MakeSense.ai vs VGG Image Annotator
MakeSense.ai
makesense.aiVGG Image Annotator
robots.ox.ac.ukSide by side
| Figure | MakeSense.ai | VGG Image Annotator |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | Yes |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 2 |
| Spec score · Image Annotation Software | 8.6 (#3) | 8.2 (#8) |
| Spec score · AI Image Annotation Tools | 8.6 (#4) | 8.2 (#7) |
| Spec score · Data Labeling Software | 8.5 (#5) | 8.2 (#9) |
| Annotation features | The tool supports point, line, rectangle, polygon, and label annotations. | The user guide describes drawing regions such as rectangles, circles and points, and importing or exporting annotations as CSV or JSON. |
| Documentation | The public documentation contains separate pages for imports, exports, shortcuts and local setup. | Documentation includes an in-app getting-started guide, FAQ, user guides, JSON-format documentation and per-version code documentation. |
| Intended users | The project describes the tool as suitable for small computer vision deep learning projects and dataset preparation. | The page describes the software as suitable for academic projects and commercial applications, and includes tools for researchers annotating images, audio and video. |
| Maker | The repository credits Piotr Skalski as author and copyright holder, with a 2019 project copyright. | VIA is developed by the Visual Geometry Group in Oxford University's Department of Engineering Science. |
| What it does | MakeSense.ai is a browser tool for labeling photos and preparing computer-vision datasets. | VIA is a manual annotation suite for images, audio and video. |
Plans
In their own words
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.
VGG Image Annotator
VGG Image Annotator (VIA) is a suite of lightweight browser tools for manually annotating images, audio, and video. The applications are distributed as single HTML files, allowing offline use without installation or setup. VIA3 handles images, video, audio, and shared or collaborative projects. VIA2 works with image files, does not support shared projects, and remains maintained. Annotation shapes include rectangles, circles, ellipses, points, polygons, and polylines. Projects can import or export annotations as CSV or JSON, while scripts support COCO and Web Annotation formats; a Python package converts VIA projects to COCO. Shared work can use a free public Version File Store maintained by the Visual Geometry Group, or a user-run VFS. VIA is built with HTML, CSS, and JavaScript without external libraries and released under the two-clause BSD license for academic and commercial use. Downloads include ZIP archives and standalone HTML files for versions 2 and 3. The makers provide a guide, FAQ, and email contacts.