State-of-the-Art Data Labeling With a True AI-Powered Data Management Platform
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How can Superb AI’s Platform help in Data Labeling
Superb AI has a unique approach to data labeling. For instance, we can label a ground truth dataset containing hundreds to thousands of objects. Depending on the object detection task’s complexity, we can utilize their transfer learning model and bulk label the large datasets. Once completed, human reviewers use auditing tools to fine-tune and deliver the labeled dataset.
Besides providing terrific support during the labeling process of a massive volume of data. Superb AI’s data labeling platform also lets us work with human labelers to produce highly accurate ground truth labels — and as you may know, producing accurate labels is the initial and one of the most critical steps in a machine learning workflow.
Steps to Labeling Data in Superb AI’s Platform
Superb AI’s platform is straightforward and user-friendly to use and perform data labeling. Please follow the following steps to get started:
Signing up for An Account with Superb AI
- Go to the signup page to create an account.
- Fill the form shown thereafter to create an account:
Login
- Log into Super AI’s Suite account. It will show the following dashboard:
This dashboard provides three important sections:
- Project List
- Data List
- My Account
Project List — Adding a Project
This section displays all created projects and also provides functionality to create a new project. It defines the way to create data labels and manage them.
Steps to add a project:
- Click on the New Project button.
- Fill in the details asked thereafter to create a new project:
Image data types provide the following annotation types:
- Box
- Polyline
- Polygon Segmentation
- Keypoint
- Image Category
Create categories as shown below:
- Note: Categories are only created for image-classification-related tasks.
Create category group
- Select all categories and move right into the Category Group.
- Give the name of the Category Group.
Create an Object Detection class
Object Detection class allows creating a type of object class. Each annotation allows selecting the type of class of annotation like a vehicle, cloth, person, and others, which will enable us to select the annotation type for each class.
Create Class Group
- Note: Creating class groups are only needed when we may have many subclasses within an object class in the dataset. For instance, traffic signs, stop signs, speed limit signs, and so on.
After creating the class groups, Superb AI gives us a complete project overview as shown below:
Adding the Dataset
Superb AI offers three options to upload a dataset, whether it is by uploading a file, uploading a CSV, or by using Cloud Upload. Superb AI’s cloud upload makes it easy to integrate with your cloud storage provider, for instance, AWS S3, GCP, or Google Drive:
- By clicking on the Data List tab, we get the following options to upload a dataset:
For instance, to integrate Amazon S3 with Superb AI, click on AWS S3, and then Add S3 Integration. Next, we are asked to connect our S3 bucket as a data source for the suite. To do so, click on Add, and follow the prompts to connect your S3 bucket.
A neat option when uploading a dataset from the cloud allows us to select whether we would like our images stored directly on Superb AI’s servers or be handled as read-only. Read-only allows for full platform functionality without physically storing images on the platform. This is especially useful when working with sensitive data and for security purposes.
Assign Uploaded Data to a Project
- Click on the Data List tab → Click on Assign to Project button.
Start Labeling
After assigning uploaded data to the project, we can start labeling the data. To start the data labeling process, please follow the following steps:
- Open the project → Click on Start Labeling.
- It opens the labeling dashboard → Click on Start Label List.
- It opens the labeling dashboard → Click on Open Label List → Assign me a new label → Select Image.
- Select Class → “Vehicle”→ Select Category → bus
- Click on Submit
Click on next, and the entire dialogue box appears where we can select the members and assign tasks to other members. Superb AI gives you various options for distribution, whether it be something as simple as the newest images or label uncertainty which is calculated by Superb AI’s Uncertainty Estimation. The latter allows for the identification of hard examples so that teams can quickly assign for manual review, creating smooth, active learning workflows.
After selecting the members, now we move to the distribution tab by clicking on Next. We can see the allocation tab appearing. Then click on the apply tab — the assignment is verified.
After we complete the process above, we can notice that the label’s status is In Progress.
Final step:
- Navigate to the Overview tab, and then the overall summarized view is shown on the dashboard.
Training the Custom Auto Label (CAL)
A big part of the active learning loop is understanding where the AI is highly uncertain, helping teams address those anomalies and complex examples before assigning uncertain items for human review.
To take advantage of Superb AI’s state-of-the-art AI-powered auto label, we first need to export our labels. To do so, click on the Export tab, and then if we click on Export Guide, they give us a quick way to export our labels, whether by exporting all labels or by exporting all submitted labels.
First, we must select the images (label tag “train set”) we want to use to train our Custom Auto-label model. Once the images are selected, click on Export Selected to export these images to train the Custom Auto-label model.
Next, we are directed to the Export page, where we can see our exported images. To train our Custom Auto-label model, click on Create Custom Auto-label AI.
One thing that truly caught our eye during the CAL process was the training’s speediness, as it completes in less than an hour.
Once the Custom Auto-label model is trained, we can find it by clicking on Custom Auto Label in the left navigation bar.
- Note: if we select less than 10 images from the label list, the suite will show us an error message
Note how, after training is complete, the platform shows how much more efficient the custom auto-label will be in comparison to human labelers.
Next, go back to the Label List page, filter for the dataset we would like to label using our Auto Label and select Auto Label as shown. As a reference, the auto-label can run through 100,000 images in about 30 minutes.
Analytics and Insights
By clicking on the Analytics tab, we can get an overview of our project analytics, the total amount of our labels, how many have been submitted, the percentage of the completion process, and others.
Superb AI offers data distribution, workforce and labeler activity, annotation statistics all within one platform.
Conclusion
Superb AI’s suite helps manage, collaborate and strengthen up our ML development cycle immensely. Superb AI creates customized data sets to meet any project’s requirements, using AI to speed up the tagging process.
This platform also helps in reducing the human-in-the-loop hours to speed up our machine learning workflows significantly. It uses a proprietary AI, specifically few-shot, transfer learning, bayesian classical machine learning, and deep learning techniques, to help data practitioners achieve faster labeling of images, videos, and others by splitting training data into smaller components.
By tackling these challenges thanks to Superb AI, we firmly believe that anyone can easily build their own AI systems. By continuing to innovate, we are confident that Superb AI will become the best global SaaS platform in the space of machine learning data management.
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