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Tables gallery

The following sections highlight some of the ways you can use tables:

View your data​

Log metrics and rich media during model training or evaluation, then visualize results in a persistent database synced to the cloud, or to your hosting instance.

Browse examples and verify the counts and distribution of your data

For example, check out this table that shows a balanced split of a photos dataset.

Interactively explore your data​

View, sort, filter, group, join, and query tables to understand your data and model performance—no need to browse static files or rerun analysis scripts.

Listen to original songs and their synthesized versions (with timbre transfer)

For example, see this report on style-transferred audio.

Compare model versions​

Quickly compare results across different training epochs, datasets, hyperparameter choices, model architectures etc.

See granular differences: the left model detects some red sidewalk, the right does not.

For example, see this table that compares two models on the same test images.

Track every detail and see the bigger picture​

Zoom in to visualize a specific prediction at a specific step. Zoom out to see the aggregate statistics, identify patterns of errors, and understand opportunities for improvement. This tool works for comparing steps from a single model training, or results across different model versions.

For example, see this example table that analyzes results after one and then after five epochs on the MNIST dataset.

Example Projects with W&B Tables​

The following highlight some real W&B Projects that use W&B Tables.

Image classification​

Read this report, follow this colab, or explore this artifacts context to see how a CNN identifies ten types of living things (plants, bird, insects, etc) from iNaturalist photos.

Compare the distribution of true labels across two different models' predictions.

Audio​

Interact with audio tables in this report on timbre transfer. You can compare a recorded whale song with a synthesized rendition of the same melody on an instrument like violin or trumpet. You can also record your own songs and explore their synthesized versions in W&B with this colab.

Text​

Browse text samples from training data or generated output, dynamically group by relevant fields, and align your evaluation across model variants or experiment settings. Render text as Markdown or use visual diff mode to compare texts. Explore a simple character-based RNN for generating Shakespeare in this report.

Doubling the size of the hidden layer yields some more creative prompt completions.

Video​

Browse and aggregate over videos logged during training to understand your models. Here is an early example using the SafeLife benchmark for RL agents seeking to minimize side effects

Browse easily through the few successful agents

Tabular data​

View a report on how to split and preprocess tabular data with version control and deduplication.

Tables and Artifacts work together to version control, label, and deduplicate your dataset iterations

Comparing model variants (semantic segmentation)​

An interactive notebook and live example of logging Tables for semantic segmentation and comparing different models. Try your own queries in this Table.

Find the best predictions across two models on the same test set

Analyzing improvement over training time​

A detailed report on how to visualize predictions over time and the accompanying interactive notebook.

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