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Manufacturing & Agriculture

Cleanlab ensures reliable data/models in industrial quality control applications (and more generally across enterprise computer vision, natural language processing, and statistical data mining). Use Cleanlab Studio to seamlessly handle mislabeled data, outliers/anomalies, drift, ambiguous instances, and other real-world data issues.
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Case StudyAutomatically Correcting Image Labels

14%
error improvement for ResNet computer vision model (without any change in modeling code)

Quote from Travis Tang, Data Scientist at Gojek:

I used an open-sourced library, cleanlab, to remove low-quality labels on an image dataset. The [ResNet] model trained on the dataset without low-quality data gained 4 percentage points of accuracy compared to the baseline model (trained on all data).

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Gojek is an Indonesian on-demand multi-service platform and digital payment technology group based in Jakarta. Gojek was first established in Indonesia in 2009 as a call center to connect consumers to courier delivery and two-wheeled ride-hailing services.
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Case StudyCleanlab Finds Errors in ImageNet

Label errors automatically detected by Cleanlab in ImageNet, the most famous image recognition dataset.

Browse other labeling errors detected by Cleanlab in famous ML benchmark datasets: labelerrors.com
Graph showing results achieved with Cleanlab on a real dataset

HOW CLEANLAB HELPS IMPROVE YOUR OUTPUT

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Automatically identify and resolve data issues, and deploy robust ML models with a few clicks. Cleanlab Studio facilitates data-centric AI workflows in:

  • Agricultural applications: disease inspection, yield estimation, animal monitoring, as well as tasks involving grading and sorting.
  • Industrial quality control applications: ingredient inspection, process quality monitoring, assembly inspection, and defect detection.

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Video on using Cleanlab Studio to find and fix incorrect labels for image data

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Automatically detect outliers (anomalies) which may have an outsized impact on data-driven conclusions and should be handled with care.

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Automatically detect low-quality images including those which are under/over-exposed, blurry, near duplicates, low-information, etc. Learn more.

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Model images together with tabular (numeric, categorical) and text information.

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Know which subset of the data is high-quality with confidence, and evaluate the quality of different data sources.

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Effectively analyze data labeled by multiple annotators, and estimate which examples require additional review and which annotators are best/worst overall. Learn more.

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Use our ActiveLab system (active learning with relabeling) to efficiently collect new labels for training accurate models.

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Read more about why 2022 was the most exciting year in computer vision history and how Cleanlab fits into it.

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Read more about why the foundations of AI are riddled with errors.

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Related applications

Data Entry, Management, and Curation

AI expert review of your data stores to find errors or incorrect labels.

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Business Intelligence / Analytics

Correct data errors for more accurate analytics/modeling enabling better decisions.

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Data Annotation & Crowdsourcing

Label data efficiently and accurately, understand annotator quality.

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