Workspace, as these two do not coexist.

    Scope Shortcut Conflicts
    Global Must be unique across all scopes, as they apply universally.
    Annotation Page Must be unique across all scopes, except Labels Editor.
    Standard Workspace Must be unique across itself, Annotation Page and Global Scope.
    Standard 3D Workspace Must be unique across itself, Annotation Page and Global Scope.
    Attribute Annotation Workspace Must be unique across itself, Annotation Page and Global Scope.
    Review Workspace Must be unique across itself, Annotation Page and Global Scope.
    Tag Annotation Workspace Must be unique across itself, Annotation Page and Global Scope.
    Control Sidebar Must be unique across itself, all workspaces, Annotation Page and Global Scope.
    Objects Sidebar Must be unique across itself, all workspaces, Annotation Page and Global Scope.
    Labels Editor Must be unique across itself and Global Scope.

    Shortcuts Customization

    You can customize shortcuts in CVAT settings.

    • Open Settings:

    • Go to the Shortcuts tab:

    • You’ll see the shortcuts customization menu:

    • As it can be seen there is a warning, that some shortcuts are reserved by a browser and cannot be overridden in CVAT, there isn’t a specific list available for such combinations, but shortcuts such as ctrl + tab (switching tabs) or ctrl + w (closing tabs) etc, are reserved by the browser and shortcuts such as alt + f4 (closing the window) are usually reserved by your operating system.

    • All sections collapsable, so you can easily navigate through the list of shortcuts. Here is the Global scope expanded:

    • To add a custom shortcut all you have to do is to click the input field and start pressing the sequence you want to assign to the action. As an example f3 has been set here for Show Shortcuts along with f1:

    • Shortcuts can be any combination of modifiers (ctrl, shift or alt) and up to one non-modifier key e.g. ctrl+shift+f1 etc.

    • If you try to add a shortcut that is already in use, you will get a warning message:

    • If pressed cancel it will remain the same otherwise the conflicting shortcut will be unset.

    • If you want to reset all the shortcuts to default, you can do so by clicking the Restore Defaults button at the top of the shortcut settings.

    31 - Filter

    Guide to using the Filter feature in CVAT.

    There are some reasons to use the feature:

    1. When you use a filter, objects that don’t match the filter will be hidden.
    2. The fast navigation between frames which have an object of interest. Use the Left Arrow / Right Arrow keys for this purpose or customize the UI buttons by right-clicking and select switching by filter. If there are no objects which correspond to the filter, you will go to the previous / next frame which contains any annotated objects.

    To apply filters you need to click on the button on the top panel.

    Create a filter

    It will open a window for filter input. Here you will find two buttons: Add rule and Add group.

    Rules

    The Add rule button adds a rule for objects display. A rule may use the following properties:

    Supported properties for annotation

    Properties Supported values Description
    Label all the label names that are in the task label name
    Type shape, track or tag type of object
    Shape all shape types type of shape
    Occluded true or false occluded (read more)
    Width number of px or field shape width
    Height number of px or field shape height
    ServerID number or field ID of the object on the server
    (You can find out by forming a link to the object through the Action menu)
    ObjectID number or field ID of the object in your client
    (indicated on the objects sidebar)
    Attributes some other fields including attributes with a
    similar type or a specific attribute value    		 any fields specified by a label

    Supported operators for properties

    == - Equally; != - Not equal; > - More; >= - More or equal; < - Less; <= - Less or equal;

    Any in; Not in - these operators allow you to set multiple values in one rule;

    Is empty; is not empty – these operators don’t require to input a value.

    Between; Not between – these operators allow you to choose a range between two values.

    Like - this operator indicate that the property must contain a value.

    Starts with; Ends with - filter by beginning or end.

    Some properties support two types of values that you can choose:

    You can add multiple rules, to do so click the add rule button and set another rule. Once you’ve set a new rule, you’ll be able to choose which operator they will be connected by: And or Or.

    All subsequent rules will be joined by the chosen operator. Click Submit to apply the filter or if you want multiple rules to be connected by different operators, use groups.

    Groups

    To add a group, click the Add group button. Inside the group you can create rules or groups.

    If there is more than one rule in the group, they can be connected by And or Or operators. The rule group will work as well as a separate rule outside the group and will be joined by an operator outside the group. You can create groups within other groups, to do so you need to click the add group button within the group.

    You can move rules and groups. To move the rule or group, drag it by the button. To remove the rule or group, click on the Delete button.

    If you activate the Not button, objects that don’t match the group will be filtered out. Click Submit to apply the filter. The Cancel button undoes the filter. The Clear filter button removes the filter.

    Once applied filter automatically appears in Recent used list. Maximum length of the list is 10.

    Sort and filter lists

    On the projects, task list on the project page, tasks, jobs, and cloud storage pages, you can use sorting and filters.

    The applied filter and sorting will be displayed in the URL of your browser, Thus, you can share the page with sorting and filter applied.

    Sort by

    You can sort by the following parameters:

    • Jobs list: ID, assignee, updated date, stage, state, task ID, project ID, task name, project name.
    • Tasks list or tasks list on project page: ID, owner, status, assignee, updated date, subset, mode, dimension, project ID, name, project name.
    • Projects list: ID, assignee, owner, status, name, updated date.
    • Cloud storages list: ID, provider type, updated date, display name, resource, credentials, owner, description.

    To apply sorting, drag the parameter to the top area above the horizontal bar. The parameters below the horizontal line will not be applied. By moving the parameters you can change the priority, first of all sorting will occur according to the parameters that are above.

    Pressing the Sort button switches Ascending sort/Descending sort.

    Quick filters

    Quick Filters contain several frequently used filters:

    • Assigned to me - show only those projects, tasks or jobs that are assigned to you.
    • Owned by me - show only those projects or tasks that are owned by you.
    • Not completed - show only those projects, tasks or jobs that have a status other than completed.
    • AWS storages - show only AWS cloud storages
    • Azure storages - show only Azure cloud storages
    • Google cloud storages - show only Google cloud storages

    Date and time selection

    When creating a Last updated rule, you can select the date and time by using the selection window.

    You can select the year and month using the arrows or by clicking on the year and month. To select a day, click on it in the calendar, To select the time, you can select the hours and minutes using the scrolling list. Or you can select the current date and time by clicking the Now button. To apply, click Ok.

    32 - Contextual images

    Contextual images of the task

    Contextual images are additional images that provide context or additional information related to the primary image.

    Use them to add extra contextual about the object to improve the accuracy of annotation.

    Contextual images are available for 2D and 3D tasks.

    See:

    Folder structure

    To add contextual images to the task, you need to organize the images folder.

    Before uploading the archive to CVAT, do the following:

    1. In the folder with the images for annotation, create a folder: related_images.
    2. Add to the related_images a subfolder with the same name as the primary image to which it should be linked.
    3. Place the contextual image(s) within the subfolder created in step 2.
    4. Add folder to the archive.
    5. Create task.

    Data format

    Example file structure for 2D and 3D tasks:

      root_directory
    image_1_to_be_annotated.jpg
    image_2_to_be_annotated.jpg
    related_images/
      image_1_to_be_annotated_jpg/
        context_image_for_image_1.jpg
      image_2_to_be_annotated_jpg/
        context_image_for_image_2.jpg
     subdirectory_example/
        image_3_to_be_annotated.jpg
         related_images/
          image_3_to_be_annotated_jpg/
             context_image_for_image_3.jpg
     root_directory
    pointcloud/
      image_1_to_be_annotated.pcd
      image_2_to_be_annotated.pcd
    related_images/
      image_1_to_be_annotated_pcd/
        context_image_for_image_1.jpg
      image_2_to_be_annotated_pcd/
        context_image_for_image_2.jpg
     /any_directory
    pointcloud.pcd
    pointcloud.jpg
/any_other_directory
    /any_subdirectory
        pointcloud.pcd
        pointcloud.png
     /image_00
    /data
        /0000000000.png
        /0000000001.png
        /0000000002.png
        /0000000003.png
/image_01
    /data
        /0000000000.png
        /0000000001.png
        /0000000002.png
        /0000000003.png
/image_02
    /data
        /0000000000.png
        /0000000001.png
        /0000000002.png
        /0000000003.png
/image_N
    /data
        /0000000000.png
        /0000000001.png
        /0000000002.png
        /0000000003.png
/velodyne_points
    /data
        /0000000000.bin
        /0000000001.bin
        /0000000002.bin
        /0000000003.bin
    • For KITTI: image_00, image_01, image_02, image_N, (where N is any number <= 12) are context images.
    • For 3D option 3: a regular image file placed near a .pcd file with the same name is considered to be a context image.

    For more general information about 3D data formats, see 3D data formats.

    Contextual images

    The maximum amount of contextual images is twelve.

    By default they will be positioned on the right side of the main image.

    Note: By default, only three contextual images will be visible.

    contex_images_1

    When you add contextual images to the set, small toolbar will appear on the top of the screen, with the following elements:

    Element Description
    contex_images_4 Fit views. Click to restore the layout to its original appearance.

    If you’ve expanded any images in the layout, they will returned to their original size.

    This won’t affect the number of context images on the screen.
    contex_images_5 Add new image. Click to add context image to the layout.
    contex_images_6 Reload layout. Click to reload layout to the default view.

    Note, that this action can change the number of context images resetting them back to three.

    Each context image has the following elements:

    contex_images_2

    Element Description
    1 Full screen. Click to expand the contextual image in to the full screen mode.

    Click again to revert contextual image to windowed mode.
    2 Move contextual image. Hold and move contextual image to the other place on the screen.

    contex_images_3
    3 Name. Unique contextual image name
    4 Select contextual image. Click to open a horizontal listview of all available contextual images.

    Click on one to select.
    5 Close. Click to remove image from contextual images menu.
    6 Extend Hold and pull to extend the image.

    33 - Shape grouping

    Grouping multiple shapes during annotation.

    This feature allows us to group several shapes.

    You may use the Group Shapes button or shortcuts:

    • G — start selection / end selection in group mode
    • Esc — close group mode
    • Shift+G — reset group for selected shapes

    You may select shapes clicking on them or selecting an area.

    Grouped shapes will have group_id filed in dumped annotation.

    Also you may switch color distribution from an instance (default) to a group. You have to switch Color By Group checkbox for that.

    Shapes that don’t have group_id, will be highlighted in white.

    Shapes grouping video tutorial

    34 - Dataset Manifest

    Overview

    When we create a new task in CVAT, we need to specify where to get the input data from. CVAT allows to use different data sources, including local file uploads, a mounted file share on the server, cloud storages and remote URLs. In some cases CVAT needs to have extra information about the input data. This information can be provided in Dataset manifest files. They are mainly used when working with cloud storages to reduce the amount of network traffic used and speed up the task creation process. However, they can also be used in other cases, which will be explained below.

    A dataset manifest file is a text file in the JSONL format. These files can be created automatically with the special command-line tool, or manually, following the manifest file format specification.

    How and when to use manifest files

    Manifest files can be used in the following cases:

    • A video file or a set of images is used as the data source and the caching mode is enabled. Read more
    • The data is located in a cloud storage. Read more
    • The predefined file sorting method is specified. Read more

    The predefined sorting method

    Independently of the file source being used, when the predefined sorting method is selected in the task configuration, the source files will be ordered according to the .jsonl manifest file, if it is found in the input list of files. If a manifest is not found, the order provided in the input file list is used.

