Comments (3)
CleanLab works with any ML framework. What you need to provide on your end is holdout predicted probabilities and noisy labels. You can get holdout predicted probabilities using a cross validation, for example. You can use any framework to get those predicted probabilities. I show an example of for CIFAR (in my case, I used PyTorch): https://github.com/cgnorthcutt/cleanlab/tree/master/examples/cifar10
Read from the start until Step 4 (where you'll learn about finding the label errors and removing them from your dataset).
If what you are looking for is a product where you upload a dataset with noisy labels, and after a week or so, you get emailed a cleaned dataset -- I have startup that does this, but this requires manpower and costs $1000 to $50,000, depending on the dataset size, type, accuracy requirements, etc. You can email [email protected] if that is what you are looking for. While that makes sense for industry, most researchers dont have that kind of funding, which is why CleanLab is open-source :)
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We will provide tutorial on using cleanlab for semantic segmentation in the near future, stay tuned!
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What I mean is: I only need to cleansed dataset. I need to clean the dataset.I do not care about the framework like pytorch,tensorflow,maxnet .etc. Can Cleanlab be used directly for that purpose @cgnorthcutt
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Related Issues (20)
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