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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데이터 과학 및 ML

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

소개

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.

주요 기능

Interactive 2D visualization within the FiftyOne App Embeddings panel
Semantic and visual similarity sorting for cluster analysis
Dimensionality reduction via UMAP, t-SNE, or PCA
Automated similarity index computation using CLIP, DINOv2, or ResNet
3 GitHub stars
Outlier detection through integrated uniqueness scoring

사용 사례

Identifying mislabeled samples and class overlaps in computer vision datasets
Detecting visual outliers and anomalies for high-quality dataset curation
Analyzing model prediction errors by comparing ground truth vs. predicted clusters

소개

주요 기능

Interactive 2D visualization within the FiftyOne App Embeddings panel
Semantic and visual similarity sorting for cluster analysis
Dimensionality reduction via UMAP, t-SNE, or PCA
Automated similarity index computation using CLIP, DINOv2, or ResNet
3 GitHub stars
Outlier detection through integrated uniqueness scoring

사용 사례

Identifying mislabeled samples and class overlaps in computer vision datasets
Detecting visual outliers and anomalies for high-quality dataset curation
Analyzing model prediction errors by comparing ground truth vs. predicted clusters