    For image archives (e.g. .zip), a manifest file (*.jsonl) is required when using the predefined file ordering. A manifest file must be provided next to the archive in the input list of files, it must not be inside the archive.

    If there are multiple manifest files in the input file list, an error will be raised.

    How to generate manifest files

    CVAT provides a dedicated Python tool to generate manifest files. The source code can be found here.

    Using the tool is the recommended way to create manifest files for you data. The data must be available locally to the tool to generate manifest.

    Usage

    usage: create.py [-h] [--force] [--output-dir .] source

positional arguments:
  source                Source paths

optional arguments:
  -h, --help            show this help message and exit
  --force               Use this flag to prepare the manifest file for video data
                        if by default the video does not meet the requirements
                        and a manifest file is not prepared
  --output-dir OUTPUT_DIR
                        Directory where the manifest file will be saved

    Use the script from a Docker image

    This is the recommended way to use the tool.

    The script can be used from the cvat/server image:

    docker run -it --rm -u "$(id -u)":"$(id -g)" \
  -v "${PWD}":"/local" \
  --entrypoint python3 \
  cvat/server \
  utils/dataset_manifest/create.py --output-dir /local /local/<path/to/sources>

    Make sure to adapt the command to your file locations.

    Use the script directly

    Ubuntu 20.04

    Install dependencies:

    # General
sudo apt-get update && sudo apt-get --no-install-recommends install -y \
    python3-dev python3-pip python3-venv pkg-config

    # Library components
sudo apt-get install --no-install-recommends -y \
    libavformat-dev libavcodec-dev libavdevice-dev \
    libavutil-dev libswscale-dev libswresample-dev libavfilter-dev

    Create an environment and install the necessary python modules:

    python3 -m venv .env
. .env/bin/activate
pip install -U pip
pip install -r utils/dataset_manifest/requirements.in

    Please note that if used with video this way, the results may be different from what would the server decode. It is related to the ffmpeg library version. For this reason, using the Docker-based version of the tool is recommended.

    Examples

    Create a dataset manifest in the current directory with video which contains enough keyframes:

    python utils/dataset_manifest/create.py ~/Documents/video.mp4

    Create a dataset manifest with video which does not contain enough keyframes:

    python utils/dataset_manifest/create.py --force --output-dir ~/Documents ~/Documents/video.mp4

    Create a dataset manifest with images:

    python utils/dataset_manifest/create.py --output-dir ~/Documents ~/Documents/images/

    Create a dataset manifest with pattern (may be used *, ?, []):

    python utils/dataset_manifest/create.py --output-dir ~/Documents "/home/${USER}/Documents/**/image*.jpeg"

    Create a dataset manifest using Docker image:

    docker run -it --rm -u "$(id -u)":"$(id -g)" \
  -v ~/Documents/data/:${HOME}/manifest/:rw \
  --entrypoint '/usr/bin/bash' \
  cvat/server \
  utils/dataset_manifest/create.py --output-dir ~/manifest/ ~/manifest/images/

    File format

    The dataset manifest files are text files in JSONL format. These files have 2 sub-formats: for video and for images and 3d data.

    Each top-level entry enclosed in curly braces must use 1 string, no empty strings is allowed. The formatting in the descriptions below is only for demonstration.

    Dataset manifest for video

    The file describes a single video.

    pts - time at which the frame should be shown to the user checksum - md5 hash sum for the specific image/frame decoded

    { "version": <string, version id> }
{ "type": "video" }
{ "properties": {
  "name": <string, filename>,
  "resolution": [<int, width>, <int, height>],
  "length": <int, frame count>
}}
{
  "number": <int, frame number>,
  "pts": <int, frame pts>,
  "checksum": <string, md5 frame hash>
} (repeatable)

    Dataset manifest for images and other data types

    The file describes an ordered set of images and 3d point clouds.

    name - file basename and leading directories from the dataset root checksum - md5 hash sum for the specific image/frame decoded

    { "version": <string, version id> }
{ "type": "images" }
{
  "name": <string, image filename>,
  "extension": <string, . + file extension>,
  "width": <int, width>,
  "height": <int, height>,
  "meta": <dict, optional>,
  "checksum": <string, md5 hash, optional>
} (repeatable)

    Example files

    Manifest for a video

    {"version":"1.0"}
{"type":"video"}
{"properties":{"name":"video.mp4","resolution":[1280,720],"length":778}}
{"number":0,"pts":0,"checksum":"17bb40d76887b56fe8213c6fded3d540"}
{"number":135,"pts":486000,"checksum":"9da9b4d42c1206d71bf17a7070a05847"}
{"number":270,"pts":972000,"checksum":"a1c3a61814f9b58b00a795fa18bb6d3e"}
{"number":405,"pts":1458000,"checksum":"18c0803b3cc1aa62ac75b112439d2b62"}
{"number":540,"pts":1944000,"checksum":"4551ecea0f80e95a6c32c32e70cac59e"}
{"number":675,"pts":2430000,"checksum":"0e72faf67e5218c70b506445ac91cdd7"}

    Manifest for a dataset with images

    {"version":"1.0"}
{"type":"images"}
{"name":"image1","extension":".jpg","width":720,"height":405,"meta":{"related_images":[]},"checksum":"548918ec4b56132a5cff1d4acabe9947"}
{"name":"image2","extension":".jpg","width":183,"height":275,"meta":{"related_images":[]},"checksum":"4b4eefd03cc6a45c1c068b98477fb639"}
{"name":"image3","extension":".jpg","width":301,"height":167,"meta":{"related_images":[]},"checksum":"0e454a6f4a13d56c82890c98be063663"}

    35 - Data preparation on the fly

    Description

    Data on the fly processing is a way of working with data, the main idea of which is as follows: when creating a task, the minimum necessary meta information is collected. This meta information allows in the future to create necessary chunks when receiving a request from a client.

    Generated chunks are stored in a cache of the limited size with a policy of evicting less popular items.

    When a request is received from a client, the required chunk is searched for in the cache. If the chunk does not exist yet, it is created using prepared meta information and then put into the cache.

    This method of working with data allows:

    • reduce the task creation time.
    • store data in a cache of the limited size with a policy of evicting less popular items.

    Unfortunately, this method has several drawbacks:

    • The first access to the data will take more time.
    • It will not work for some videos, even if they have a valid manifest file. If there are not enough keyframes in the video for smooth video decoding, the task data chunks will be created with the default method, i.e. during the task creation.
    • If the data has not been cached yet, and is not reachable during the access time, it cannot be retrieved.

    How to use

    To enable or disable this feature for a new task, use the Use Cache toggle in the task configuration.

    Uploading a manifest with data

    When creating a task, you can upload a manifest.jsonl file along with the video or dataset with images. You can see how to prepare it here.

    36 - Serverless tutorial

    Introduction

    Leveraging the power of computers to solve daily routine problems, fix mistakes, and find information has become second nature. It is therefore natural to use computing power in annotating datasets. There are multiple publicly available DL models for classification, object detection, and semantic segmentation which can be used for data annotation. Whilst some of these publicly available DL models can be found on CVAT, it is relatively simple to integrate your privately trained ML/DL model into CVAT.

    With the imperfection of the world, alongside the unavailability of a silver bullet that can solve all our problems; publicly available DL models cannot be used when we want to detect niche or specific objects on which these publicly available models were not trained. As annotation requirements can be sometimes strict, automatically annotated objects cannot be accepted as it is, and it is easier to annotate them from scratch. With these limitations in mind, a DL solution that can perfectly annotate 50% of your data equates to reducing manual annotation by half.

    Since we know DL models can help us to annotate faster, how then do we use them? In CVAT all such DL models are implemented as serverless functions using the Nuclio serverless platform. There are multiple implemented functions that can be found in the serverless directory such as Mask RCNN, Faster RCNN, SiamMask, Inside Outside Guidance, Deep Extreme Cut, etc. Follow the installation guide to build and deploy these serverless functions. See the user guide to understand how to use these functions in the UI to automatically annotate data.

    What is a serverless function and why is it used for automatic annotation in CVAT? Let’s assume that you have a DL model and want to use it for AI-assisted annotation. The naive approach is to implement a Python script which uses the DL model to prepare a file with annotations in a public format like MS COCO or Pascal VOC. After that you can upload the annotation file into CVAT. It works but it is not user-friendly. How to make CVAT run the script for you?

    You can pack the script with your DL model into a container which provides a standard interface for interacting with it. One way to do that is to use the function as a service approach. Your script becomes a function inside cloud infrastructure which can be called over HTTP. The Nuclio serverless platform helps us to implement and manage such functions.

    CVAT supports Nuclio out of the box if it is built properly. See the installation guide for instructions. Thus if you deploy a serverless function, the CVAT server can see it and call it with appropriate arguments. Of course there are some tricks how to create serverless functions for CVAT and we will discuss them in next sections of the tutorial.

    Using builtin DL models in practice

    In the tutorial it is assumed that you already have the cloned CVAT GitHub repo. To build CVAT with serverless support you need to run docker compose command with specific configuration files. In the case it is docker-compose.serverless.yml. It has necessary instructions how to build and deploy Nuclio platform as a docker container and enable corresponding support in CVAT.

    docker compose -f docker-compose.yml -f docker-compose.dev.yml -f components/serverless/docker-compose.serverless.yml up -d --build

    docker compose -f docker-compose.yml -f docker-compose.dev.yml -f components/serverless/docker-compose.serverless.yml ps

       Name                 Command                  State                            Ports
-------------------------------------------------------------------------------------------------------------
cvat         /usr/bin/supervisord             Up             8080/tcp
cvat_db      docker-entrypoint.sh postgres    Up             5432/tcp
cvat_proxy   /docker-entrypoint.sh /bin ...   Up             0.0.0.0:8080->80/tcp,:::8080->80/tcp
cvat_redis   docker-entrypoint.sh redis ...   Up             6379/tcp
cvat_ui      /docker-entrypoint.sh ngin ...   Up             80/tcp
nuclio       /docker-entrypoint.sh sh - ...   Up (healthy)   80/tcp, 0.0.0.0:8070->8070/tcp,:::8070->8070/tcp

    Next step is to deploy builtin serverless functions using Nuclio command line tool (aka nuctl). It is assumed that you followed the installation guide and nuctl is already installed on your operating system. Run the following command to check that it works. In the beginning you should not have any deployed serverless functions.

    nuctl get functions

    No functions found

    Let’s see on examples how to use DL models for annotation in different computer vision tasks.

    Tracking using SiamMask

    In this use case a user needs to annotate all individual objects on a video as tracks. Basically for every object we need to know its location on every frame.

