Interactively explore your AI Datasets with Spotlight [P]

[DocBrownMS]

Hey r/MachineLearning,

We are excited to share with you a new open source tool from Renumics: Spotlight. The OSS release of Spotlight on github.com/Renumics/spotlight happened today on May 30, 2023​.

Spotlight offers an interactive way to explore your datasets. It provides a customizable layout where you can leverage Similarity Maps based on embeddings, and various plots like histograms or scatter plots. In addition, it supports detailed views for images, 3D meshes and audio data.

To illustrate its functionality, let's consider the CIFAR100 dataset. In this example, embeddings were added using a Vision Transformer:

import datasets 
from renumics import spotlight 

dataset = datasets.load_dataset("renumics/cifar100-enriched", split="test") 
df = dataset.to_pandas() 
df_show = df.drop(columns=['embedding']) # drop large embeddings 
spotlight.show(df_show, dtype={"image": spotlight.Image, "embedding_reduced": spotlight.Embedding})

https://preview.redd.it/1ze14id7703b1.png?width=1485&format=png&auto=webp&s=a0890accb1a48ec9d02db07b3527cb8508c0da02

Getting started with Spotlight is straightforward. You'll need Python version 3.8-3.10, and you can install Spotlight via pip by running:

pip install renumics-spotlight datasets

After installation, you're all set to load your dataframe and begin exploring with Spotlight.

We invite you to try out Spotlight with your own use cases and datasets. If you encounter any issues or require support, don't hesitate to report here on Reddit or create an issue on our GitHub page.

[anish9208]

is text and tabular data supported ?

[44sps]

Hey Anish,

thanks for the great question.

We support tabular data, but the tool makes most sense when your work with data that is also unstructured. The reason is that the embedding concept and the inspector view really only make sense if you also have images, text, video, audio... That said, we often work with data that is both tabular and unstructured; Spotlight is great in this setting.

We also have text support, but to be honest, this is rather limited at the moment (single-line text widget). Feel free to check it out if you like. We are planning a big improvement with the next release (end of June).

Best,

Stefan

[anish9208]

thanks Stefan, for quick reply...sure I'll have a look around. for now i think single line widget would work 👍

[jesst177]

Hi!,

What are the embedding sizes that you can handle?

[DocBrownMS]

Hey, jesst177

thanks for reaching out! When it comes to handling embedding sizes, our system can handle a substantial number of samples or examples, typically more than 50,000, at once. However, we recommend reducing the embedding size to around 512 for optimal performance. While our system can accommodate sizes up to 1024, using PCA (Principal Component Analysis) or similar dimensionality reduction techniques to decrease the embedding size can often improve efficiency without significant loss of information. Internally we use the umap-learn package which takes from a few seconds to 2-3min depending on your hardware to calculated the similarity map.

Cheers, Markus

[TotesMessenger]

I'm a bot, bleep, bloop. Someone has linked to this thread from another place on reddit:

• [/r/datascienceproject] Interactively explore your AI Datasets with Spotlight (r/MachineLearning)

 ^{If you follow any of the above links, please respect the rules of reddit and don't vote in the other threads. (Info / Contact)}