No model has ever beaten 91% accuracy on ImageNet! Because this dataset has over 100,000 mislabeled images. Folks at MIT have released a tool that automatically detects such issues to help you clean any dataset 🔥 Introducing Cleanlab 🚀 A confident learning based Python library that can clean your data in a single line of code 🔥 It's like a magic wand 🪄 With one line of python code you can: - flag errors in data - detect out of distribution samples - train a robust model - perform active-learning - analyse multi-annotator data Cleanlab can be used for any data ( text, image, tabular, audio, etc. ) Also, many ML tasks ( classification, tagging, entity recognition & more ... ) Notebooks to start right away!🔥👇 🔗 → docs.cleanlab.ai If you are wondering how it works 💭 Below, I have provided a simple explanation of the Confident Learning algorithm used by Cleanlab. @CleanlabAI is FREE & open-source!! ✅ Make sure you ⭐️ the Cleanlab repo to support their work! 👇 github.com/cleanlab/cleanlab A No-code studio to do all what we just saw 👇 cleanlab.ai/ That's a wrap, thanks for reading! Find me → @akshay_pachaar ✔️ For more content like this! ✅ Cheers!