    First step is to deploy SiamMask. The deployment process can depend on your operating system. On Linux you can use serverless/deploy_cpu.sh auxiliary script, but below we are using nuctl directly.

    nuctl create project cvat

nuctl deploy --project-name cvat --path "./serverless/pytorch/foolwood/siammask/nuclio" --platform local

    24.04.18 20:52:47.910 (I)                     nuctl Deploying function {"name": "pth-foolwood-siammask"}
24.04.18 20:52:47.910 (I)                     nuctl Building {"builderKind": "docker", "versionInfo": "Label: 1.13.0, Git commit: c4422eb772781fb50fbf017698aae96199d81388, OS: linux, Arch: amd64, Go version: go1.21.7", "name": "pth-foolwood-siammask"}
24.04.18 20:52:47.929 (W)            nuctl.platform MaxWorkers is deprecated and will be removed in v1.15.x, use NumWorkers instead
24.04.18 20:52:48.044 (I)                     nuctl Staging files and preparing base images
24.04.18 20:52:48.044 (W)                     nuctl Using user provided base image, runtime interpreter version is provided by the base image {"baseImage": "ubuntu:20.04"}
24.04.18 20:52:48.044 (I)                     nuctl Building processor image {"registryURL": "", "taggedImageName": "cvat.pth.foolwood.siammask:latest"}
24.04.18 20:52:48.044 (I)     nuctl.platform.docker Pulling image {"imageName": "quay.io/nuclio/handler-builder-python-onbuild:1.13.0-amd64"}
24.04.18 20:52:49.717 (I)     nuctl.platform.docker Pulling image {"imageName": "quay.io/nuclio/uhttpc:0.0.1-amd64"}
24.04.18 20:52:51.363 (I)            nuctl.platform Building docker image {"image": "cvat.pth.foolwood.siammask:latest"}
24.04.18 20:55:58.853 (I)            nuctl.platform Pushing docker image into registry {"image": "cvat.pth.foolwood.siammask:latest", "registry": ""}
24.04.18 20:55:58.853 (I)            nuctl.platform Docker image was successfully built and pushed into docker registry {"image": "cvat.pth.foolwood.siammask:latest"}
24.04.18 20:55:58.853 (I)                     nuctl Build complete {"image": "cvat.pth.foolwood.siammask:latest"}
24.04.18 20:55:58.861 (I)                     nuctl Cleaning up before deployment {"functionName": "pth-foolwood-siammask"}
24.04.18 20:55:59.593 (I)            nuctl.platform Waiting for function to be ready {"timeout": 120}
24.04.18 20:56:01.315 (I)                     nuctl Function deploy complete {"functionName": "pth-foolwood-siammask", "httpPort": 33453, "internalInvocationURLs": ["172.17.0.5:8080"], "externalInvocationURLs": ["0.0.0.0:33453"]}

    nuctl get functions

      NAMESPACE |         NAME          | PROJECT | STATE | NODE PORT | REPLICAS
  nuclio    | pth-foolwood-siammask | cvat    | ready |     49155 | 1/1

    Let’s see how it works in the UI. Go to the models tab and check that you can see SiamMask in the list. If you cannot, it means that there are some problems. Go to one of our public channels and ask for help.

    Models list with SiamMask

    After that, go to the new task page and create a task with this video file. You can choose any task name, any labels, and even another video file if you like. In this case, the Remote sources option was used to specify the video file. Press submit button at the end to finish the process.

    Create a video annotation task

    Open the task and use AI tools to start tracking an object. Draw a bounding box around an object, and sequentially switch through the frame and correct the restrictive box if necessary.

    Start tracking an object

    Finally you will get bounding boxes.

    SiamMask results

    SiamMask model is more optimized to work on Nvidia GPUs. For more information about deploying the model for the GPU, read on.

    Object detection using YOLO-v3

    First of all let’s deploy the DL model. The deployment process is similar for all serverless functions. Need to run nuctl deploy command with appropriate arguments. To simplify the process, you can use serverless/deploy_cpu.sh command. Inference of the serverless function is optimized for CPU using Intel OpenVINO framework.

    serverless/deploy_cpu.sh serverless/openvino/omz/public/yolo-v3-tf/

    Deploying serverless/openvino/omz/public/yolo-v3-tf function...
21.07.12 15:55:17.314                     nuctl (I) Deploying function {"name": ""}
21.07.12 15:55:17.314                     nuctl (I) Building {"versionInfo": "Label: 1.5.16, Git commit: ae43a6a560c2bec42d7ccfdf6e8e11a1e3cc3774, OS: linux, Arch: amd64, Go version: go1.14.3", "name": ""}
21.07.12 15:55:17.682                     nuctl (I) Cleaning up before deployment {"functionName": "openvino-omz-public-yolo-v3-tf"}
21.07.12 15:55:17.739                     nuctl (I) Staging files and preparing base images
21.07.12 15:55:17.743                     nuctl (I) Building processor image {"imageName": "cvat/openvino.omz.public.yolo-v3-tf:latest"}
21.07.12 15:55:17.743     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/handler-builder-python-onbuild:1.5.16-amd64"}
21.07.12 15:55:21.048     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/uhttpc:0.0.1-amd64"}
21.07.12 15:55:24.595            nuctl.platform (I) Building docker image {"image": "cvat/openvino.omz.public.yolo-v3-tf:latest"}
21.07.12 15:55:30.359            nuctl.platform (I) Pushing docker image into registry {"image": "cvat/openvino.omz.public.yolo-v3-tf:latest", "registry": ""}
21.07.12 15:55:30.359            nuctl.platform (I) Docker image was successfully built and pushed into docker registry {"image": "cvat/openvino.omz.public.yolo-v3-tf:latest"}
21.07.12 15:55:30.359                     nuctl (I) Build complete {"result": {"Image":"cvat/openvino.omz.public.yolo-v3-tf:latest","UpdatedFunctionConfig":{"metadata":{"name":"openvino-omz-public-yolo-v3-tf","namespace":"nuclio","labels":{"nuclio.io/project-name":"cvat"},"annotations":{"framework":"openvino","name":"YOLO v3","spec":"[\n  { \"id\": 0, \"name\": \"person\" },\n  { \"id\": 1, \"name\": \"bicycle\" },\n  { \"id\": 2, \"name\": \"car\" },\n  { \"id\": 3, \"name\": \"motorbike\" },\n  { \"id\": 4, \"name\": \"aeroplane\" },\n  { \"id\": 5, \"name\": \"bus\" },\n  { \"id\": 6, \"name\": \"train\" },\n  { \"id\": 7, \"name\": \"truck\" },\n  { \"id\": 8, \"name\": \"boat\" },\n  { \"id\": 9, \"name\": \"traffic light\" },\n  { \"id\": 10, \"name\": \"fire hydrant\" },\n  { \"id\": 11, \"name\": \"stop sign\" },\n  { \"id\": 12, \"name\": \"parking meter\" },\n  { \"id\": 13, \"name\": \"bench\" },\n  { \"id\": 14, \"name\": \"bird\" },\n  { \"id\": 15, \"name\": \"cat\" },\n  { \"id\": 16, \"name\": \"dog\" },\n  { \"id\": 17, \"name\": \"horse\" },\n  { \"id\": 18, \"name\": \"sheep\" },\n  { \"id\": 19, \"name\": \"cow\" },\n  { \"id\": 20, \"name\": \"elephant\" },\n  { \"id\": 21, \"name\": \"bear\" },\n  { \"id\": 22, \"name\": \"zebra\" },\n  { \"id\": 23, \"name\": \"giraffe\" },\n  { \"id\": 24, \"name\": \"backpack\" },\n  { \"id\": 25, \"name\": \"umbrella\" },\n  { \"id\": 26, \"name\": \"handbag\" },\n  { \"id\": 27, \"name\": \"tie\" },\n  { \"id\": 28, \"name\": \"suitcase\" },\n  { \"id\": 29, \"name\": \"frisbee\" },\n  { \"id\": 30, \"name\": \"skis\" },\n  { \"id\": 31, \"name\": \"snowboard\" },\n  { \"id\": 32, \"name\": \"sports ball\" },\n  { \"id\": 33, \"name\": \"kite\" },\n  { \"id\": 34, \"name\": \"baseball bat\" },\n  { \"id\": 35, \"name\": \"baseball glove\" },\n  { \"id\": 36, \"name\": \"skateboard\" },\n  { \"id\": 37, \"name\": \"surfboard\" },\n  { \"id\": 38, \"name\": \"tennis racket\" },\n  { \"id\": 39, \"name\": \"bottle\" },\n  { \"id\": 40, \"name\": \"wine glass\" },\n  { \"id\": 41, \"name\": \"cup\" },\n  { \"id\": 42, \"name\": \"fork\" },\n  { \"id\": 43, \"name\": \"knife\" },\n  { \"id\": 44, \"name\": \"spoon\" },\n  { \"id\": 45, \"name\": \"bowl\" },\n  { \"id\": 46, \"name\": \"banana\" },\n  { \"id\": 47, \"name\": \"apple\" },\n  { \"id\": 48, \"name\": \"sandwich\" },\n  { \"id\": 49, \"name\": \"orange\" },\n  { \"id\": 50, \"name\": \"broccoli\" },\n  { \"id\": 51, \"name\": \"carrot\" },\n  { \"id\": 52, \"name\": \"hot dog\" },\n  { \"id\": 53, \"name\": \"pizza\" },\n  { \"id\": 54, \"name\": \"donut\" },\n  { \"id\": 55, \"name\": \"cake\" },\n  { \"id\": 56, \"name\": \"chair\" },\n  { \"id\": 57, \"name\": \"sofa\" },\n  { \"id\": 58, \"name\": \"pottedplant\" },\n  { \"id\": 59, \"name\": \"bed\" },\n  { \"id\": 60, \"name\": \"diningtable\" },\n  { \"id\": 61, \"name\": \"toilet\" },\n  { \"id\": 62, \"name\": \"tvmonitor\" },\n  { \"id\": 63, \"name\": \"laptop\" },\n  { \"id\": 64, \"name\": \"mouse\" },\n  { \"id\": 65, \"name\": \"remote\" },\n  { \"id\": 66, \"name\": \"keyboard\" },\n  { \"id\": 67, \"name\": \"cell phone\" },\n  { \"id\": 68, \"name\": \"microwave\" },\n  { \"id\": 69, \"name\": \"oven\" },\n  { \"id\": 70, \"name\": \"toaster\" },\n  { \"id\": 71, \"name\": \"sink\" },\n  { \"id\": 72, \"name\": \"refrigerator\" },\n  { \"id\": 73, \"name\": \"book\" },\n  { \"id\": 74, \"name\": \"clock\" },\n  { \"id\": 75, \"name\": \"vase\" },\n  { \"id\": 76, \"name\": \"scissors\" },\n  { \"id\": 77, \"name\": \"teddy bear\" },\n  { \"id\": 78, \"name\": \"hair drier\" },\n  { \"id\": 79, \"name\": \"toothbrush\" }\n]\n","type":"detector"}},"spec":{"description":"YOLO v3 via Intel OpenVINO","handler":"main:handler","runtime":"python:3.6","env":[{"name":"NUCLIO_PYTHON_EXE_PATH","value":"/opt/nuclio/common/openvino/python3"}],"resources":{},"image":"cvat/openvino.omz.public.yolo-v3-tf:latest","targetCPU":75,"triggers":{"myHttpTrigger":{"class":"","kind":"http","name":"myHttpTrigger","maxWorkers":2,"workerAvailabilityTimeoutMilliseconds":10000,"attributes":{"maxRequestBodySize":33554432}}},"volumes":[{"volume":{"name":"volume-1","hostPath":{"path":"/home/nmanovic/Workspace/cvat/serverless/common"}},"volumeMount":{"name":"volume-1","mountPath":"/opt/nuclio/common"}}],"build":{"image":"cvat/openvino.omz.public.yolo-v3-tf","baseImage":"openvino/ubuntu18_dev:2020.2","directives":{"preCopy":[{"kind":"USER","value":"root"},{"kind":"WORKDIR","value":"/opt/nuclio"},{"kind":"RUN","value":"ln -s /usr/bin/pip3 /usr/bin/pip"},{"kind":"RUN","value":"/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/downloader.py --name yolo-v3-tf -o /opt/nuclio/open_model_zoo"},{"kind":"RUN","value":"/opt/intel/openvino/deployment_tools/open_model_zoo/tools/downloader/converter.py --name yolo-v3-tf --precisions FP32 -d /opt/nuclio/open_model_zoo -o /opt/nuclio/open_model_zoo"}]},"codeEntryType":"image"},"platform":{"attributes":{"mountMode":"volume","restartPolicy":{"maximumRetryCount":3,"name":"always"}}},"readinessTimeoutSeconds":60,"securityContext":{},"eventTimeout":"30s"}}}}
21.07.12 15:55:31.496            nuctl.platform (I) Waiting for function to be ready {"timeout": 60}
21.07.12 15:55:32.894                     nuctl (I) Function deploy complete {"functionName": "openvino-omz-public-yolo-v3-tf", "httpPort": 49156}

    Again, go to models tab and check that you can see YOLO v3 in the list. If you cannot by a reason it means that there are some problems. Go to one of our public channels and ask for help.

