Use W&B Tables to log, query, and analyze tabular data. Understand your datasets, visualize model predictions, and share insights in a central dashboard.
Compare changes precisely across models, epochs, or individual examples
Understand higher-level patterns in your data
Capture and communicate your insights with visual samples
A W&B Table (
wandb.Table) is a two dimensional grid of data where each column has a single type of data—think of this as a more powerful DataFrame. Tables support primitive and numeric types, as well as nested lists, dictionaries, and rich media types. Log a Table to W&B, then query, compare, and analyze results in the UI.
Tables are great for storing, understanding, and sharing any form of data critical to your ML workflow—from datasets to model predictions and everything in between.
Log metrics and rich media during model training or evaluation, then visualize results in a persistent database synced to the cloud, or to your self-hosted instance. For example, check out this balanced split of a photos dataset →
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. For example, see this project on style-transfered audio →
Quickly compare results across different training epochs, datasets, hyperparameter choices, model architectures etc. For example, take a look at this comparison of two models on the same test images →
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. Check out this example table analyzing results after 1 vs 5 epochs on MNIST →
Interact with audio Tables in this report on timbre transfer. In this live example, you can compare a recorded whale song with a synthesized rendition of the same melody on an instrument like violin or trumpet.
Browse text samples from training data or generated output, dynamically group by relevant fields, and align your evaluation across model variants or experiment settings. Explore a simple character-based RNN for generating Shakespeare in this report →