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Cleanlab Alternatives
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AFFiNE
There can be more than Notion and Miro. AFFiNE(pronounced [ə‘fain]) is a next-gen knowledge base that brings planning, sorting and creating all together. Privacy first, open-source, customizable and ready to use.
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CodeRabbit
CodeRabbit: AI Code Reviews for Developers. Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.
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OpenRefine
OpenRefine is a free, open source power tool for working with messy data and improving it
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refinery
The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
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Nutrient
Nutrient – The #1 PDF SDK Library, trusted by 10K+ developers. Other PDF SDKs promise a lot - then break. Laggy scrolling, poor mobile UX, tons of bugs, and lack of support cost you endless frustrations. Nutrient’s SDK handles billion-page workloads - so you don’t have to debug PDFs. Used by ~1 billion end users in more than 150 different countries.
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argilla
Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets
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label-studio
Label Studio is a multi-type data labeling and annotation tool with standardized output format
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label-errors
🛠️ Corrected Test Sets for ImageNet, MNIST, CIFAR, Caltech-256, QuickDraw, IMDB, Amazon Reviews, 20News, and AudioSet
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grape
🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations (by AnacletoLAB)
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multiannotator-benchmarks
Benchmarking algorithms for assessing quality of data labeled by multiple annotators
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SSL4MIS
Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.
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susi
SuSi: Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)
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token-label-error-benchmarks
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
cleanlab discussion
cleanlab reviews and mentions
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Ask HN: Not a webdev, why are these sites so good?
https://cleanlab.ai/
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[Research] Detecting Annotation Errors in Semantic Segmentation Data
We have feely open-sourced our new method for improving segmentation data, published a paper on the research behind it, and released a 5-min code tutorial. You can also read more in the blog if you'd like.
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[R] Automated Quality Assurance for Object Detection Datasets
We’ve open-sourced one line of code to find errors in any object detection dataset via Cleanlab Object Detection, which can utilize any existing object detection model you’ve trained.
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[Research] Detecting Errors in Numerical Data via any Regression Model
If you'd like to learn more, you can check out the blogpost, research paper, code, and tutorial to run this on your data.
- Detecting Errors in Numerical Data via Any Regression Model
- cleanlab v2.5 now supports all major ML tasks (adds regression, object detection, and image segmentation)
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Automated Data Quality at Scale
Sharing some context here: in grad school, I spent months writing custom data analysis code and training ML models to find errors in large-scale datasets like ImageNet, work that eventually resulted in this paper (https://arxiv.org/abs/2103.14749) and demo (https://labelerrors.com/).
Since then, I’ve been interested in building tools to automate this sort of analysis. We’ve finally gotten to the point where a web app can do automatically in a couple of hours what I spent months doing in Jupyter notebooks back in 2019—2020. It was really neat to see the software we built automatically produce the same figures and tables that are in our papers.
The blog post shared here is results-focused, talking about some of the data and dataset-level issues that a tool using data-centric AI algorithms can automatically find in ImageNet, which we used as a case study. Happy to answer any questions about the post or data-centric AI in general here!
P.S. all of our core algorithms are open-source, in case any of you are interested in checking out the code: https://github.com/cleanlab/cleanlab
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Enhancing Product Analytics and E-commerce Business
Cleanlab Studio offers a user-friendly interface that allows you to visualize and review the identified issues in your dataset. You can easily explore the detected errors and make corrections with confidence. It's a hassle-free solution that can save you valuable time and improve your overall e-commerce operations. If you'd like more details you can check this article out.
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Databricks users can now automatically correct data and improve ML models
I thought this community might find it very useful that Databricks has partnered with Cleanlab to bring automated data correction and ML model improvement for both structured and unstructured datasets to all Databricks users.
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[R] Automated Checks for Violations of Independent and Identically Distributed (IID) Assumption
I just published a paper detailing this non-IID check and open-sourced its code in the cleanlab package — just one line of code will check for this and many other types of issues in your dataset.
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A note from our sponsor - CodeRabbit
coderabbit.ai | 12 Feb 2025
Stats
cleanlab/cleanlab is an open source project licensed under GNU Affero General Public License v3.0 which is an OSI approved license.
The primary programming language of cleanlab is Python.