    Let us reuse the task which you created for testing SiamMask serverless function above. Choose the magic wand tool, go to the Detectors tab, and select YOLO v3 model. Press Annotate button and after a couple of seconds you should see detection results. Do not forget to save annotations.

    YOLO v3 results

    Also it is possible to run a detector for the whole annotation task. Thus CVAT will run the serverless function on every frame of the task and submit results directly into database. For more details please read the guide.

    Objects segmentation using Mask-RCNN

    If you have a detector, which returns polygons, you can segment objects. One of such detectors is Mask-RCNN. There are several implementations of the detector available out of the box:

    • serverless/openvino/omz/public/mask_rcnn_inception_resnet_v2_atrous_coco is optimized using Intel OpenVINO framework and works well if it is run on an Intel CPU.
    • serverless/tensorflow/matterport/mask_rcnn/ is optimized for GPU.

    The deployment process for a serverless function optimized for GPU is similar. Just need to run serverless/deploy_gpu.sh script. It runs mostly the same commands but utilize function-gpu.yaml configuration file instead of function.yaml internally. See next sections if you want to understand the difference.

    Note: Please do not run several GPU functions at the same time. In many cases it will not work out of the box. For now you should manually schedule different functions on different GPUs and it requires source code modification. Nuclio autoscaler does not support the local platform (docker).

    serverless/deploy_gpu.sh serverless/tensorflow/matterport/mask_rcnn

    Deploying serverless/tensorflow/matterport/mask_rcnn function...
21.07.12 16:48:48.995                     nuctl (I) Deploying function {"name": ""}
21.07.12 16:48:48.995                     nuctl (I) Building {"versionInfo": "Label: 1.5.16, Git commit: ae43a6a560c2bec42d7ccfdf6e8e11a1e3cc3774, OS: linux, Arch: amd64, Go version: go1.14.3", "name": ""}
21.07.12 16:48:49.356                     nuctl (I) Cleaning up before deployment {"functionName": "tf-matterport-mask-rcnn"}
21.07.12 16:48:49.470                     nuctl (I) Function already exists, deleting function containers {"functionName": "tf-matterport-mask-rcnn"}
21.07.12 16:48:50.247                     nuctl (I) Staging files and preparing base images
21.07.12 16:48:50.248                     nuctl (I) Building processor image {"imageName": "cvat/tf.matterport.mask_rcnn:latest"}
21.07.12 16:48:50.249     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/handler-builder-python-onbuild:1.5.16-amd64"}
21.07.12 16:48:53.674     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/uhttpc:0.0.1-amd64"}
21.07.12 16:48:57.424            nuctl.platform (I) Building docker image {"image": "cvat/tf.matterport.mask_rcnn:latest"}
21.07.12 16:48:57.763            nuctl.platform (I) Pushing docker image into registry {"image": "cvat/tf.matterport.mask_rcnn:latest", "registry": ""}
21.07.12 16:48:57.764            nuctl.platform (I) Docker image was successfully built and pushed into docker registry {"image": "cvat/tf.matterport.mask_rcnn:latest"}
21.07.12 16:48:57.764                     nuctl (I) Build complete {"result": {"Image":"cvat/tf.matterport.mask_rcnn:latest","UpdatedFunctionConfig":{"metadata":{"name":"tf-matterport-mask-rcnn","namespace":"nuclio","labels":{"nuclio.io/project-name":"cvat"},"annotations":{"framework":"tensorflow","name":"Mask RCNN via Tensorflow","spec":"[\n  { \"id\": 0, \"name\": \"BG\" },\n  { \"id\": 1, \"name\": \"person\" },\n  { \"id\": 2, \"name\": \"bicycle\" },\n  { \"id\": 3, \"name\": \"car\" },\n  { \"id\": 4, \"name\": \"motorcycle\" },\n  { \"id\": 5, \"name\": \"airplane\" },\n  { \"id\": 6, \"name\": \"bus\" },\n  { \"id\": 7, \"name\": \"train\" },\n  { \"id\": 8, \"name\": \"truck\" },\n  { \"id\": 9, \"name\": \"boat\" },\n  { \"id\": 10, \"name\": \"traffic_light\" },\n  { \"id\": 11, \"name\": \"fire_hydrant\" },\n  { \"id\": 12, \"name\": \"stop_sign\" },\n  { \"id\": 13, \"name\": \"parking_meter\" },\n  { \"id\": 14, \"name\": \"bench\" },\n  { \"id\": 15, \"name\": \"bird\" },\n  { \"id\": 16, \"name\": \"cat\" },\n  { \"id\": 17, \"name\": \"dog\" },\n  { \"id\": 18, \"name\": \"horse\" },\n  { \"id\": 19, \"name\": \"sheep\" },\n  { \"id\": 20, \"name\": \"cow\" },\n  { \"id\": 21, \"name\": \"elephant\" },\n  { \"id\": 22, \"name\": \"bear\" },\n  { \"id\": 23, \"name\": \"zebra\" },\n  { \"id\": 24, \"name\": \"giraffe\" },\n  { \"id\": 25, \"name\": \"backpack\" },\n  { \"id\": 26, \"name\": \"umbrella\" },\n  { \"id\": 27, \"name\": \"handbag\" },\n  { \"id\": 28, \"name\": \"tie\" },\n  { \"id\": 29, \"name\": \"suitcase\" },\n  { \"id\": 30, \"name\": \"frisbee\" },\n  { \"id\": 31, \"name\": \"skis\" },\n  { \"id\": 32, \"name\": \"snowboard\" },\n  { \"id\": 33, \"name\": \"sports_ball\" },\n  { \"id\": 34, \"name\": \"kite\" },\n  { \"id\": 35, \"name\": \"baseball_bat\" },\n  { \"id\": 36, \"name\": \"baseball_glove\" },\n  { \"id\": 37, \"name\": \"skateboard\" },\n  { \"id\": 38, \"name\": \"surfboard\" },\n  { \"id\": 39, \"name\": \"tennis_racket\" },\n  { \"id\": 40, \"name\": \"bottle\" },\n  { \"id\": 41, \"name\": \"wine_glass\" },\n  { \"id\": 42, \"name\": \"cup\" },\n  { \"id\": 43, \"name\": \"fork\" },\n  { \"id\": 44, \"name\": \"knife\" },\n  { \"id\": 45, \"name\": \"spoon\" },\n  { \"id\": 46, \"name\": \"bowl\" },\n  { \"id\": 47, \"name\": \"banana\" },\n  { \"id\": 48, \"name\": \"apple\" },\n  { \"id\": 49, \"name\": \"sandwich\" },\n  { \"id\": 50, \"name\": \"orange\" },\n  { \"id\": 51, \"name\": \"broccoli\" },\n  { \"id\": 52, \"name\": \"carrot\" },\n  { \"id\": 53, \"name\": \"hot_dog\" },\n  { \"id\": 54, \"name\": \"pizza\" },\n  { \"id\": 55, \"name\": \"donut\" },\n  { \"id\": 56, \"name\": \"cake\" },\n  { \"id\": 57, \"name\": \"chair\" },\n  { \"id\": 58, \"name\": \"couch\" },\n  { \"id\": 59, \"name\": \"potted_plant\" },\n  { \"id\": 60, \"name\": \"bed\" },\n  { \"id\": 61, \"name\": \"dining_table\" },\n  { \"id\": 62, \"name\": \"toilet\" },\n  { \"id\": 63, \"name\": \"tv\" },\n  { \"id\": 64, \"name\": \"laptop\" },\n  { \"id\": 65, \"name\": \"mouse\" },\n  { \"id\": 66, \"name\": \"remote\" },\n  { \"id\": 67, \"name\": \"keyboard\" },\n  { \"id\": 68, \"name\": \"cell_phone\" },\n  { \"id\": 69, \"name\": \"microwave\" },\n  { \"id\": 70, \"name\": \"oven\" },\n  { \"id\": 71, \"name\": \"toaster\" },\n  { \"id\": 72, \"name\": \"sink\" },\n  { \"id\": 73, \"name\": \"refrigerator\" },\n  { \"id\": 74, \"name\": \"book\" },\n  { \"id\": 75, \"name\": \"clock\" },\n  { \"id\": 76, \"name\": \"vase\" },\n  { \"id\": 77, \"name\": \"scissors\" },\n  { \"id\": 78, \"name\": \"teddy_bear\" },\n  { \"id\": 79, \"name\": \"hair_drier\" },\n  { \"id\": 80, \"name\": \"toothbrush\" }\n]\n","type":"detector"}},"spec":{"description":"Mask RCNN optimized for GPU","handler":"main:handler","runtime":"python:3.6","env":[{"name":"MASK_RCNN_DIR","value":"/opt/nuclio/Mask_RCNN"}],"resources":{"limits":{"nvidia.com/gpu":"1"}},"image":"cvat/tf.matterport.mask_rcnn:latest","targetCPU":75,"triggers":{"myHttpTrigger":{"class":"","kind":"http","name":"myHttpTrigger","maxWorkers":1,"workerAvailabilityTimeoutMilliseconds":10000,"attributes":{"maxRequestBodySize":33554432}}},"volumes":[{"volume":{"name":"volume-1","hostPath":{"path":"/home/nmanovic/Workspace/cvat/serverless/common"}},"volumeMount":{"name":"volume-1","mountPath":"/opt/nuclio/common"}}],"build":{"functionConfigPath":"serverless/tensorflow/matterport/mask_rcnn/nuclio/function-gpu.yaml","image":"cvat/tf.matterport.mask_rcnn","baseImage":"tensorflow/tensorflow:1.15.5-gpu-py3","directives":{"postCopy":[{"kind":"WORKDIR","value":"/opt/nuclio"},{"kind":"RUN","value":"apt update \u0026\u0026 apt install --no-install-recommends -y git curl"},{"kind":"RUN","value":"git clone --depth 1 https://github.com/matterport/Mask_RCNN.git"},{"kind":"RUN","value":"curl -L https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5 -o Mask_RCNN/mask_rcnn_coco.h5"},{"kind":"RUN","value":"pip3 install numpy cython pyyaml keras==2.1.0 scikit-image Pillow"}]},"codeEntryType":"image"},"platform":{"attributes":{"mountMode":"volume","restartPolicy":{"maximumRetryCount":3,"name":"always"}}},"readinessTimeoutSeconds":60,"securityContext":{},"eventTimeout":"30s"}}}}
21.07.12 16:48:59.071            nuctl.platform (I) Waiting for function to be ready {"timeout": 60}
21.07.12 16:49:00.437                     nuctl (I) Function deploy complete {"functionName": "tf-matterport-mask-rcnn", "httpPort": 49155}

    Now you should be able to annotate objects using segmentation masks.

