Here are some of the key definitions a user would come across while using Labellerr.
Terminology Reference
| Key | Definition |
|---|---|
| Annotation | Annotation is the process of labeling or tagging data (such as text, images, videos, or audio) to provide meaningful information or context for machine learning models. This process helps models understand and learn patterns in the data for tasks like object detection, classification, transcription, or segmentation. |
| Auto Label | A tool that can be set up to call auto labeling predictions powered by A.I. |
| Attributes | The attributes of an object refer to additional properties or characteristics that provide more context or details about the object being labeled. |
| Bounding Box | A rectangular box drawn around an object to define its location and size. |
| Bulk Assign | Owners and Supereditors have the option of changing the status and reassigning samples to one of the annotators through manual selection. The assigned files would then be displayed for action to the particular user. |
| Classification | Classifications are structured questions used to capture specific details about an object, ensuring consistency in annotations. Examples include formats like radio buttons, dropdowns, input fields, and multi-select options. |
| Data type | The type of data to be selected for a project. For. E.g. Image, Video, Audio, Text. |
| Dataset | A collection of Data files that are added to Labellerr for annotation. A project may have multiple datasets. |
| Data Sources | The source from which data is uploaded. On Labellerr, data can be uploaded from Google Drive, Dropbox, Google Cloud Storage, Amazon S3, Microsoft Azure, and Local Drives/Storages. |
| Data Curation | Data curation is the practice of gathering and managing data to use for analytical purposes. |
| Export | The process of saving annotated files in formats like JSON, CSV, or PNG for use. |
| File Type | File types are the file format supported. For E.g Pictures (JPG/PNG), Video (MP4), text (.txt files), etc. |
| Guidelines | A set of instructions or parameters that can be added to each object, classification, or file to help annotators in labeling. Guidelines can be added while creating a dataset or by reviewers. |
| Hotkeys | Keyboard shortcuts that speed up annotation tasks. |
| Interpolation | Creating annotations for in-between frames in a video automatically. |
| Masking | Covering specific areas of an image for pixel-perfect segmentation. |
| Metadata | Additional information about files, such as timestamp or location. |
| Object | Objects refer to the specific entities or elements in an image, video, text, or other data types that need to be labeled or identified. |
| Workspaces | A workspace is a ‘Centralized environment’ enabling large organizations to manage and track multiple projects under the same domain. A single Organization might have a single subsidiary or teams where each team might own a separate workspace. |
| Project | Projects include multiple datasets, users, annotations, etc with a single datatype. For E.g A project named ‘Buildings’ with image files can have multiple datasets of Buildings all of the same ‘Image’ file type. |
| Polygon | A multi-sided shape used to annotate irregular objects more precisely. |
| Polygon Line Width | The thickness of the border around a polygon. |
| Taxonomy | Taxonomy is the system of organizing things into categories based on the shared characteristics. A template with a set of objects and classifications or filtration can be created and saved to be made available and applied to future projects. |
| Status | File status shows the progress of a file in the annotation process, such as unassigned, in progress, pending review, or completed. It helps track tasks and identify files needing attention or rework. |
| Segmentation | Dividing an image or object into parts, such as semantic or instance segmentation. |
| User Roles | Users can be assigned different roles which grant them access to a different interface and activities on Labellerr. A user can be an Annotator, Reviewer, Admin, Supervisor, etc. |
For further assistance contact support@tensormatics.com