cleanlab
The standard package for machine learning with noisy labels and finding mislabeled data. Works with most datasets and models. [Moved to: https://github.com/cleanlab/cleanlab] (by cgnorthcutt)
SSL4MIS
Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations. (by HiLab-git)
| cleanlab | SSL4MIS | |
|---|---|---|
| 5 | 2 | |
| 2,254 | 2,010 | |
| - | 2.4% | |
| 8.4 | 6.2 | |
| almost 3 years ago | 10 months ago | |
| Python | Python | |
| GNU Affero General Public License v3.0 | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
cleanlab
Posts with mentions or reviews of cleanlab.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-03-29.
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[P] Confident Learning making ML QA 34x cheaper
Code for https://arxiv.org/abs/1911.00068 found: https://github.com/cgnorthcutt/cleanlab
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Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
Code: https://github.com/cgnorthcutt/cleanlab
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[D] Andrew Ng's data-centric vs model-centric Machine Learning
I am an author on this, so I am biased. Around half a decade ago, we began developing a field at MIT called confident learning [ paper | blog | reddit post ] that takes a data-centric approach: instead of improving the model quality, it improves the data label quality. It's used by Google, Facebook, and is open-sourced in Python as the cleanlab package.
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[R] Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
👍An easy first step to find label errors in datasets is cleanlab: https://github.com/cgnorthcutt/cleanlab
SSL4MIS
Posts with mentions or reviews of SSL4MIS.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-04-07.
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Researchers at Oxford University Propose a Machine Learning Framework Called ‘TriSegNet’ Based on Triple-View Feature Learning for Medical Image Segmentation
Continue reading | Check out the paper and github link
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How to get image dataset annotated? Any idea?
Otherwise, you may be able to look into semi-supervised learning. Basically, you label a subset of your data, and use semi-supervised techniques to extrapolate and label the rest. This, of course, is a challenge in itself, but luckily this particular challenge has been researched a lot, so you may find something to get started with.
What are some alternatives?
When comparing cleanlab and SSL4MIS you can also consider the following projects:
zeroshot_topics - Topic Inference with Zeroshot models
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
karateclub - Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
awesome-data-labeling - A curated list of awesome data labeling tools
uda - Unsupervised Data Augmentation (UDA)
alibi-detect - Algorithms for outlier, adversarial and drift detection