Pixano vs Segments.ai
Pixano
pixano.cea.frSegments.ai
segments.aiSide by side
| Figure | Pixano | Segments.ai |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | Yes |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | — |
| Plans published | 0 | 4 |
| Spec score · AI Image Annotation Tools | 7.6 (#15) | 8.1 (#9) |
| Spec score · Image Annotation Software | 7.6 (#18) | 8.1 (#11) |
| Founded | 2020 | 2020 |
| Intended users | CEA-List describes Pixano as supporting AI developers and applications in areas including manufacturing, security, robotics, and transportation. | The company describes the product as designed for computer vision engineers and data scientists working in autonomous robotics and driving. |
| Product | Pixano is an open-source tool for exploring and annotating computer vision datasets with AI features. | Segments.ai is a multi-sensor data labeling platform for robotics and autonomous vehicle teams. |
| Security and compliance | The pages reviewed did not state a security certification or compliance standard. | Company materials state that Segments.ai is ISO 27001 certified, GDPR compliant and EU based. |
| Support | The project README directs users to its Getting Started guide and contributing guide for usage and contribution information. | Fusion includes priority support and mail, Slack, and Discord support. |
Plans
Segments.ai
- Academic licenseFree
- CorePrice not published
- EnterprisePrice not published
- FusionPrice not published
In their own words
Pixano
Pixano is a free, open-source tool for exploring and annotating computer-vision datasets, including collections of text, images, and video. Its annotation tools cover boxes, polygons, pixel masks, keypoints, cuboids, classification, and tracking; video labels can be propagated across time. Smart features include semantic search using models such as CLIP and segmentation using models such as SAM. Dataset formats such as COCO can be imported and exported, and Pixano uses Lance storage for dataset navigation. Its reusable Web Components can be assembled into custom apps. Pixano documents REST and Python APIs, along with Pixano Inference, a Ray Serve-based inference server with a Python client and REST API. Installation options documented by the project are pip in a Python virtual environment or an official Docker image. The supported Python range is 3.10 or later and earlier than 3.14. Pixano is licensed under CeCILL-C and is under active development, so its API may change. Three-dimensional point-cloud support is described as planned.
Segments.ai
Segments.ai is a data labeling platform for robotics and autonomous vehicle work, with tools for both images and 3D point clouds. Teams can mark data with segmentation, cuboids, bounding boxes, keypoints, polygons, and polylines, view images alongside point clouds, and project labels between sensors. For sequences, Batch Mode, merged point clouds, interpolation, and automated tracking support labeling across objects and frames. Image workflows include Superpixel, Autosegment, hover-and-click segmentation, and model predictions that teams can review and correct. Its Python SDK handles uploads, label downloads, dataset management, and webhooks; TensorFlow and PyTorch are named integrations. The product is available through web and API access, and datasets can be exported as JSON, PyTorch, TensorFlow, or Hugging Face formats. Core includes cloud bucket integrations. The company states it is EU based, ISO 27001 certified, and GDPR compliant. Pricing is on request, with a 14-day trial; a free academic license is available for non-commercial research at free.