This example uses cleanlab's Datalab class to audit an image dataset. Here we run Datalab with a Swin Transformer model trained for classification.
There are two notebooks:
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train_image_classifier.ipynb - Trains a Swin Transformer classifier model on a subset of Caltech-256
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Install dependencies with:
pip install -r requirements-train.txt --extra-index-url https://download.pytorch.org/whl/cu116
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datalab.ipynb - Audits the dataset using Datalab applied to outputs from the trained model.
- Install dependencies with
pip install -r requirements-datalab.txt
- Install dependencies with
You can also audit your dataset for the same issues detected by Datalab (and more) without having to: write code, train your own machine learning model, or set up your own interface to the data/results. Cleanlab Studio does all this for you automatically. For image/text/tabular datasets, most users obtain better results with Cleanlab Studio vs. implementing their own solution (and achieve these results 100x faster).
