ipyannotator
ipyannotator copied to clipboard
palaimon
the infinitely hackable annotation framework
Bumps [abatilo/actions-poetry](https://github.com/abatilo/actions-poetry) from 2.1.6 to 2.3.0. Release notes Sourced from abatilo/actions-poetry's releases. v2.3.0 2.3.0 (2023-02-24) Features by default use the latest poetry version (#62) (192395c) v2.2.0 2.2.0 (2022-11-26) Features update...
![dependabot[bot] avatar](https://avatars.githubusercontent.com/in/29110?v=4 "dependabot[bot] avatar"){kind=avatar}
Bumps [actions/setup-python](https://github.com/actions/setup-python) from 4.1.0 to 4.5.0. Release notes Sourced from actions/setup-python's releases. Fix cache issue for Poetry projects located in subfolders In scope of this release we fixed cache issue...
# Patching CVE-2007-4559 Hi, we are security researchers from the Advanced Research Center at [Trellix](https://www.trellix.com). We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a...
Bumps [JamesIves/github-pages-deploy-action](https://github.com/JamesIves/github-pages-deploy-action) from 4.4.0 to 4.4.1. Release notes Sourced from JamesIves/github-pages-deploy-action's releases. v4.4.1 What's Changed The action now fully runs on Node 16. (Thank you @nickmccurdy) Changelog Bump eslint-plugin-jest from...
Cleanlab is a tool that can detect label errors in datasets. The [label error tutorial](https://github.com/cleanlab/label-errors/blob/main/examples/Tutorial%20-%20How%20To%20Find%20Label%20Errors%20With%20CleanLab.ipynb) shows how the tool can be used to detect errors in Cifar-10 (and others datasets)....
Ipyannotator uses an API that contains a pair of Input/Output and a Settings class. The `Settings` contains parameters that are used for all annotators, but some of them are redundant....
Ipyannotator has an API to use its previously defined annotators. The API uses a pair of input/output and when this pair it's not configured the API should throw a friendly...
Fix #37 This MR adds test coverage to Ipyannotator. Feature adapted [from FastAI Forum](https://forums.fast.ai/t/nbdev-code-coverage-n-tests/73993). I'm attaching the coverage file result as well. [coverate.txt](https://github.com/palaimon/ipyannotator/files/9351873/coverate.txt)
Motivation ---------- ipyannotator currently support image labeling. However, for data sets with a very large number of classes it's very difficult to quickly match the image to the right class....
I modified the im2imannotator, so it displays a grid of 4 images (when possible) An example from 01b_tutorial_image_classification with the `oxford_flowers` dataset  It falls back to using the text...
