Cleaner Image Data. Smarter Models.
A toolkit for preparing large, computer vision image datasets faster, more accurately, and with less manual effort
Next Gen Image Dataset Tools
"I think the most important shift the AI world needs to go through this decade will be a shift to data centric AI."
2D Pixel Coordinated Analysis
The ONLY data quality tools for image datasets with actionable cleanliness insights based on the 2D information content of each image
Data Quality Tools For Images
The FIRST data quality platform to automate cleaning and optimizing of image datasets for preparation of computer vision model training
Data Scientists & Machine Learning Engineers SAVE:
Time
Money
Projects
ML Needs Image Data Quality Tools
Image datasets are too big for human review
So computer vision datasets are DIRTY
Causing huge time waste for data scientists
And producing WORSE model outcomes
Problem
Image datasets are:
Frequently massive - too big for thorough human review
Full of labeling errors, duplicates, and irrelevant data
Data scientists spend too much time cleaning instead of building models
Noisy data degrades accuracy, skews training and reduces reproducibility
Solution
Intelligent dataset preparation and cleaning toolkit engineered for large-scale image datasets that:
Reduces data cleaning time by 60–80%
Improves labeling accuracy and dataset quality
Reduces manual review
Optimizes dataset structure
Saves time & budget
Accelerates model development and deployment
"Improving the data is not a ‘preprocessing’ step that you do once. It’s part of the iterative process of model development."
Common Issues:
Are you solving overfitting problems by adding more data?
You're spending 60% of your time cleaning data sets?
Frustrated there are no analytic tools such as k-mean for images?
Think entropy is just for decision trees?
Need to speed up your data preparation timeline?
Struggling to deliver high performing models?
Frequent Tasks:
Label accuracy
Content accuracy
Label consistency
Format consistency
Missing data
Label coverage
Diversity and variance
De-duplicate
Outlier review
Popular Use Cases:
Clean before and during model training
Reduce noise early to boost training efficiency and accuracy
Optimize dataset structure for model stability
Optimize class distribution to prevent under/overfitting
Ensure consistent quality across different data sources
Audit & understand legacy datasets
Reveal structure and quality issues hidden in datasets
Let's Build Data-Centric AI
Watch Andrew Ng Discuss His Vision for a Data-Centric AI Future
Highlights
Watch Now
"Data-centric AI is the practice of systematically engineering the data used to build AI systems."
Private Beta Launch in 2026
We’re preparing to launch Data Wash, a platform for high-throughput image dataset optimization through 2D image analysis and structural cleanup.
Before public release, we’re selecting a very limited number of early customer partners to onboard with reduced pricing during our beta phase.
If your team works with image datasets ≥100k samples, it could be a strong fit. If you'd like to be considered, connect with us now before our applicant list is closed.
The provided information does not constitute an offer or invitation to make offers or invitation to buy, sell or otherwise use any services, products and/or resources referred to on this website, and may be changed at any time. Contact us for more information.
Data Wash is transforming how image data is prepared and processed for deep learning models. We make massive image datasets move fast. And help data engineers & scientists be the project hero.
Don't be left in the dirt! Turn your bottleneck into a competitive advantage.
ABOUT DATA WASH
We're on a mission to elevate data scientists & engineers, to help them spend more time innovating & creating and less time cleaning.
We make image dataset preparation and cleaning fast, predictable and scalable, so teams can accelerate their ML breakthroughs.
Join us for a data centric approach to building smarter AI models.
Built by scientists, for scientists.
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