ADVIT Product Documentation
Introduction Page
ADVIT (Analysis of Data-like Video Image and Text) is the most advanced AI-assisted data annotation platform used to create and manage high-quality training data all in one place. ADVIT includes deep learning models for automated labeling and ease of annotation.
The following are the premier features of ADVIT:
  1. Custom UI - Custom user interface (UI) designed specifically for the ease of manual annotation and automated labeling.
  2. Secure - Easy to label data, securely export data in multiple standard formats, swiftly build and train models, and integrate these models into real-time workflows.
  3. Models IncludedADVIT includes 10 Deep learning (DL) models which can automatically label data across more than 100 classes.
  4. Integration - Capable of integrating custom trained models for annotation.
  5. Tool to annotate at scale: A lot of people may be involved in the annotation process with access permissions, quality control, workers activity monitoring, progress monitoring, etc.
  6. Enterprise Identity & Access management: Easily manageable annotator team (at different locations) and data scientists for the data labeling, management and access management.
  7. Data Support - Supports all type of data
    1. Image (.jpg, .jpeg, .png)
    2. Video (.mp4, .avi, .mov)
Secure Dataset management platform for labeled and unlabeled data
Getting Started...
ADVIT is protected with multiple layers of security measures in order to prevent unauthorized access to your projects, data, models and annotations.
  1. Registration:
    The first time you register on ADVIT, you are required to enter your username, email id, and password to register.
  2. Login:
    1. Enter your Admin Username and Password.
    2. For additional security, you can determine which parts of the Admin each user has permission to access, and also limit the number of login attempts. By default, after six attempts the account is locked, and the user must wait a few minutes before trying again.
    3. Locked accounts can also be reset from the Admin.
  3. Sign out of the ADVIT:
    1. In the upper-right corner, click the Account (Account) icon.
    2. Click Sign Out.
The Sign In page displays a message that you are logged out. We recommend you sign out of the Admin any time you leave your computer unattended.
Platform Pages Overview
  1. Projects:
    The Project Page shows all the projects under a particular user. The user can create multiple projects and add data sets to them.

