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

# ImageEmbedding

> Represents custom features derived from image embeddings for visual content analysis.

Represents custom features derived from image embeddings for visual content analysis.

ImageEmbedding extends VectorFeature to handle image-based embeddings, providing
clustering analysis specifically designed for visual content. This feature type
is used to monitor image models and detect visual drift in high-dimensional
embedding spaces.

The feature type is automatically set to CustomFeatureType.FROM\_IMAGE\_EMBEDDING
and applies clustering to image embeddings for visual pattern analysis.

## Examples

Creating an image embedding feature for product photos:

```python theme={null}
image_feature = ImageEmbedding(
    name="product_image_clusters",
    column="product_embedding",
    source_column="product_image_url",
    n_clusters=15
)
```

Creating a feature for medical image analysis:

```python theme={null}
medical_feature = ImageEmbedding(
    name="xray_pattern_clusters",
    column="xray_embedding",
    source_column="xray_image_path",
    n_clusters=10
)
```

## type

## source\_column

## model\_config

Configuration for the model, should be a dictionary conforming to \[ConfigDict]\[pydantic.config.ConfigDict].