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SPECIFICTION
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Deepen AI vs Doccano

Deepen AI

deepen.ai

Doccano

doccano.github.io

Side by side

FigureDeepen AIDoccano
Price from—Free
Free plan—Yes
Free trial——
Open source——
WebYesYes
Windows—Yes
macOS—Yes
Linux—Yes
APIYesYes
Self-hostedYesYes
Plans published01
Spec score · Data Labeling Software7.7 (#16)8.8 (#2)
DeploymentThe listed deployment options are managed cloud, virtual private cloud, on-premises, and air-gapped environments.The repository lists one-click deployment options for AWS and Heroku, and the guide supports installation locally or in the cloud.
PurposeDeepen AI provides a connected data infrastructure stack for Physical AI, covering calibration, capture, curation, annotation, and validation.Doccano is an open-source text annotation tool for machine-learning practitioners.
SupportThe Services page describes a dedicated program manager as the point of contact for an engagement.The project directs users to its FAQ and invites them to contact the author for help and feedback.

Plans

Deepen AI

No plans published.

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Doccano

  • doccanoFree

Doccano pricing →

In their own words

Deepen AI

Deepen AI provides connected data infrastructure for Physical AI, linking sensor calibration, capture, curation, annotation, and validation. Deepen Calibrate aligns camera, LiDAR, radar, and IMU sensors to a shared frame; the site reports under 0.8-pixel reprojection error and sub-1° angular error. Deepen Curate deduplicates sensor data, searches clips and frames, and surfaces rare or long-tail scenarios before labeling. Deepen Annotate handles 2D and 3D labels, including boxes, segmentation, keypoints, point clouds, and multi-frame tracking. Deepen Validate scores scenario coverage, audits and compares labels, prioritizes risky frames for review, and records signed audit trails. REST APIs and SDKs connect products to customer pipelines. Supported formats include ROS 2, MCAP, nuScenes, HDF5, Parquet, COCO, Rosbag, Zarr, KITTI, ORC, Pascal, WebDataset, and ASAM OpenLABEL. Deployment spans managed cloud, VPC, on-premises, and air-gapped environments. Pricing is on request.

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Doccano

Doccano is a free, open-source data-labeling tool for machine-learning practitioners. Its supported annotation tasks include text classification, sequence labeling, and sequence-to-sequence annotation, with uses such as sentiment analysis, named entity recognition, and text summarization. Users can set up projects, import datasets, add collaborators, annotate examples, and export labeled data. REST APIs let scripts connect to Doccano for model-assisted labeling, and multiple people can work collaboratively. Installation options include pip, Docker, Docker Compose, source, or cloud deployment. The installation guide lists Linux, Windows, and macOS machines running Python 3.8 or later. SQLite 3 is the default database; PostgreSQL configuration is described, and MySQL is mentioned as an option. Imported datasets can be stored using Amazon S3 or Google Cloud Storage. The project also lists mobile and multi-language support, emoji support, and a dark theme. Integration guidance demonstrates Amazon Comprehend Sentiment Analysis and custom REST APIs for auto-labeling. One upgrade caveat is important: the installation guide warns that an upgrade using SQLite 3 can cause database loss.

Visit doccano.github.io