    Mask RCNN results

    Adding your own DL models

    Choose a DL model

    For the tutorial I will choose a popular AI library with a lot of models inside. In your case it can be your own model. If it is based on detectron2 it will be easy to integrate. Just follow the tutorial.

    Detectron2 is Facebook AI Research’s next generation library that provides state-of-the-art detection and segmentation algorithms. It is the successor of Detectron and maskrcnn-benchmark. It supports a number of computer vision research projects and production applications in Facebook.

    Clone the repository somewhere. I assume that all other experiments will be run from the cloned detectron2 directory.

    git clone https://github.com/facebookresearch/detectron2
cd detectron2

    Run local experiments

    Let’s run a detection model locally. First of all need to install requirements for the library.

    In my case I have Ubuntu 20.04 with python 3.8.5. I installed PyTorch 1.8.1 for Linux with pip, python, and CPU inside a virtual environment. Follow opencv-python installation guide to get the library for demo and visualization.

    python3 -m venv .detectron2
. .detectron2/bin/activate
pip install torch==1.8.1+cpu torchvision==0.9.1+cpu torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
pip install opencv-python

    Install the detectron2 library from your local clone (you should be inside detectron2 directory).

    python -m pip install -e .

    After the library from Facebook AI Research is installed, we can run a couple of experiments. See the official tutorial for more examples. I decided to experiment with RetinaNet. First step is to download model weights.

    curl -O https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_101_FPN_3x/190397697/model_final_971ab9.pkl

    To run experiments let’s download an image with cats from wikipedia.

    curl -O https://upload.wikimedia.org/wikipedia/commons/thumb/0/0b/Cat_poster_1.jpg/1920px-Cat_poster_1.jpg

    Finally let’s run the DL model inference on CPU. If all is fine, you will see a window with cats and bounding boxes around them with scores.

    python demo/demo.py --config-file configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml \
  --input 1920px-Cat_poster_1.jpg --opts MODEL.WEIGHTS model_final_971ab9.pkl MODEL.DEVICE cpu

    Cats detected by RetinaNet R101

    Next step is to minimize demo/demo.py script and keep code which is necessary to load, run, and interpret output of the model only. Let’s hard code parameters and remove argparse. Keep only code which is responsible for working with an image. There is no common advice how to minimize some code.

    Finally you should get something like the code below which has fixed config, read a predefined image, initialize predictor, and run inference. As the final step it prints all detected bounding boxes with scores and labels.

    from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.engine.defaults import DefaultPredictor
from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES

CONFIG_FILE = "configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml"
CONFIG_OPTS = ["MODEL.WEIGHTS", "model_final_971ab9.pkl", "MODEL.DEVICE", "cpu"]
CONFIDENCE_THRESHOLD = 0.5

def setup_cfg():
    cfg = get_cfg()
    cfg.merge_from_file(CONFIG_FILE)
    cfg.merge_from_list(CONFIG_OPTS)
    cfg.MODEL.RETINANET.SCORE_THRESH_TEST = CONFIDENCE_THRESHOLD
    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = CONFIDENCE_THRESHOLD
    cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = CONFIDENCE_THRESHOLD
    cfg.freeze()
    return cfg


if __name__ == "__main__":
    cfg = setup_cfg()
    input = "1920px-Cat_poster_1.jpg"
    img = read_image(input, format="BGR")
    predictor = DefaultPredictor(cfg)
    predictions = predictor(img)
    instances = predictions['instances']
    pred_boxes = instances.pred_boxes
    scores = instances.scores
    pred_classes = instances.pred_classes
    for box, score, label in zip(pred_boxes, scores, pred_classes):
        label = COCO_CATEGORIES[int(label)]["name"]
        print(box.tolist(), float(score), label)

    DL model as a serverless function

    When we know how to run the DL model locally, we can prepare a serverless function which can be used by CVAT to annotate data. Let’s see how function.yaml will look like…

    Let’s look at faster_rcnn_inception_v2_coco serverless function configuration as an example and try adapting it to our case. First of all let’s invent an unique name for the new function: pth-facebookresearch-detectron2-retinanet-r101. Section annotations describes our function for CVAT serverless subsystem:

    • annotations.name is a display name
    • annotations.type is a type of the serverless function. It can have several different values. Basically it affects input and output of the function. In our case it has detector type and it means that the integrated DL model can generate shapes with labels for an image.
    • annotations.framework is used for information only and can have arbitrary value. Usually it has values like OpenVINO, PyTorch, TensorFlow, etc.
    • annotations.spec describes the list of labels which the model supports. In the case the DL model was trained on MS COCO dataset and the list of labels correspond to the dataset.
    • spec.description is used to provide basic information for the model.

    All other parameters are described in Nuclio documentation.

    • spec.handler is the entry point to your function.
    • spec.runtime is the name of the language runtime.
    • spec.eventTimeout is the global event timeout

    Next step is to describe how to build our serverless function:

    • spec.build.image is the name of your docker image
    • spec.build.baseImage is the name of a base container image from which to build the function
    • spec.build.directives are commands to build your docker image

    In our case we start from Ubuntu 20.04 base image, install curl to download weights for our model, git to clone detectron2 project from GitHub, and python together with pip. Repeat installation steps which we used to setup the DL model locally with minor modifications.

    For Nuclio platform we have to specify a couple of more parameters:

    • spec.triggers.myHttpTrigger describes HTTP trigger to handle incoming HTTP requests.
    • spec.platform describes some important parameters to run your functions like restartPolicy and mountMode. Read Nuclio documentation for more details.
    metadata:
  name: pth-facebookresearch-detectron2-retinanet-r101
  namespace: cvat
  annotations:
    name: RetinaNet R101
    type: detector
    spec: |
      [
        { "id": 1, "name": "person" },
        { "id": 2, "name": "bicycle" },

        ...

        { "id":89, "name": "hair_drier" },
        { "id":90, "name": "toothbrush" }
      ]      

spec:
  description: RetinaNet R101 from Detectron2
  runtime: 'python:3.8'
  handler: main:handler
  eventTimeout: 30s

  build:
    image: cvat/pth.facebookresearch.detectron2.retinanet_r101
    baseImage: ubuntu:20.04

    directives:
      preCopy:
        - kind: ENV
          value: DEBIAN_FRONTEND=noninteractive
        - kind: RUN
          value: apt-get update && apt-get -y install curl git python3 python3-pip
        - kind: WORKDIR
          value: /opt/nuclio
        - kind: RUN
          value: pip3 install torch==1.8.1+cpu torchvision==0.9.1+cpu torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
        - kind: RUN
          value: pip3 install 'git+https://github.com/facebookresearch/detectron2@v0.4'
        - kind: RUN
          value: curl -O https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_101_FPN_3x/190397697/model_final_971ab9.pkl
        - kind: RUN
          value: ln -s /usr/bin/pip3 /usr/local/bin/pip

  triggers:
    myHttpTrigger:
      maxWorkers: 2
      kind: 'http'
      workerAvailabilityTimeoutMilliseconds: 10000
      attributes:
        maxRequestBodySize: 33554432 # 32MB

  platform:
    attributes:
      restartPolicy:
        name: always
        maximumRetryCount: 3
      mountMode: volume

    Full code can be found here: detectron2/retinanet/nuclio/function.yaml

    Next step is to adapt our source code which we implemented to run the DL model locally to requirements of Nuclio platform. First step is to load the model into memory using init_context(context) function. Read more about the function in Best Practices and Common Pitfalls.

    After that we need to accept incoming HTTP requests, run inference, reply with detection results. For the process our entry point is responsible which we specified in our function specification handler(context, event). Again in accordance to function specification the entry point should be located inside main.py.

    
def init_context(context):
    context.logger.info("Init context...  0%")

    cfg = get_config('COCO-Detection/retinanet_R_101_FPN_3x.yaml')
    cfg.merge_from_list(CONFIG_OPTS)
    cfg.MODEL.RETINANET.SCORE_THRESH_TEST = CONFIDENCE_THRESHOLD
    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = CONFIDENCE_THRESHOLD
    cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = CONFIDENCE_THRESHOLD
    cfg.freeze()
    predictor = DefaultPredictor(cfg)

    context.user_data.model_handler = predictor

    context.logger.info("Init context...100%")

def handler(context, event):
    context.logger.info("Run retinanet-R101 model")
    data = event.body
    buf = io.BytesIO(base64.b64decode(data["image"]))
    threshold = float(data.get("threshold", 0.5))
    image = convert_PIL_to_numpy(Image.open(buf), format="BGR")

    predictions = context.user_data.model_handler(image)

    instances = predictions['instances']
    pred_boxes = instances.pred_boxes
    scores = instances.scores
    pred_classes = instances.pred_classes
    results = []
    for box, score, label in zip(pred_boxes, scores, pred_classes):
        label = COCO_CATEGORIES[int(label)]["name"]
        if score >= threshold:
            results.append({
                "confidence": str(float(score)),
                "label": label,
                "points": box.tolist(),
                "type": "rectangle",
            })

    return context.Response(body=json.dumps(results), headers={},
        content_type='application/json', status_code=200)

    Full code can be found here: detectron2/retinanet/nuclio/main.py

    Deploy RetinaNet serverless function

    To use the new serverless function you have to deploy it using nuctl command. The actual deployment process is described in automatic annotation guide.

