Label Types
Learn about the labeling methods on the Input-Viewer
We support different types of labeling methods, each suited for different tasks and data characteristics. This lets you create high-quality training data depending on the objective you want your machine learning model to achieve.
We provide the following label types for your images, videos, and texts:
- Classification — categorizes images, videos, and texts into categories;
- Detection — detects where an object of interest is and draws a bounding box around it;
- Segmentation (polygons for segmentation) — outlines the exact shape or contour of the object;
- Masks — a type of image segmentation that defines the exact boundaries of an object at a pixel level.
You can use any of the labeling types to complete your tasks easily and quickly. These label types provide increasing levels of granularity to support the needs of your specific use case.
Let's illustrate how you can carry out each label type on the Input-Viewer page.
Click here to learn how to use the Scribe Labeling Tasks tool to carry out each of the different label types.
Manual Labeling on Input-Viewer Page
The Input-Viewer page is the main page that showcases the details of a single input available in your app. If you click an input listed on the Input-Manager page, you'll be redirected to the viewer page for that input, where you can see its details.
After navigating to the Input-Viewer, ensure the page's mode is set to Annotate, which is the default status. You can find the mode settings at the upper-right corner of the page.
Here is an example:
The page has several assistive tools to help you make the most of the labeling exercise. For example, on the top section, you can select an icon to label an input with bounding boxes, move an image around the workspace, or zoom an image in or out.
The left sidebar shows a film strip of images you can scroll through to get the specific image you want to annotate. If you click any image on the sidebar, it'll appear on the main workspace.
Click here to learn how to carry out AI-assisted labeling on the Input-Viewer.
Classification
The classification label type lets you assign annotations to an entire image, a single frame of video, or a piece of text.
Image Classification
To manually classify an image on the Input-Viewer page, first click the Select / Edit tool (it's selected by default) on the navigation bar.
The keyboard shortcut for the Select / Edit tool is V.
Next, click the Add annotation tags... field and select the concept you want to use to annotate the image from the drop-down list — that is, if the concept already exists in your app.
If you want to annotate an input with a new concept that does not already exist in your app, click the Add annotation tags... field and type the concept's name. Then, click the drop-down box that appears with the concept name beneath that field.
The new annotation will be added to your app and labeled with your input.
You can add as many annotations as you want. The added annotations will appear in the Add annotation tags... field as well as on the right sidebar of the page.
Text Classification
You can classify your text inputs into pre-defined categories — just as with image classification described above.
Ensure you select the appropriate base workflow when creating an app for text inputs.
Video Classification
Support for video labeling is coming soon.
Detection
The detection label type lets you identify the objects in your inputs and also draw bounding boxes around them.
Detection for Still Images
To manually add detection labels on the Input-Viewer page, first click the Bounding box tool on the navigation bar.
The keyboard shortcut for the Bounding box tool is B.
Next, click the Add annotation tags... field and select the concept you want to use from the drop-down list — that is, if the concept is already existing in your app. If it's not already existing, you'll need to add it as described earlier.
Next, draw a rectangle as closely as possible around the image's region of interest.
You can add as many detection annotations as you want. The added annotations will appear in the Add annotation tags... field as well as on the right sidebar of the page.
Detection for Video
Support for video labeling is coming soon.
Segmentation
The segmentation label type lets you outline a boundary of an object using a series of vertices that define a closed polygonal shape. It's ideal for annotating irregularly shaped areas or objects.
To manually add segmentation labels on an image on the Input-Viewer page, first click the Polygon tool on the navigation bar.
The keyboard shortcut for the Polygon tool is P.
Next, click the Add annotation tags... field and select the concept you want to use from the drop-down list — that is, if the concept is already existing in your app. If it's not already existing, you'll need to add it as described earlier.
Next, draw a contour as closely as possible around the image's region of interest.
You can add as many detection annotations as you want. The added annotations will appear in the Add annotation tags... field as well as on the right sidebar of the page.
Masks
The masks label type lets you label each pixel within the region of interest. It provides pixel-level labeling that allows you to precisely identify and delineate objects within an image.
Currently, you can only minimally review existing image mask annotations on the Input-Viewer page. After creating mask images via the API and uploading them on the platform, you can view them on the page.
You can delete an entire image mask annotation on an input on the Input-Viewer page. However, we currently do not support creating and editing mask annotations on the page.
