Labellerr vs MakeSense.ai

Labellerr is a data labeling and annotation platform for AI and machine-learning teams preparing training data. Projects can use images, videos, PDFs, text, and audio. Annotation options include bounding boxes, polygons, polylines, keypoints, dots, classification, segmentation, and object detection. Automated labeling includes prompt-based and model-assisted methods, plus active learning; the company also states native support for Meta’s SAM, SAM 2, and SAM 3. A Python SDK can manage datasets and workflows in ML pipelines. Annotated data can be exported in formats such as COCO JSON, YOLO, Pascal VOC XML, CSV, and custom formats. Remote storage options include AWS S3, Google Cloud Storage, and Azure Blob; integrations with Vertex AI, SageMaker, and Azure ML are add-ons for applicable plans. Stated security measures include Auth0 authentication, TLS 1.2 or higher in transit, and AES-256 encryption. The free Researcher plan includes 2,500 data credits, one seat, one workspace, and up to 10 projects. Pro costs $9,999 a year, and unused data credits do not roll into the next payment cycle.

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.