MakeSense.ai vs Pixano

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.

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.