> ## Documentation Index
> Fetch the complete documentation index at: https://visual-layer-mintlify-b738e5e2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Visualizing Data

> In this tutorial, you will learn how to utilize Fastdup's versatile galleries to facilitate visual data analysis and refinement.

# Overview

For visualizing the results or individual parts of the analysis, fastdup generates galleries in the form of `HTML` files that are saved to the `galleries` sub-dir of the work directory and presented interactively when using Jupyter notebooks.

Starting from V1.0 galleries have a new layer of abstraction that automatically adds bounding boxes and labels to images where available.

## Supported galleries

### Components: `Fastdup.vis.component_gallery`

![](https://files.readme.io/cbfb28a-Screen_Shot_2023-02-27_at_12.26.40.png)

### Duplicates: `Fastdup.vis.duplicates_gallery`

![](https://i.imgur.com/90VfH50.png)

### Outliers: `Fastdup.vis.outliers_gallery`

![](https://files.readme.io/73cd5f8-Screen_Shot_2023-02-27_at_12.28.45.png)

### Image statistics: `Fastdup.vis.stats_gallery`

![](https://files.readme.io/fa9313e-Screen_Shot_2023-02-27_at_12.48.11.png)

### Similarity: `Fastdup.vis.similarity_gallery`

![](https://i.imgur.com/wOQ2Gvv.png)

#### For more detail, see [Galleries API Reference](/fastdup_docs_old/v1-api-engine-temp#fastdupfastdup_galleries)

## Gallery configuration

Galleries share a few methods and arguments used for visualizing labels and bounding boxes, and for setting general attributes:

* `slice`: Visualize a subset of the data with the given label, e.g., `slice='dog'`

* `sort_by`:  Sort images by a property, supported are:
  * default: `comp_size` - Number of images in the component
  * `distance` - The average distance between cluster members. Clusters where the images are most similar will be presented first
  * `area` - From the largest to the smallest image or bounding box average size

* `label_col`: Column to use as labels, common options are `label`, `split` and `img_filename`.

* `num_images`: (default=20) The number of images to visualize.

* `max_width`: (default=None) Pixel width of displayed gallery. Useful values are often in the 800-1200 range.

* `lazy_load`: (default=False) When `False`, images are embedded into the gallery `HTML` files. Otherwise images are loaded by the browser using their relative paths. Using `lazy_load` makes galleries lighter and faster to generate, but less portable and shareable. On the other hand, Without lazy loading galleries become very large files.

# Visualizing Images

For most cases, visualization is as simple as `fd.vis.component_gallery()`. The rest of the parameters are optional, and could be selected in hindsight.

## Adding Labels

The `label_col`  argument controls the labels appended to each image visualized. By default it fetches labels from the label column in the annotations dataframes provided during the `fastdup.run()` call. When labels are not provided, or if the use of another column is desired, the `label_col` argument could be set for using the required column.