Deepen AI vs Doccano
Deepen AI
deepen.aiDoccano
doccano.github.ioSide by side
| Figure | Deepen AI | Doccano |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 1 |
| Spec score · Data Labeling Software | 7.7 (#16) | 8.8 (#2) |
| Deployment | The 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. |
| Purpose | Deepen 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. |
| Support | The 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
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.
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.