    ./serverless/deploy_cpu.sh ./serverless/pytorch/facebookresearch/detectron2/retinanet/

    21.07.21 15:20:31.011                     nuctl (I) Deploying function {"name": ""}
21.07.21 15:20:31.011                     nuctl (I) Building {"versionInfo": "Label: 1.5.16, Git commit: ae43a6a560c2bec42d7ccfdf6e8e11a1e3cc3774, OS: linux, Arch: amd64, Go version: go1.14.3", "name": ""}
21.07.21 15:20:31.407                     nuctl (I) Cleaning up before deployment {"functionName": "pth-facebookresearch-detectron2-retinanet-r101"}
21.07.21 15:20:31.497                     nuctl (I) Function already exists, deleting function containers {"functionName": "pth-facebookresearch-detectron2-retinanet-r101"}
21.07.21 15:20:31.914                     nuctl (I) Staging files and preparing base images
21.07.21 15:20:31.915                     nuctl (I) Building processor image {"imageName": "cvat/pth.facebookresearch.detectron2.retinanet_r101:latest"}
21.07.21 15:20:31.916     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/handler-builder-python-onbuild:1.5.16-amd64"}
21.07.21 15:20:34.495     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/uhttpc:0.0.1-amd64"}
21.07.21 15:20:37.524            nuctl.platform (I) Building docker image {"image": "cvat/pth.facebookresearch.detectron2.retinanet_r101:latest"}
21.07.21 15:20:37.852            nuctl.platform (I) Pushing docker image into registry {"image": "cvat/pth.facebookresearch.detectron2.retinanet_r101:latest", "registry": ""}
21.07.21 15:20:37.853            nuctl.platform (I) Docker image was successfully built and pushed into docker registry {"image": "cvat/pth.facebookresearch.detectron2.retinanet_r101:latest"}
21.07.21 15:20:37.853                     nuctl (I) Build complete {"result": {"Image":"cvat/pth.facebookresearch.detectron2.retinanet_r101:latest","UpdatedFunctionConfig":{"metadata":{"name":"pth-facebookresearch-detectron2-retinanet-r101","namespace":"nuclio","labels":{"nuclio.io/project-name":"cvat"},"annotations":{"framework":"pytorch","name":"RetinaNet R101","spec":"[\n  { \"id\": 1, \"name\": \"person\" },\n  { \"id\": 2, \"name\": \"bicycle\" },\n  { \"id\": 3, \"name\": \"car\" },\n  { \"id\": 4, \"name\": \"motorcycle\" },\n  { \"id\": 5, \"name\": \"airplane\" },\n  { \"id\": 6, \"name\": \"bus\" },\n  { \"id\": 7, \"name\": \"train\" },\n  { \"id\": 8, \"name\": \"truck\" },\n  { \"id\": 9, \"name\": \"boat\" },\n  { \"id\":10, \"name\": \"traffic_light\" },\n  { \"id\":11, \"name\": \"fire_hydrant\" },\n  { \"id\":13, \"name\": \"stop_sign\" },\n  { \"id\":14, \"name\": \"parking_meter\" },\n  { \"id\":15, \"name\": \"bench\" },\n  { \"id\":16, \"name\": \"bird\" },\n  { \"id\":17, \"name\": \"cat\" },\n  { \"id\":18, \"name\": \"dog\" },\n  { \"id\":19, \"name\": \"horse\" },\n  { \"id\":20, \"name\": \"sheep\" },\n  { \"id\":21, \"name\": \"cow\" },\n  { \"id\":22, \"name\": \"elephant\" },\n  { \"id\":23, \"name\": \"bear\" },\n  { \"id\":24, \"name\": \"zebra\" },\n  { \"id\":25, \"name\": \"giraffe\" },\n  { \"id\":27, \"name\": \"backpack\" },\n  { \"id\":28, \"name\": \"umbrella\" },\n  { \"id\":31, \"name\": \"handbag\" },\n  { \"id\":32, \"name\": \"tie\" },\n  { \"id\":33, \"name\": \"suitcase\" },\n  { \"id\":34, \"name\": \"frisbee\" },\n  { \"id\":35, \"name\": \"skis\" },\n  { \"id\":36, \"name\": \"snowboard\" },\n  { \"id\":37, \"name\": \"sports_ball\" },\n  { \"id\":38, \"name\": \"kite\" },\n  { \"id\":39, \"name\": \"baseball_bat\" },\n  { \"id\":40, \"name\": \"baseball_glove\" },\n  { \"id\":41, \"name\": \"skateboard\" },\n  { \"id\":42, \"name\": \"surfboard\" },\n  { \"id\":43, \"name\": \"tennis_racket\" },\n  { \"id\":44, \"name\": \"bottle\" },\n  { \"id\":46, \"name\": \"wine_glass\" },\n  { \"id\":47, \"name\": \"cup\" },\n  { \"id\":48, \"name\": \"fork\" },\n  { \"id\":49, \"name\": \"knife\" },\n  { \"id\":50, \"name\": \"spoon\" },\n  { \"id\":51, \"name\": \"bowl\" },\n  { \"id\":52, \"name\": \"banana\" },\n  { \"id\":53, \"name\": \"apple\" },\n  { \"id\":54, \"name\": \"sandwich\" },\n  { \"id\":55, \"name\": \"orange\" },\n  { \"id\":56, \"name\": \"broccoli\" },\n  { \"id\":57, \"name\": \"carrot\" },\n  { \"id\":58, \"name\": \"hot_dog\" },\n  { \"id\":59, \"name\": \"pizza\" },\n  { \"id\":60, \"name\": \"donut\" },\n  { \"id\":61, \"name\": \"cake\" },\n  { \"id\":62, \"name\": \"chair\" },\n  { \"id\":63, \"name\": \"couch\" },\n  { \"id\":64, \"name\": \"potted_plant\" },\n  { \"id\":65, \"name\": \"bed\" },\n  { \"id\":67, \"name\": \"dining_table\" },\n  { \"id\":70, \"name\": \"toilet\" },\n  { \"id\":72, \"name\": \"tv\" },\n  { \"id\":73, \"name\": \"laptop\" },\n  { \"id\":74, \"name\": \"mouse\" },\n  { \"id\":75, \"name\": \"remote\" },\n  { \"id\":76, \"name\": \"keyboard\" },\n  { \"id\":77, \"name\": \"cell_phone\" },\n  { \"id\":78, \"name\": \"microwave\" },\n  { \"id\":79, \"name\": \"oven\" },\n  { \"id\":80, \"name\": \"toaster\" },\n  { \"id\":81, \"name\": \"sink\" },\n  { \"id\":83, \"name\": \"refrigerator\" },\n  { \"id\":84, \"name\": \"book\" },\n  { \"id\":85, \"name\": \"clock\" },\n  { \"id\":86, \"name\": \"vase\" },\n  { \"id\":87, \"name\": \"scissors\" },\n  { \"id\":88, \"name\": \"teddy_bear\" },\n  { \"id\":89, \"name\": \"hair_drier\" },\n  { \"id\":90, \"name\": \"toothbrush\" }\n]\n","type":"detector"}},"spec":{"description":"RetinaNet R101 from Detectron2","handler":"main:handler","runtime":"python:3.8","resources":{},"image":"cvat/pth.facebookresearch.detectron2.retinanet_r101:latest","targetCPU":75,"triggers":{"myHttpTrigger":{"class":"","kind":"http","name":"myHttpTrigger","maxWorkers":2,"workerAvailabilityTimeoutMilliseconds":10000,"attributes":{"maxRequestBodySize":33554432}}},"volumes":[{"volume":{"name":"volume-1","hostPath":{"path":"/home/nmanovic/Workspace/cvat/serverless/common"}},"volumeMount":{"name":"volume-1","mountPath":"/opt/nuclio/common"}}],"build":{"image":"cvat/pth.facebookresearch.detectron2.retinanet_r101","baseImage":"ubuntu:20.04","directives":{"preCopy":[{"kind":"ENV","value":"DEBIAN_FRONTEND=noninteractive"},{"kind":"RUN","value":"apt-get update \u0026\u0026 apt-get -y install curl git python3 python3-pip"},{"kind":"WORKDIR","value":"/opt/nuclio"},{"kind":"RUN","value":"pip3 install torch==1.8.1+cpu torchvision==0.9.1+cpu torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html"},{"kind":"RUN","value":"pip3 install 'git+https://github.com/facebookresearch/detectron2@v0.4'"},{"kind":"RUN","value":"curl -O https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_101_FPN_3x/190397697/model_final_971ab9.pkl"},{"kind":"RUN","value":"ln -s /usr/bin/pip3 /usr/local/bin/pip"}]},"codeEntryType":"image"},"platform":{"attributes":{"mountMode":"volume","restartPolicy":{"maximumRetryCount":3,"name":"always"}}},"readinessTimeoutSeconds":60,"securityContext":{},"eventTimeout":"30s"}}}}
21.07.21 15:20:39.042            nuctl.platform (I) Waiting for function to be ready {"timeout": 60}
21.07.21 15:20:40.480                     nuctl (I) Function deploy complete {"functionName": "pth-facebookresearch-detectron2-retinanet-r101", "httpPort": 49153}

    Advanced capabilities

    Optimize using GPU

    To optimize a function for a specific device (e.g. GPU), basically you just need to modify instructions above to run the function on the target device. In most cases it will be necessary to modify installation instructions only.

    For RetinaNet R101 which was added above modifications will look like:

    --- function.yaml	2021-06-25 21:06:51.603281723 +0300
+++ function-gpu.yaml	2021-07-07 22:38:53.454202637 +0300
@@ -90,7 +90,7 @@
       ]

 spec:
-  description: RetinaNet R101 from Detectron2
+  description: RetinaNet R101 from Detectron2 optimized for GPU
   runtime: 'python:3.8'
   handler: main:handler
   eventTimeout: 30s
@@ -108,7 +108,7 @@
         - kind: WORKDIR
           value: /opt/nuclio
         - kind: RUN
-          value: pip3 install torch==1.8.1+cpu torchvision==0.9.1+cpu torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
+          value: pip3 install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
         - kind: RUN
           value: git clone https://github.com/facebookresearch/detectron2
         - kind: RUN
@@ -120,12 +120,16 @@

   triggers:
     myHttpTrigger:
-      maxWorkers: 2
+      maxWorkers: 1
       kind: 'http'
       workerAvailabilityTimeoutMilliseconds: 10000
       attributes:
         maxRequestBodySize: 33554432 # 32MB

+  resources:
+    limits:
+      nvidia.com/gpu: 1
+
   platform:
     attributes:
       restartPolicy:

    Note: GPU has very limited amount of memory and it doesn’t allow to run multiple serverless functions in parallel for now using free open-source Nuclio version on the local platform because scaling to zero feature is absent. Theoretically it is possible to run different functions on different GPUs, but it requires to change source code on corresponding serverless functions to choose a free GPU.

    Debugging a serverless function

    Let’s say you have a problem with your serverless function and want to debug it. Of course you can use context.logger.info or similar methods to print the intermediate state of your function. Another way is to debug using Visual Studio Code. Please see instructions below to setup your environment step by step.

    Let’s modify our function.yaml to include debugpy package and specify that maxWorkers count is 1. Otherwise both workers will try to use the same port and it will lead to an exception in python code.

            - kind: RUN
          value: pip3 install debugpy

  triggers:
    myHttpTrigger:
      maxWorkers: 1

    Change main.py to listen to a port (e.g. 5678). Insert code below in the beginning of your file with entry point.

    import debugpy
debugpy.listen(5678)

    After these changes deploy the serverless function once again. For serverless/pytorch/facebookresearch/detectron2/retinanet/nuclio/ you should run the command below:

    serverless/deploy_cpu.sh serverless/pytorch/facebookresearch/detectron2/retinanet

    To debug python code inside a container you have to publish the port (in this tutorial it is 5678). Nuclio deploy command doesn’t support that and we have to workaround it using SSH port forwarding.

    • Install SSH server on your host machine using sudo apt install openssh-server
    • In /etc/ssh/sshd_config host file set GatewayPorts yes
    • Restart ssh service to apply changes using sudo systemctl restart ssh.service

    Next step is to install ssh client inside the container and run port forwarding. In the snippet below instead of user and ipaddress provide username and IP address of your host (usually IP address starts from 192.168.). You will need to confirm that you want to connect to your host computer and enter your password. Keep the terminal open after that.

    docker exec -it nuclio-nuclio-pth-facebookresearch-detectron2-retinanet-r101 /bin/bash
apt update && apt install -y ssh
ssh -R 5678:localhost:5678 user@ipaddress

    See how the latest command looks like in my case:

    root@2d6cceec8f70:/opt/nuclio# ssh -R 5678:localhost:5678 nmanovic@192.168.50.188
The authenticity of host '192.168.50.188 (192.168.50.188)' can't be established.
ECDSA key fingerprint is SHA256:0sD6IWi+FKAhtUXr2TroHqyjcnYRIGLLx/wkGaZeRuo.
Are you sure you want to continue connecting (yes/no/[fingerprint])? yes
Warning: Permanently added '192.168.50.188' (ECDSA) to the list of known hosts.
nmanovic@192.168.50.188's password:
Welcome to Ubuntu 20.04.2 LTS (GNU/Linux 5.8.0-53-generic x86_64)

 * Documentation:  https://help.ubuntu.com
 * Management:     https://landscape.canonical.com
 * Support:        https://ubuntu.com/advantage

223 updates can be applied immediately.
132 of these updates are standard security updates.
To see these additional updates run: apt list --upgradable

Your Hardware Enablement Stack (HWE) is supported until April 2025.
Last login: Fri Jun 25 16:39:04 2021 from 172.17.0.5
[setupvars.sh] OpenVINO environment initialized
nmanovic@nmanovic-dl-node:~$

    Finally, add the configuration below into your launch.json. Open Visual Studio Code and run Serverless Debug configuration, set a breakpoint in main.py and try to call the serverless function from CVAT UI. The breakpoint should be triggered in Visual Studio Code and it should be possible to inspect variables and debug code.

