Data Visualization
Iterate on datasets and understand model predictions
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
The fastest way to try Tables is to log a dataframe and see the Table UI.
wandb.log({"table": my_dataframe})
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 →
Browse actual examples and verify the counts & distribution of 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. For example, see this project on style-transfered audio →
Listen to original songs and their synthesized versions (with timbre transfer)
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 →
See granular differences: the left model detects some red sidewalk, the right does not.
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 →
Read this report, follow this colab, or explore this artifacts context for a CNN identifying 10 types of living things (plants, bird, insects, etc) from iNaturalist photos.
Compare the distribution of true labels across two different models' predictions.
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 via this colab →
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 →
Doubling the size of the hidden layer yields some more creative prompt completions.
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
Tables and Artifacts work together to version control, label, and deduplicate your dataset iterations
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
Last modified 1mo ago