What is the FiftyOne Embeddings Visualization skill?

It is a specialized capability for Claude Code that automates the generation of 2D representations of image or video datasets, making it easier to see how data is distributed.

How does it help with dataset cleaning?

By visualizing embeddings, you can find samples that are far from their class clusters (outliers) or areas where different classes overlap, indicating potential labeling errors.

Can I use specific models for embedding generation?

Yes, it supports various models from the FiftyOne Model Zoo, including CLIP-ViT, DINOv2, ResNet, and MobileNet, to generate the underlying features.

Which dimensionality reduction methods are supported?

The skill supports UMAP (recommended for speed and structure preservation), t-SNE (excellent for local structure), and PCA (fast linear reduction).

Does this skill require the FiftyOne App?

Yes, the skill is designed to work alongside the FiftyOne App, automatically launching the interface so you can interact with the resulting scatter plots.

FiftyOne Embeddings Visualization

Name: FiftyOne Embeddings Visualization
Author: voxel51

byvoxel51

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Data Science & ML

Visualizes computer vision datasets in 2D using dimensionality reduction to explore data structure, clusters, and outliers.

This skill provides a specialized workflow for exploring computer vision datasets within the FiftyOne environment by projecting high-dimensional embeddings into a navigable 2D space. It leverages powerful dimensionality reduction methods like UMAP and t-SNE, allowing users to visually identify clusters, detect anomalies, and understand class distributions. By automating the setup of similarity indices and visualization operators, it streamlines the process of dataset auditing and model error analysis, helping developers find mislabeled data and edge cases through intuitive interactive scatter plot interfaces.

Key Features

01Interactive 2D visualization within the FiftyOne App Embeddings panel

02Semantic and visual similarity sorting for cluster analysis

03Dimensionality reduction via UMAP, t-SNE, or PCA

04Automated similarity index computation using CLIP, DINOv2, or ResNet

053 GitHub stars

06Outlier detection through integrated uniqueness scoring

Use Cases

01Identifying mislabeled samples and class overlaps in computer vision datasets

02Detecting visual outliers and anomalies for high-quality dataset curation

03Analyzing model prediction errors by comparing ground truth vs. predicted clusters

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add voxel51/fiftyone-skills fiftyone-embeddings-visualization

For use in Claude.ai and ChatGPT

Download Skill