LabelU vs MakeSense.ai
LabelU
opendatalab.github.ioMakeSense.ai
makesense.aiSide by side
| Figure | LabelU | MakeSense.ai |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | Yes |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 1 |
| Spec score · Data Labeling Software | 7.2 (#21) | 8.5 (#5) |
| Export formats | The project says it supports exporting annotation data in JSON, COCO, and MASK formats. | Supported exports include CSV, YOLO, VOC XML, VGG JSON and COCO JSON, depending on annotation type. |
| Maker | The LabelU repository is published under the OpenDataLab GitHub organization. | The repository credits Piotr Skalski as author and copyright holder, with a 2019 project copyright. |
Plans
In their own words
LabelU
LabelU has no write-up on Specifiction yet; its figures are above.
MakeSense.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.