Doccano vs LightlyStudio
Doccano
doccano.github.ioLightlyStudio
lightly.aiSide by side
| Figure | Doccano | LightlyStudio |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | Yes |
| Open source | — | Yes |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 4 |
| Spec score · Data Labeling Software | 8.8 (#2) | 8.5 (#4) |
| Collaboration | Features include collaborative annotation and multi-language support. | Enterprise collaboration provides Viewer, Labeler, Editor and Admin roles plus centrally managed cloud credentials. |
| Deployment | The repository lists one-click deployment options for AWS and Heroku, and the guide supports installation locally or in the cloud. | LightlyStudio Enterprise is offered as a Lightly-hosted service or as an on-premise deployment; the on-premise option can run fully offline and air-gapped. |
| Founded | 2018 | 2019 |
| Integrations | The auto-labeling guide demonstrates Amazon Comprehend Sentiment Analysis and allows users to configure a custom REST API. | It reads images and videos from Amazon S3, Google Cloud Storage, and Azure Blob Storage, and supports common annotation formats including COCO, YOLO, Pascal VOC, and YouTube-VIS. |
| Support | The project directs users to its FAQ and invites them to contact the author for help and feedback. | The Open Source plan lists no customer support; the on-premise offering includes dedicated engineering support. |
| Supported systems | The install guide says doccano can be installed on Linux, Windows, or macOS machines running Python 3.8 or later. | The local application runs on Windows, Linux, and macOS and supports x86 and ARM processors with Python 3.9 or later. |
Plans
LightlyStudio
- Open SourceFree
- Open SourceFree
- Lightly-HostedPriced on request
- On-PremisePriced on request
In their own words
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.
LightlyStudio
LightlyStudio has no write-up on Specifiction yet; its figures are above.