# SPIN: Hierarchical Segmentation with Subpart Granularity in Natural Images

Source: [SPIN: Hierarchical Segmentation with Subpart Granularity in Natural Images](<https://www.sota2.com/research/paper/68fa746e1e209131cea61b5c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:09:38.833Z

Authors: Josh Myers-Dean, Jarek Reynolds, Brian Price, Yifei Fan, Danna Gurari

Published: 2024-07-12T21:08:00.000Z

## Abstract

Hierarchical segmentation entails creating segmentations at varying levels of granularity. We introduce the first hierarchical semantic segmentation dataset with subpart annotations for natural images, which we call SPIN (SubPartImageNet). We also introduce two novel evaluation metrics to evaluate how well algorithms capture spatial and semantic relationships across hierarchical levels. We benchmark modern models across three different tasks and analyze their strengths and weaknesses across objects, parts, and subparts. To facilitate community-wide progress, we publicly release our dataset at https://joshmyersdean.github.io/spin/index.html.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2407.09686>)

[PDF](<https://arxiv.org/pdf/2407.09686.pdf>)