    {
  "name": "Serverless Debug",
  "type": "python",
  "request": "attach",
  "connect": {
    "host": "localhost",
    "port": 5678
  },
  "pathMappings": [
    {
      "localRoot": "${workspaceFolder}/serverless/pytorch/facebookresearch/detectron2/retinanet/nuclio",
      "remoteRoot": "/opt/nuclio"
    }
  ]
}

    VS Code debug RetinaNet

    Note: In case of changes in the source code, need to re-deploy the function and initiate port forwarding again.

    Troubleshooting

    First of all need to check that you are using the recommended version of Nuclio framework. In my case it is 1.5.16 but you need to check the installation manual.

    nuctl version

    Client version:
"Label: 1.5.16, Git commit: ae43a6a560c2bec42d7ccfdf6e8e11a1e3cc3774, OS: linux, Arch: amd64, Go version: go1.14.3"

    Check that Nuclio dashboard is running and its version corresponds to nuctl.

    docker ps --filter NAME=^nuclio$

    CONTAINER ID   IMAGE                                   COMMAND                  CREATED       STATUS                    PORTS                                               NAMES
7ab0c076c927   quay.io/nuclio/dashboard:1.5.16-amd64   "/docker-entrypoint.…"   6 weeks ago   Up 46 minutes (healthy)   80/tcp, 0.0.0.0:8070->8070/tcp, :::8070->8070/tcp   nuclio

    Be sure that the model, which doesn’t work, is healthy. In my case Inside Outside Guidance is not running.

    docker ps --filter NAME=iog

    CONTAINER ID   IMAGE     COMMAND   CREATED   STATUS    PORTS     NAMES

    Let’s run it. Go to the root of CVAT repository and run the deploying command.

    serverless/deploy_cpu.sh serverless/pytorch/shiyinzhang/iog

    Deploying serverless/pytorch/shiyinzhang/iog function...
21.07.06 12:49:08.763                     nuctl (I) Deploying function {"name": ""}
21.07.06 12:49:08.763                     nuctl (I) Building {"versionInfo": "Label: 1.5.16, Git commit: ae43a6a560c2bec42d7ccfdf6e8e11a1e3cc3774, OS: linux, Arch: amd64, Go version: go1.14.3", "name": ""}
21.07.06 12:49:09.085                     nuctl (I) Cleaning up before deployment {"functionName": "pth-shiyinzhang-iog"}
21.07.06 12:49:09.162                     nuctl (I) Function already exists, deleting function containers {"functionName": "pth-shiyinzhang-iog"}
21.07.06 12:49:09.230                     nuctl (I) Staging files and preparing base images
21.07.06 12:49:09.232                     nuctl (I) Building processor image {"imageName": "cvat/pth.shiyinzhang.iog:latest"}
21.07.06 12:49:09.232     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/handler-builder-python-onbuild:1.5.16-amd64"}
21.07.06 12:49:12.525     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/uhttpc:0.0.1-amd64"}
21.07.06 12:49:16.222            nuctl.platform (I) Building docker image {"image": "cvat/pth.shiyinzhang.iog:latest"}
21.07.06 12:49:16.555            nuctl.platform (I) Pushing docker image into registry {"image": "cvat/pth.shiyinzhang.iog:latest", "registry": ""}
21.07.06 12:49:16.555            nuctl.platform (I) Docker image was successfully built and pushed into docker registry {"image": "cvat/pth.shiyinzhang.iog:latest"}
21.07.06 12:49:16.555                     nuctl (I) Build complete {"result": {"Image":"cvat/pth.shiyinzhang.iog:latest","UpdatedFunctionConfig":{"metadata":{"name":"pth-shiyinzhang-iog","namespace":"nuclio","labels":{"nuclio.io/project-name":"cvat"},"annotations":{"framework":"pytorch","min_pos_points":"1","name":"IOG","spec":"","startswith_box":"true","type":"interactor"}},"spec":{"description":"Interactive Object Segmentation with Inside-Outside Guidance","handler":"main:handler","runtime":"python:3.6","env":[{"name":"PYTHONPATH","value":"/opt/nuclio/iog"}],"resources":{},"image":"cvat/pth.shiyinzhang.iog:latest","targetCPU":75,"triggers":{"myHttpTrigger":{"class":"","kind":"http","name":"myHttpTrigger","maxWorkers":2,"workerAvailabilityTimeoutMilliseconds":10000,"attributes":{"maxRequestBodySize":33554432}}},"volumes":[{"volume":{"name":"volume-1","hostPath":{"path":"/home/nmanovic/Workspace/cvat/serverless/common"}},"volumeMount":{"name":"volume-1","mountPath":"/opt/nuclio/common"}}],"build":{"image":"cvat/pth.shiyinzhang.iog","baseImage":"continuumio/miniconda3","directives":{"preCopy":[{"kind":"WORKDIR","value":"/opt/nuclio"},{"kind":"RUN","value":"conda create -y -n iog python=3.6"},{"kind":"SHELL","value":"[\"conda\", \"run\", \"-n\", \"iog\", \"/bin/bash\", \"-c\"]"},{"kind":"RUN","value":"conda install -y -c anaconda curl"},{"kind":"RUN","value":"conda install -y pytorch=0.4 torchvision=0.2 -c pytorch"},{"kind":"RUN","value":"conda install -y -c conda-forge pycocotools opencv scipy"},{"kind":"RUN","value":"git clone https://github.com/shiyinzhang/Inside-Outside-Guidance.git iog"},{"kind":"WORKDIR","value":"/opt/nuclio/iog"},{"kind":"ENV","value":"fileid=1Lm1hhMhhjjnNwO4Pf7SC6tXLayH2iH0l"},{"kind":"ENV","value":"filename=IOG_PASCAL_SBD.pth"},{"kind":"RUN","value":"curl -c ./cookie -s -L \"https://drive.google.com/uc?export=download\u0026id=${fileid}\""},{"kind":"RUN","value":"echo \"/download/ {print \\$NF}\" \u003e confirm_code.awk"},{"kind":"RUN","value":"curl -Lb ./cookie \"https://drive.google.com/uc?export=download\u0026confirm=`awk -f confirm_code.awk ./cookie`\u0026id=${fileid}\" -o ${filename}"},{"kind":"WORKDIR","value":"/opt/nuclio"},{"kind":"ENTRYPOINT","value":"[\"conda\", \"run\", \"-n\", \"iog\"]"}]},"codeEntryType":"image"},"platform":{"attributes":{"mountMode":"volume","restartPolicy":{"maximumRetryCount":3,"name":"always"}}},"readinessTimeoutSeconds":60,"securityContext":{},"eventTimeout":"30s"}}}}
21.07.06 12:49:17.422     nuctl.platform.docker (W) Failed to run container {"err": "stdout:\n1373cb432a178a3606685b5975e40a0755bc7958786c182304f5d1bbc0873ceb\ndocker: Error response from daemon: driver failed programming external connectivity on endpoint nuclio-nuclio-pth-shiyinzhang-iog (df68e7b4a60e553ee3079f1f1622b050cc958bd50f2cd359a20164d8a417d0ea): Bind for 0.0.0.0:49154 failed: port is already allocated.\n\nstderr:\n", "errVerbose": "\nError - exit status 125\n    /nuclio/pkg/cmdrunner/shellrunner.go:96\n\nCall stack:\nstdout:\n1373cb432a178a3606685b5975e40a0755bc7958786c182304f5d1bbc0873ceb\ndocker: Error response from daemon: driver failed programming external connectivity on endpoint nuclio-nuclio-pth-shiyinzhang-iog (df68e7b4a60e553ee3079f1f1622b050cc958bd50f2cd359a20164d8a417d0ea): Bind for 0.0.0.0:49154 failed: port is already allocated.\n\nstderr:\n\n    /nuclio/pkg/cmdrunner/shellrunner.go:96\nstdout:\n1373cb432a178a3606685b5975e40a0755bc7958786c182304f5d1bbc0873ceb\ndocker: Error response from daemon: driver failed programming external connectivity on endpoint nuclio-nuclio-pth-shiyinzhang-iog (df68e7b4a60e553ee3079f1f1622b050cc958bd50f2cd359a20164d8a417d0ea): Bind for 0.0.0.0:49154 failed: port is already allocated.\n\nstderr:\n", "errCauses": [{"error": "exit status 125"}], "stdout": "1373cb432a178a3606685b5975e40a0755bc7958786c182304f5d1bbc0873ceb\ndocker: Error response from daemon: driver failed programming external connectivity on endpoint nuclio-nuclio-pth-shiyinzhang-iog (df68e7b4a60e553ee3079f1f1622b050cc958bd50f2cd359a20164d8a417d0ea): Bind for 0.0.0.0:49154 failed: port is already allocated.\n", "stderr": ""}
21.07.06 12:49:17.422                     nuctl (W) Failed to create a function; setting the function status {"err": "Failed to run a Docker container", "errVerbose": "\nError - exit status 125\n    /nuclio/pkg/cmdrunner/shellrunner.go:96\n\nCall stack:\nstdout:\n1373cb432a178a3606685b5975e40a0755bc7958786c182304f5d1bbc0873ceb\ndocker: Error response from daemon: driver failed programming external connectivity on endpoint nuclio-nuclio-pth-shiyinzhang-iog (df68e7b4a60e553ee3079f1f1622b050cc958bd50f2cd359a20164d8a417d0ea): Bind for 0.0.0.0:49154 failed: port is already allocated.\n\nstderr:\n\n    /nuclio/pkg/cmdrunner/shellrunner.go:96\nFailed to run a Docker container\n    /nuclio/pkg/platform/local/platform.go:653\nFailed to run a Docker container", "errCauses": [{"error": "stdout:\n1373cb432a178a3606685b5975e40a0755bc7958786c182304f5d1bbc0873ceb\ndocker: Error response from daemon: driver failed programming external connectivity on endpoint nuclio-nuclio-pth-shiyinzhang-iog (df68e7b4a60e553ee3079f1f1622b050cc958bd50f2cd359a20164d8a417d0ea): Bind for 0.0.0.0:49154 failed: port is already allocated.\n\nstderr:\n", "errorVerbose": "\nError - exit status 125\n    /nuclio/pkg/cmdrunner/shellrunner.go:96\n\nCall stack:\nstdout:\n1373cb432a178a3606685b5975e40a0755bc7958786c182304f5d1bbc0873ceb\ndocker: Error response from daemon: driver failed programming external connectivity on endpoint nuclio-nuclio-pth-shiyinzhang-iog (df68e7b4a60e553ee3079f1f1622b050cc958bd50f2cd359a20164d8a417d0ea): Bind for 0.0.0.0:49154 failed: port is already allocated.\n\nstderr:\n\n    /nuclio/pkg/cmdrunner/shellrunner.go:96\nstdout:\n1373cb432a178a3606685b5975e40a0755bc7958786c182304f5d1bbc0873ceb\ndocker: Error response from daemon: driver failed programming external connectivity on endpoint nuclio-nuclio-pth-shiyinzhang-iog (df68e7b4a60e553ee3079f1f1622b050cc958bd50f2cd359a20164d8a417d0ea): Bind for 0.0.0.0:49154 failed: port is already allocated.\n\nstderr:\n", "errorCauses": [{"error": "exit status 125"}]}]}