    The project specifies information like
    1. Dataset Name
    2. Project Creation Date
    3. Time Lapsed
    4. Status of the Project
  2. Dataset Listing and details:
    The dataset page displays all the datasets under a project. The client can add or delete multiple datasets. Images and videos can be uploaded to a dataset. Videos can be converted to frames quickly and can be viewed or deleted.
  3. Manual Annotation:
    A manual annotation page has a classic layout and is used for annotating multiple images in less time. Users can annotate an image in different shapes and label them with various categories. The process of annotating images is very advanced and quick. Users can save, delete and update objects in no time.
  4. Assign Attributes:
    The Attribute Assignment page is designed to incorporate advanced features where a user can tag the annotated images with attribute keys and attribute values very quickly. The client can cozily save, delete and update the tags as per their choice.
  5. Import:
    For importing images users can select from multiple image formats and import images and annotations respectively. The annotations can be uploaded with categories as well.
  6. Export:
    Annotations can be exported based on categories and datasets. Annotations can be exported in standard multiple formats (PASCAL VOC, YOLO txt, or COCO, CSV).
Projects Summary
  1. The project summary page displays multiple projects created by the user. The dashboard summarises the project details, completion status, and time statistics.
  2. A user can create, edit and delete various projects. In addition, statistics feature of the project displays evaluation of datasets, and includes status of the datasets as well as time elapsed since the user created the project.
  3. All the projects are saved by project Id, project name along with the timestamp. The user can sort the listing of projects as per custom requirements.
  4. The ‘Help’ icon showcases product Documentation of ADVIT and the ‘Notification’ icon notifies the user with regular updates.
Dataset Listing and Detail Page
  1. To add a dataset, click on the ‘Dataset’ button shown in the upper right corner of the screen.
  2. A new window will appear as shown in the above image.
  3. Enter the name of the dataset as per your convenience in the ‘Dataset Name’ text box.
  4. Select the ‘Dataset Type’ by clicking on the ‘Image’ (To upload raw image data, please refer import functionality) or ‘Video’ option.
  5. Add your comments (if any) in the ‘Comments’ Text box.
  6. Click on the ‘Save’ button to save the dataset or Click on ‘Save & Create New’ to Save the current dataset and create a new dataset.
  7. The dataset will be created as shown in the image above. Users can add multiple datasets by following the above steps.
  8. User can edit the dataset details by clicking the ‘Edit’ Button.
  9. User can delete the dataset by clicking the ‘Delete’ Button.
  10. After clicking on the ‘Details’ button, the layout shown in the above image will be displayed.
  11. To add files to a dataset, click on the "New File” button and a window will be displayed.
  12. Enter the name of the file as your choice in the ‘File Name’ text box.
  13. Upload the image by dragging the file directly to the ‘Upload Image’ option or selecting the file from the directory.
  14. Add desired comments to the ‘Comments’ text box.
  15. Click on the ‘Save’ button to save the file or click on ‘Save & Create New’ to save the file and create a new file.
  16. After clicking on the ‘Save’ button the user is notified with a success message.
  17. The Columns in the table are as follows:
    1. Name – Displays the Name of the image.
    2. Size – Image size in MB (megabyte)
    3. Resolution – Pixel values in Width X Height.
    4. Type – Specifies format of the image.
  18. Users can quickly visualize images in the dataset by clicking on the ‘View Frames’ button.
  19. The ‘Delete’ button deletes the selected file.
Categories and Attribute
  1. Category (Classes):
    When a new project is created, there are pre-trained custom models for automated labeling which contain predefined categories (Classes).
    If you want to annotate data for a new category, categories can be added at runtime.
    1. To add a category/class click on the ‘+’ icon
    2. Enter the name of the new category and save.
      E.g., Root canal
  2. Attributes:
    1. Once the desired category is created, you can now add attributes to each category by clicking on the + icon in front of the attributes tab.
      1. Root canal Obturation.
      2. Attributes can have various values.
        E.g. - Root Perforation, Broken Instrument.
    2. These categories can be used while the annotation is carried out.
    3. Add, Edit, or delete a category or attribute can be done at any point in time.
Import Page
  1. Importing Images
    1. First, select your preferred image format from the ‘Select Format’ dropdown list of values.
    2. Select the dataset in which you want to upload your images. If the dataset does not exist, Create One.
    3. Upload the .zip file which contains your set of Images.
  2. Importing Annotations
    1. Select your preferred annotation format from the ‘Select Format’ dropdown List.
    2. Upload the .zip file which contains annotations files of respective images as well as categories.txt file.
    3. Click on the ‘Import’ button and wait for few seconds till the ‘Imported Successfully’, once done a success message appears as shown below.
Processing (Automated Labeling)
Use AI to help you build AI.
ADVIT makes it easy to deploy and repurpose existing models.
Automated Data-labeling uses your model predictions to label your training data. This reduces the time and efforts for the data-labeling and increases the efficiency of the data-labelers.

This tutorial demonstrates how automated data-labeling using Machine Learning (ML) models can be configured onto your system using the ADVIT UI / API. With automated data labeling, you can use model predictions to label your inputs. Automated data-labeling can help you to prepare training data, or assign other useful labels and metadata to your inputs. Since models are doing most of the work of annotating your data, this enables you to speed up and scale up your annotation process while ensuring quality standards, typically reducing the human effort of labeling data by orders of magnitude. This process is built into our APIs and it seamlessly integrates with all the functionality of the ADVIT platform.

