MakeSense.ai vs Segments.ai

MakeSense.ai is a browser-based tool for labeling photos and preparing datasets for computer vision projects. It supports point, line, rectangle, polygon, and label annotations, with model-assisted labeling through YOLOv5, SSD pretrained on COCO, and PoseNet. Users can load their own YOLOv5 models exported to TensorFlow.js format. Export options include CSV, YOLO, VOC XML, VGG JSON, and COCO JSON; imports include YOLO, VOC XML, and COCO JSON. The available format combinations vary by annotation type, and some are not yet supported. The project says images are not sent to a server and AI features run using TensorFlow.js. It works in a browser across operating systems, and documented local and Docker setups are available; Docker serves the application on localhost:3000. The project describes it as suitable for small computer vision deep-learning projects and dataset preparation. It was founded in 2019, is licensed under GPL-3.0, and credits Piotr Skalski as author and copyright holder. The Free plan costs nothing.

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.