Error - exit status 125
    /nuclio/pkg/cmdrunner/shellrunner.go:96

Call stack:
stdout:
1373cb432a178a3606685b5975e40a0755bc7958786c182304f5d1bbc0873ceb
docker: Error response from daemon: driver failed programming external connectivity on endpoint nuclio-nuclio-pth-shiyinzhang-iog (df68e7b4a60e553ee3079f1f1622b050cc958bd50f2cd359a20164d8a417d0ea): Bind for 0.0.0.0:49154 failed: port is already allocated.

stderr:

    /nuclio/pkg/cmdrunner/shellrunner.go:96
Failed to run a Docker container
    /nuclio/pkg/platform/local/platform.go:653
Failed to deploy function
    ...//nuclio/pkg/platform/abstract/platform.go:182
  NAMESPACE |                      NAME                      | PROJECT | STATE | NODE PORT | REPLICAS
  nuclio    | openvino-dextr                                 | cvat    | ready |     49154 | 1/1
  nuclio    | pth-foolwood-siammask                          | cvat    | ready |     49155 | 1/1
  nuclio    | pth-facebookresearch-detectron2-retinanet-r101 | cvat    | ready |     49155 | 1/1
  nuclio    | pth-shiyinzhang-iog                            | cvat    | error |         0 | 1/1

    In this case the container was built some time ago and the port 49154 was assigned by Nuclio. Now the port is used by openvino-dextr as we can see in logs. To prove our hypothesis just need to run a couple of docker commands:

    docker container ls -a | grep iog

    eb0c1ee46630   cvat/pth.shiyinzhang.iog:latest                              "conda run -n iog pr…"   9 minutes ago       Created                                                                          nuclio-nuclio-pth-shiyinzhang-iog

    docker inspect eb0c1ee46630 | grep 49154

                "Error": "driver failed programming external connectivity on endpoint nuclio-nuclio-pth-shiyinzhang-iog (02384290f91b2216162b1603322dadee426afe7f439d3d090f598af5d4863b2d): Bind for 0.0.0.0:49154 failed: port is already allocated",
                        "HostPort": "49154"

    To solve the problem let’s just remove the previous container for the function. In this case it is eb0c1ee46630. After that the deploying command works as expected.

    docker container rm eb0c1ee46630

    eb0c1ee46630

    serverless/deploy_cpu.sh serverless/pytorch/shiyinzhang/iog

    Deploying serverless/pytorch/shiyinzhang/iog function...
21.07.06 13:09:52.934                     nuctl (I) Deploying function {"name": ""}
21.07.06 13:09:52.934                     nuctl (I) Building {"versionInfo": "Label: 1.5.16, Git commit: ae43a6a560c2bec42d7ccfdf6e8e11a1e3cc3774, OS: linux, Arch: amd64, Go version: go1.14.3", "name": ""}
21.07.06 13:09:53.282                     nuctl (I) Cleaning up before deployment {"functionName": "pth-shiyinzhang-iog"}
21.07.06 13:09:53.341                     nuctl (I) Staging files and preparing base images
21.07.06 13:09:53.342                     nuctl (I) Building processor image {"imageName": "cvat/pth.shiyinzhang.iog:latest"}
21.07.06 13:09:53.342     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/handler-builder-python-onbuild:1.5.16-amd64"}
21.07.06 13:09:56.633     nuctl.platform.docker (I) Pulling image {"imageName": "quay.io/nuclio/uhttpc:0.0.1-amd64"}
21.07.06 13:10:00.163            nuctl.platform (I) Building docker image {"image": "cvat/pth.shiyinzhang.iog:latest"}
21.07.06 13:10:00.452            nuctl.platform (I) Pushing docker image into registry {"image": "cvat/pth.shiyinzhang.iog:latest", "registry": ""}
21.07.06 13:10:00.452            nuctl.platform (I) Docker image was successfully built and pushed into docker registry {"image": "cvat/pth.shiyinzhang.iog:latest"}
21.07.06 13:10:00.452                     nuctl (I) Build complete {"result": {"Image":"cvat/pth.shiyinzhang.iog:latest","UpdatedFunctionConfig":{"metadata":{"name":"pth-shiyinzhang-iog","namespace":"nuclio","labels":{"nuclio.io/project-name":"cvat"},"annotations":{"framework":"pytorch","min_pos_points":"1","name":"IOG","spec":"","startswith_box":"true","type":"interactor"}},"spec":{"description":"Interactive Object Segmentation with Inside-Outside Guidance","handler":"main:handler","runtime":"python:3.6","env":[{"name":"PYTHONPATH","value":"/opt/nuclio/iog"}],"resources":{},"image":"cvat/pth.shiyinzhang.iog:latest","targetCPU":75,"triggers":{"myHttpTrigger":{"class":"","kind":"http","name":"myHttpTrigger","maxWorkers":2,"workerAvailabilityTimeoutMilliseconds":10000,"attributes":{"maxRequestBodySize":33554432}}},"volumes":[{"volume":{"name":"volume-1","hostPath":{"path":"/home/nmanovic/Workspace/cvat/serverless/common"}},"volumeMount":{"name":"volume-1","mountPath":"/opt/nuclio/common"}}],"build":{"image":"cvat/pth.shiyinzhang.iog","baseImage":"continuumio/miniconda3","directives":{"preCopy":[{"kind":"WORKDIR","value":"/opt/nuclio"},{"kind":"RUN","value":"conda create -y -n iog python=3.6"},{"kind":"SHELL","value":"[\"conda\", \"run\", \"-n\", \"iog\", \"/bin/bash\", \"-c\"]"},{"kind":"RUN","value":"conda install -y -c anaconda curl"},{"kind":"RUN","value":"conda install -y pytorch=0.4 torchvision=0.2 -c pytorch"},{"kind":"RUN","value":"conda install -y -c conda-forge pycocotools opencv scipy"},{"kind":"RUN","value":"git clone https://github.com/shiyinzhang/Inside-Outside-Guidance.git iog"},{"kind":"WORKDIR","value":"/opt/nuclio/iog"},{"kind":"ENV","value":"fileid=1Lm1hhMhhjjnNwO4Pf7SC6tXLayH2iH0l"},{"kind":"ENV","value":"filename=IOG_PASCAL_SBD.pth"},{"kind":"RUN","value":"curl -c ./cookie -s -L \"https://drive.google.com/uc?export=download\u0026id=${fileid}\""},{"kind":"RUN","value":"echo \"/download/ {print \\$NF}\" \u003e confirm_code.awk"},{"kind":"RUN","value":"curl -Lb ./cookie \"https://drive.google.com/uc?export=download\u0026confirm=`awk -f confirm_code.awk ./cookie`\u0026id=${fileid}\" -o ${filename}"},{"kind":"WORKDIR","value":"/opt/nuclio"},{"kind":"ENTRYPOINT","value":"[\"conda\", \"run\", \"-n\", \"iog\"]"}]},"codeEntryType":"image"},"platform":{"attributes":{"mountMode":"volume","restartPolicy":{"maximumRetryCount":3,"name":"always"}}},"readinessTimeoutSeconds":60,"securityContext":{},"eventTimeout":"30s"}}}}
21.07.06 13:10:01.604            nuctl.platform (I) Waiting for function to be ready {"timeout": 60}
21.07.06 13:10:02.976                     nuctl (I) Function deploy complete {"functionName": "pth-shiyinzhang-iog", "httpPort": 49159}
  NAMESPACE |                      NAME                      | PROJECT | STATE | NODE PORT | REPLICAS
  nuclio    | openvino-dextr                                 | cvat    | ready |     49154 | 1/1
  nuclio    | pth-foolwood-siammask                          | cvat    | ready |     49155 | 1/1
  nuclio    | pth-saic-vul-fbrs                              | cvat    | ready |     49156 | 1/1
  nuclio    | pth-facebookresearch-detectron2-retinanet-r101 | cvat    | ready |     49155 | 1/1
  nuclio    | pth-shiyinzhang-iog                            | cvat    | ready |     49159 | 1/1

    When you investigate an issue with a serverless function, it is extremely useful to look at logs. Just run a couple of commands like docker logs <container>.

    docker logs cvat

    2021-07-06 13:44:54,699 DEBG 'runserver' stderr output:
[Tue Jul 06 13:44:54.699431 2021] [wsgi:error] [pid 625:tid 140010969868032] [remote 172.28.0.3:40972] [2021-07-06 13:44:54,699] ERROR django.request: Internal Server Error: /api/lambda/functions/pth-shiyinzhang-iog

2021-07-06 13:44:54,700 DEBG 'runserver' stderr output:
[Tue Jul 06 13:44:54.699712 2021] [wsgi:error] [pid 625:tid 140010969868032] [remote 172.28.0.3:40972] ERROR - 2021-07-06 13:44:54,699 - log - Internal Server Error: /api/lambda/functions/pth-shiyinzhang-iog

    docker container ls --filter name=iog

    CONTAINER ID   IMAGE                             COMMAND                  CREATED       STATUS                 PORTS                                         NAMES
3b6ef9a9f3e2   cvat/pth.shiyinzhang.iog:latest   "conda run -n iog pr…"   4 hours ago   Up 4 hours (healthy)   0.0.0.0:49159->8080/tcp, :::49159->8080/tcp   nuclio-nuclio-pth-shiyinzhang-iog

    docker logs nuclio-nuclio-pth-shiyinzhang-iog

    If before model deployment you see that the NODE PORT is 0, you need to assign it manually. Add the port: 32001 attribute to the function.yaml file of each model, before you deploy the model. Different ports should be prescribed for different models.

    triggers:
myHttpTrigger:
    maxWorkers: 1
    kind: 'http'
    workerAvailabilityTimeoutMilliseconds: 10000
    attributes:
+     port: 32001
      maxRequestBodySize: 33554432 # 32MB

    Installation serverless functions on Windows 10 with using the Ubuntu subsystem

    If you encounter a problem running serverless functions on Windows 10, you can use the Ubuntu subsystem, for this do the following:

    1. Install WSL 2 and Docker Desktop as described in installation manual

    2. Install Ubuntu 18.04 from Microsoft store.

    3. Enable integration for Ubuntu-18.04 in the settings of Docker Desktop in the Resources WSL integration tab:

      Docker WSL integration Ubuntu 18.04

    4. Then you can download and install nuctl on Ubuntu, using the automatic annotation guide.

    5. Install git and clone repository on Ubuntu, as described in the installation manual.

    6. After that, run the commands from this tutorial through Ubuntu.