The following ML/ DL models are available in the ADVIT model library and are ready to integrate with your existing models.
  1. Man, Woman detection
  2. Adult detection
  3. Child detection
  4. Face detection
  5. Facial sentiment analysis
  1. Man-Woman Classification and Detection Model:
    1. Content: Identify man and woman in real-time data such as photo or video data. The AI model returns two classes: "Man" and "Woman" along with bounding boxes in PASCAL VOC, YOLO txt, or COCO JSON format along with its probability scores.
    2. Use Case:
      1. Surveillance System
      2. Effective Retail Analytics
      3. Population Statistics and Prediction
      4. Mobile Application and Video Games to Improve user experience based on gender Information
  2. Face Detection Models:
    1. Content: Face plays a significant role in social engagement for delivering on the personality and sensations of an individual. People cannot recognize unexpected appearances of faces in comparison to machines. The automatic face identification framework does a significant job in face recognition, facial expression recognition, human-computer interaction, etc. Face detection is a computer vision technology that determines the location and size of a human face in real-time data such as photo and video data.
    2. Use Case:
      1. Unlocking mobile phones
      2. Find missing persons
      3. Aid forensic investigations
      4. Identify people on social media platforms
      5. Diagnose facial diseases
      6. Tracking school, colleges, employee attendance
      7. Facilitate secured transactions.
      8. Validate identity at ATM
      9. Control access to sensitive areas
      10. Identify the age of an individual
  3. Facial Expression Recognition Model:
    1. Content: recognizing emotion from pictures is now becoming an important part of the image and in applications based processing human-computer interaction. Automatic facial expression recognition systems have many applications including, human behavior understanding, detection of mental disorders, and synthetic human expressions. AI models have been used with images and videos for displaying facial emotions including happiness, sadness, anger, surprise, disgust, and fear.
    2. Use Case:
      1. Automatic tagging for movie genres like thriller, comedy, romance
      2. Retail Analytics
      3. Extracting data about crowds at public events
      4. Predict actions based on facial expressions.
  4. Root canal Detection Model:
    1. Content: artificial intelligence (AI), represented by deep learning, can be utilized for real-life problems and is applied across all areas of society including the clinical and dental fields. AI models can be used to detect root canal in teeth from digitalized dental X-Ray images for clinical quality improvement. To support dentists to make clinical decisions, we propose an AI model for automated clinical quality evaluation.
    2. Use Case:
      1. Detection of Root canal in Dental X-Ray images
  5. People and Vehicle Model:
    1. Content: Pedestrian and vehicle detection by autonomous cars is an emerging area of research in the automotive community. The AI model can be used to detect people and different vehicles in real-time data such as photo and video data. This is required to make efficacious real-time decisions to avert imminent collisions with vulnerable traffic users such as humans, stranded or moving vehicles, cyclists, or other static obstacles.
    2. Use Case:
      1. Surveillance of vehicles in real-time
      2. Vehicle statistics in a specific location
      3. Traffic Analysis
  6. Multi Class Classification:
    1. Content: This model has widest range of categories comprising of:
      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
Manual Annotation Page
The Instructions for Annotating an image is given as follows:

  1. Enter the file ID in the ‘Enter file ID’ box and click on the ‘Go’ button.
  2. Select the desired annotation shape from the ‘Select Shape’ Dropdown list.
  3. Choose the category from the ‘Category’ Dropdown list and If you feel to add new categories to the dataset go to ‘Category Page’ from ADVIT Menu and follow the given steps.
  4. Click on the ‘Add’ button and start annotating the object from the image.
  5. After Annotating the object, click on the ‘Save’ button to Save the annotated object.
  1. The Annotation will be featured in the image as Shown Above.
  2. The ‘Delete’ button is utilized to Delete the Annotation.
  3. The ‘Update’ button will Update the Annotation on basis of ‘Category’ and ‘Shape’.
  4. The Co-Ordinates Column records the annotation points of the Coordinates and lists accordingly. You can delete the co-ordinate points and redo annotation.
  5. Finally, click on the ‘Save all objects to server’ Button which will save all the annotated objects from the image to the server and will go to the next image for annotation.
Attribute Assignment
The image shown above is the Layout of the Attribute Assignment Page.

  1. Enter the desired file ID in the ‘Enter File ID’ box and click on the ‘Go’ Button.
  2. Below, you can get the Statistics of Attributes assigned as well as Attributes yet to be assigned with ‘Attribute Key’.
  3. For Assignment of Attribute, Select the Annotation from the image.
  4. ‘Attribute Key column shows you a list of Attributes. Select the Attribute among the list.
  5. ‘Attribute Value column shows you a list of Attribute Values. Select the Value among them or Select the ‘CD’ value if you cannot define an attribute to the Annotation.
  6. Click on the ‘Save’ Button and the Attribute value will be assigned to the annotation shown in the below image.
Export Data page
In ADVIT the user interface is simple and easy to use. You simply upload the images you want to annotate, annotate the images and export the labels. ADVIT supports multiple annotations: bounding box, polygon, and point annotation.
You can export the labels in different formats including YOLO, VOC XML, VGG JSON, and CSV. ADVIT makes it simple for you to export data based on custom category selection.

