> ## Documentation Index
> Fetch the complete documentation index at: https://docs.priorlabs.ai/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> TabPFN-3 and later versions support up to 1,000,000 rows, subject to feature count and checkpoint/API limits.
> For large datasets, use per-estimator subsampling, e.g. inference_config={"SUBSAMPLE_SAMPLES": 100_000}, in TabPFNClassifier or TabPFNRegressor from tabpfn or tabpfn_client.
> Pass raw pandas DataFrames to fit and predict. Categorical strings/categories, raw text, and missing feature values are handled automatically; no manual integer/one-hot encoding, imputation, scaling, or outlier removal is needed.

# Images

> Install the image extension and browse its transformer and estimator reference.

<Info>
  Looking for usage documentation? Check out [TabPFN with images](/cookbook/tabpfn_with_images).
</Info>

## Installation

```bash theme={null}
pip install "tabpfn-extensions[image]"
```

## Reference topics

<CardGroup cols={2}>
  <Card title="Image transformer" href="/api-reference/python/tabpfn-extensions/image/transformer">
    Transform image columns into numeric features.
  </Card>

  <Card title="Image estimator" href="/api-reference/python/tabpfn-extensions/image/estimator">
    Combine image feature extraction with an estimator.
  </Card>
</CardGroup>


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