AI dataset management, data curation, and version control.

Data labeling tools and efficient project management.

‍ Key features: The software itself focuses on 5 key components of the AI lifecycle: SuperAnnotate raised about $22M from investors such as P9 and Base10. research, SuperAnnotate was founded in 2018, primarily as an image annotation tool for semantic segmentation, but quickly gained momentum and extended into other areas of ML pipeline development. The company also provides data on its platform with various labeling tools without annotation services. The labeling teams are directly integrated into SuperAnnotate's platform and managed by their professional project managers which is reported to be one of their key strengths.

Ranked as the best data labeling platform in G2, SuperAnnotate builds an end-to-end data solution with an integrated service marketplace, where they help their customers to find the right annotation team within the preferred geographic location and proficiency. We will be updating this article quarterly and tracking all the position changes, feature releases, large news, and announcements. Project and quality management ( Team/role management, annotation project management, and performance tracking).MLOps and Automation ( SDK, webhooks, orchestrations, AI-enabled labeling, model management, etc.).Integrations and security (Storage integrations, model inference API or model training integrations, etc.).AI Data Management and curation (active learning, query system, smart sampling, subset selection, data versioning, etc.).Annotation software ( image annotation, video annotation, text annotation, etc.).As we indicated above, for each company/solution, we will cover the following: We will rely on one of the most reliable software ranking marketplaces, G2, where data labeling is a separate software category within various AI software. We will cover various types of data labeling companies, along with their history and functionalities, detailed feature sets, additional AI pipeline-oriented components, and much more. Therefore, this article is neither about simple data labeling tools using open-source software such as label studio, cvat, labelme, nor is it about specific functionalities within labeling editors such as bounding boxes, polygons, text labeling, etc. Namely, annotation software, AI data management and curation, integrations and security, project and quality management, and automation. We identified 6 essential components that make data labeling tools a compelling solution for building modern AI pipelines. However, data labeling tools or simple data labeling editors are far away from covering all the growing needs of anyone's complex ML pipeline. Labels are assigned to a single polygon.Since ML engineers are spending such a huge portion of their time on structuring, labeling, versioning, and debugging datasets to become AI-ready training data (aka SuperData), data labeling toolsets have become essential for building scalable AI applications.Flags are assigned to an entire image.When the program is run with this flag, it will display labels in the order that they are provided. Without the -nosortlabels flag, the program will list labels in alphabetical order.If you would prefer to use a config file from another location, you can specify this file with the -config flag. You can edit this file and the changes will be applied the next time that you launch labelme.

The first time you run labelme, it will create a config file in ~/.labelmerc.Annotations will be stored in this directory with a name that corresponds to the image that the annotation was made on. json, the program will assume it is a directory. Only one image can be annotated if a location is specified with. json, a single annotation will be written to this file. -output specifies the location that annotations will be written to.

Image annotation tool open source