When you have annotated all the images, you are ready to export your Annotations. Select your desired output format and click “Export”, To export, click on the “Export Annotation” button on the bottom-right of the page

When your project is first deployed, a default Admin account is created with login credentials that give you full administrative access. As a best practice, you should create another user account with full Administrator access. That way, you can use one account for your everyday administrative activities and reserve the other as a “Super Admin” account. This can be helpful if you forget your regular credentials, or they somehow become unusable.

If there are others on your team or service providers who need access, you can create a separate user account for each and assign restricted access based on their role. To limit the modules that users can access when they log in to ADVIT, you must first create a role with limited scope and only the necessary resources selected. Then, you can assign the role to a specific user account. Admin users who are assigned to a restricted role can see and change data only for associated modules, but cannot make changes to any global settings or data.

For users or roles that are temporary, you can also set an expiration date for the user account. Default User types for ADVIT:
  1. Super Admin
  2. Admin
  3. Automaton AI user
  4. Annotator
  5. Project Lead
Create a new user
  1. On the Admin, the sidebar goes to System > User Database.
  2. In the upper-right corner, click Add New User.
  3. To edit an existing user, click a username in the grid. You can modify the User Info and User Role sections as needed.
  4. In the Account Information section, do the following.
    1. Enter the User Name for the account.
    2. The User Name should be easy to remember. It is not case-sensitive. For example, if your username is John, you can also log in as john.
      1. Complete the following information:
      2. First Name
      3. Last Name
      4. Email address
    3. Each user account must have a unique email address.
    4. Enter a password for the account. For Password Confirmation, re-enter the password to make sure it was entered correctly.
    5. Set this Account as Active.
    6. Under Current User Identity Verification, enter your user account password.
Assign a User Role
  1. In the left panel, click User Role. The grid lists all the existing user roles. For a new project, Administrators are the only role available.
  2. In the Assigned column, select a user role. You can view existing or define additional user roles. After a role is defined, you must edit the user account to assign the new role.
    API Documentation
    First time using an API? We're here to help.

    Automaton AI provides a Model Inferencing API for you to directly import annotations at inference runtime. An application programming interface(API) is a connection between computers or between computer programs. It is a type of software interface, offering a service to other pieces of software. If you're unfamiliar with the concept of an API, imagine a television system.
    You have a remote control clicker that allows you to turn on the TV, adjust the volume, and change channels. In this case, Automaton is the TV and the API is your remote control. Our API docs are your user manual. The API docs lay out all the functionalities available through our API in detail.

    • Base endpoint url:
    • You can use our API to import annotations at runtime from any deep learning models (for example: Keras-retinanet, Tensorflow Object Detection API, Yolo)
    • The first thing you need is the API key to integrate with ADVIT - Deep learning Platform.
    Keyboard Shortcuts

    The shortcuts ease the process of annotation as well has helps in quality check of the annotations.

    aTo add a node after current node, while using polygon annotation.
    bTo show all annotated objects on screen
    dTo Delete the current / selected node
    DTo Delete the current / selected object
    eTo save point locally on browser
    hTo fade background
    mTo move current / selected node in any direction and not doesn’t maintain shape of rectangle
    MTo Move current / node and adjust other nodes to as per its axis to maintain shape of rectangle
    nTo tavel through nodes and go to next node
    NTo tavel through nodes and go to previous node
    qTo load and go to previous image
    sTo save all objects to the server
    STo Shift or move whole shape.
    wTo load and go to next image
    xTo show / hide the reference axis on screen
    zTo add object with current selected category
    uTo update and load next image according to presence of objects. (if object is present for 1st image and 20th image then after pressing ‘u’ while being on 1st image it will load 20th image instead of 2nd one)
    yTo update and load previous image according to presence of objects. (if object is present for 1st image and 20th image then after pressing ‘y’ while being on 20th image it will load 1st image instead of 19th one)