Tutorials¶
Ipyannotator uses tutorials to demonstrate how the library can be used.
All tutorials are statically generated from jupyter notebooks. All notebooks can be found on our Github’s repository
The notebooks contains the development dependencies: [pytest](https://docs.pytest.org/en/7.1.x/) and [ipytest](https://github.com/chmp/ipytest), if you want to run the notebooks remove the tests or install the libraries mentioned.
- Image classification - Assigning meaning to images via classes
- Bounding Box Annotator - Identifying objects in images through boxes
- Video Annotator - Tracking objects through video frames
- Road damage - Iterative annotations on road damage images
- Voila - Using Ipyannotator as a standalone web application
- Build Annotator - Understanding Ipyannotator design to easily extend and customize
- Image classification - Real project example with CIFAR-10 dataset