# Sample Visualization ## Intro and shared properties of the visualizers In this page we present examples for the different visualizers, and how they are viewed in the platform. To better understand how to add these visualization via your [code integration](https://docs.tensorleap.ai/tensorleap-integration/writing-integration-code), refer to the [visualizer functions](https://docs.tensorleap.ai/tensorleap-integration/writing-integration-code/visualizer-function). ### The top bar of visualizers: Identifying a sample and its properties

The image visualizer for the MNIST dataset. Hovering over a specific sample allows to zoom into the image (see top left magnifying glasses over sample). The top of each sample shows its sample ID and abbreviation of its respective data subset (train, val, test).

All visualizers in the sample review window share a common top bar that shows the following: * Sample ID, together with an abbreviation of the subset it belongs to (train, validation, test) * On Hover - a magnifying glass action that allows to zoom in and out * The selected [Metadata tags](https://docs.tensorleap.ai/user-interface/project/dashboards/dashlets/population-exploration/..#metadata-tags) that were added ## The different visualization types in the Dashboard ### Heatmaps Visualizer

Each visualized input get a second, attention overlayed visualizer. This overlays the shared features of a concept that is clustered within the platform over the image. It answers the question: "What makes these samples part of the same group?"

Each Input that we connect to a visualizer [using the mapping](https://docs.tensorleap.ai/user-interface/project/network/network-mapping/create-a-mapping-deprecated) would automatically be accompanied by a corresponding second visualizer in the sample review panel. This visualizer is a *heatmap visualization* that overlays the shared features of a concept that is clustered within the platform over the image. It answers the question: "What makes these samples part of the same group?". Controlling the color saturation and opacity of the heatmaps is possible via the top controller of the heatmap visualizer. Each input visualizer named "x" would result in a second ### Metadata Visualizer

The metadata visualizer. In the top controller the option to keep only selected columns, reset selection, or export the data into .csv are presented by the Columns, Reset, and Export button respectively.

The metadata visualizer is a defaultive visualizer that shows [the metadata](https://docs.tensorleap.ai/tensorleap-integration/writing-integration-code/metadata-function) for all selected samples. The top controller of this visualizer allows to control and export the current table: * The Columns button - Allows column selection and filtering for the table, narrowing the metadata to a relevant subset of the properties. * The Reset button - Allows to reset previous column filtering to show all. * The Export button - Exports the current table into a .csv ### Image Visualizer

The image visualizer for the MNIST dataset. Hovering over a specific sample allows to zoom into the image (see top left magnifying glasses over sample)

The [image visualizer](https://docs.tensorleap.ai/tensorleap-integration/python-api/code_loader/visualizer_classes) supports a visualization of images within the platform. Hovering over an image shows the RGB values. It also presents an option to zoom in or out, by clicking the magnifying glass over the image. ### Semantic Segmentation Visualizer

Semantic Segmentation visualizer. Allows a dynamic selection of which labels to plot in the mask, and a control over the opacity

This visualizer visualizes the semantic segmentation of an image. Different properties of these settings could be controlled: * Choosing which label would be shown in the mask * Controlling what is the mask opacity ### Object Detection Visualizer

Presenting the Object Detection visualizer. This visualizer allows you to visualize bounding boxes (BBs) over an image. It allows a dynamic selection of what BBs are shown by confidence or label. It also allow to view any metadata that is attached to the bounding box.

This visualizer visualizes an image with bounding boxes. Different properties of these settings could be controlled: * Choosing which bounding box labels would be presented * Showcasing all [bounding box metadata](https://docs.tensorleap.ai/tensorleap-integration/python-api/code_loader/visualizer_classes/leapimagewithbbox) or hiding it * Controlling what are the minimal and maximal confidence of the bounding boxes that are presented. ### Horizontal Bar Visualizer

The horizontal bar visualizer. Used to visualize logits and probabilities

The horizontal bar is used to visualize logits and probabilities. ### Graph Visualizer

The Graph visualizer. jsed to visualize time-series

The Graph visualizer is used to visualize time series. ### Text Visualizer

The Text visualizer

The Text visualizer is used to visualize raw text. ### Text Mask Visualizer

The text mask visualizer. Used to visualize token classification tasks (NER, etc.)

The Text mask visualizer is used to visualize a TextMask, for token classification tasks such as NER.