Improving Datasets and Debugging Machine Learning Models with Scale Nucleus, Rebuilt from the Ground Up
It’s been an exciting journey since we shipped Scale Nucleus for the first
time
just over a year ago. We’ve helped over 100 different organizations explore, curate and improve
their datasets and debug their machine learning (ML) models, across semantic
segmentation, object detection, and even 3D point clouds. Today, we’re excited
to share an all-new Nucleus, with powerful new features and a redesigned user
interface that make it easier than ever to create better models with better
datasets.
To improve ML models requires first understanding where they fail. Nucleus
helps you go beyond aggregate error metrics, and instead quickly discover
specific patterns of failure across your model predictions and dataset labels.
Once you’ve identified problems, Nucleus makes them easy to fix. Address bad
labels through efficient, model-assisted data quality assurance (QA) and
one-click labeling integration, or improve model predictions through targeted
data curation of what to label next.
Scale Nucleus is one of the most
intuitive and efficient ways to select the right data for labeling: random
sampling shouldn’t be the way you select data to label. Training
better-performing models requires a focus on edge cases, false positives, and
the portion of minority classes or edge cases that are relevant to the
modeling problem you’re trying to solve. And your curation process shouldn’t
be overly time-intensive or manual.
As we launched Nucleus, we
explained that
better ML starts with understanding your data in depth. To improve production
ML, you need to understand your models’ qualitative failure modes, fix them by
gathering the right data, and curate diverse scenarios.
From Similarity Search to Autotag:
When Scale customers look to improve ML accuracy, they find that their models
often struggle with minority classes and edge cases, what we refer to as “the
long tail.” It might be a QA team that highlights challenges with a
self-driving vehicle, like the relatively rare combination of entering a
tunnel at nighttime behind a truck. Although this cross-section accounts for
only a small fraction of driving scenarios, their computer vision models must
be capable of handling them just as well.
In the below example, we’ve identified uncertain samples, and using a
similarity search based on internal models and feature vectors, surfaced
similar portions of the dataset for labeling or QA. This process culminates in
our Autotag feature:
Autotag is an incredibly efficient way to tag similar objects or scenes with
a new class. Simply select a few similar images of the class you are trying
to classify or create a tag for, click ‘Autotag,’ and then further refine
your set of images with positive and negative examples. Scale Nucleus will
then create an Autotag, an internal classification attached to the subset of
your dataset that Scale identified as similar. You can then retrieve objects
in your dataset based on this classification, even if labels for it don’t
exist in your ground truth.
The same useful core, with refined usability:
Nucleus focuses on several main use cases that we’ve developed in
conjunction with our customers:
Understand the strengths and weaknesses in your dataset as you identify
ways to improve quality:
Using the query bar at top or powerful search options at left , you can
discover edge cases, debug model failures, and efficiently QA any part or
your dataset.
Analyze the long tail of your dataset, with collaborative label edits,
and granular insights like Intersection over Union (IoU):
Object view helps you assess all classes, labels, and bounding boxes in your
dataset and make quick tweaks to a label, approve/reject the object as
well-labeled, or send the object to be labeled.
Examine metrics, failure cases, and confusion matrices, all linked to
your underlying training data.
The Insights tab provides you with interactive class distributions,
correlations, and confusion matrices, from which you can access the
underlying data for each category in one click.
Scale Nucleus was built with the explicit goal of helping ML teams improve
their datasets to extract better and better performance out of models
they’re training on their data. Whether you’re starting out with an
industry-standard dataset, and then execute transfer learning with the
addition of proprietary data, or training a model entirely on your own data,
Nucleus helps you train your model on the data that matters.
Privacy Mode: using Nucleus without sharing sensitive data
Several prospective customers asked if they could use Scale Nucleus without
uploading their training datasets to our cloud. Accordingly, we created
Privacy Mode in conjunction with our existing API, letting you use Nucleus
to curate your dataset without sensitive raw data ever leaving your servers
With Privacy Mode, you can submit URLs to Nucleus that link to raw data
assets like images or point clouds, instead of transferring that data to
Scale. These URLs may optionally be protected behind your corporate VPN or
an IP whitelist. When you load a Nucleus web page, your browser will
directly fetch the raw data from your servers without it ever being
accessible to Scale. Privacy Mode even works well with similarity search and
Autotag, as users can
create custom model embedding indexes
for their datasets.
What customers are saying about Nucleus:
“KeepTruckin encounters all manners of surprising edge cases in real world
data collection, so when it comes to knowing we’re labeling the most
valuable subset of our collected data, we turn to Scale Nucleus. Its
intuitive visualizations, query engine and Autotag help our teams improve
both data quality and models, all in the same motion.”
—Ali Rehan, Engineering Manager AI/Vision Products, KeepTruckin
Nucleus makes it easy to query data samples based on their metadata, or
simply bucket images based on “similarity,” specifically along feature
vectors of a Scale-trained model. For example, if you know your model should
be detecting police vehicles and it’s not doing so already, you can quickly
query for a handful of examples, find similar images, and through simple
positive and negative feedback to Nucleus, identify a subset of your dataset
to send out for labeling.
One year in, Scale Nucleus has already come a long way towards building a
new generation of ML tooling, but we’re just getting started. If you’d like
to join us in this journey and try Scale Nucleus, sign up
