makesense.ai is a free to use online tool for labelling photos. Thanks to the use of a browser it does not require any complicated installation - just visit the website and you are ready to go. It also doesn't matter which operating system you're running on - we do our best to be truly cross-platform. It is perfect for small computer vision deeplearning projects, making the process of preparing a dataset much easier and faster. Prepared labels can be downloaded in one of multiple supported formats. Documentation and more details can be found in the project repository.
The Computer Vision Annotation Tool (CVAT) is a free, online, interactive video and image annotation tool for Computer Vision. Many UI and UX decisions are based on feedback from professional data annotation teams.
- Introduction [video]
- Annotation mode [video]
- Interpolation mode [video]
- Attribute mode [video]
- Segmentation mode [video]
- Tutorial for polygons [video]
- Semi-automatic segmentation [video]
Classifai aims to be one of the most comprehensive open-source data annotation platforms available. It supports the labelling of various data types with multi labelled outputs forms for AI model training. Plus it runs locally on your computer.
Classifai has native builds for Windows, Linux and MacOS so it's easy to get started.
Figure below show how Classifai fits in the machine learning workflow. It enables the labelling of raw data imported from data source. The labelled data can then channel into training environments for supervised / semi-supervised learning.
Classifai also comes with a Conversion Launcher which is useful for Optical Character Recognition (OCR), Currently supports the conversion of format of pdf/tif to png/jpg.
- Download: https://classifai.ai/products/
- Source Code: https://github.com/CertifaiAI/classifai
- Documentation: https://docs.classifai.ai/
COCO Annotator is a web-based image annotation tool designed for versatility and efficiently label images to create training data for image localization and object detection. It provides many distinct features including the ability to label an image segment (or part of a segment), track object instances, labeling objects with disconnected visible parts, efficiently storing and export annotations in the well-known COCO format. The annotation process is delivered through an intuitive and customizable interface and provides many tools for creating accurate datasets.
COCO Annotator allows users to:
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directly export to COCO format
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segment the objects
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to add key points
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to analyze data through useful API endpoints
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import datasets already annotated in COCO format and many more.
doccano is an open source text annotation tool for human. It provides annotation features for text classification, sequence labeling and sequence to sequence. You can create labeled data for sentiment analysis, named entity recognition, text summarization etc. https://doccano.herokuapp.com/
A semantic annotation platform offering intelligent assistance and knowledge management. It's free and more feature-rich than prodi.gy. https://inception-project.github.io
A jupyter notebook that uses language embeddings and dimensionality reduction techniques to apply labels in bulk. The technique was developed at Rasa and is demonstrated in detail on youtube
