CVAT vs MakeSense.ai

CVAT turns images, video, 3D data and audio into labeled datasets for AI workflows. Its annotation tools include boxes, masks, polygons, keypoints, skeletons and object tracks, with additional listed types such as cuboids, ellipses and tags. Teams can review jobs through validation and acceptance stages and assess quality using ground truth, honeypots and consensus workflows. Storage connections include Amazon S3, Azure Blob Storage, Google Cloud Storage, compatible buckets and self-hosted storage. CVAT names SAM 2, SAM 3, Ultralytics, Hugging Face and custom models as labeling aids. It exports to more than 20 formats, including COCO, YOLO, KITTI, Cityscapes and Pascal VOC, and offers API, SDK and CLI automation. Deployment can be managed in the cloud or self-hosted, including on-premises and air-gapped options. Free plans are available; online paid plans start at $23/user/mo billed annually. CVAT lists security controls but says it does not hold ISO 27001 or SOC 2 certifications.

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.