# Model Doctor for Diagnosing and Treating Segmentation Error

Source: [Model Doctor for Diagnosing and Treating Segmentation Error](<https://www.sota2.com/research/paper/68fa6e951e209131cea40d58>)

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

Data updated: 2025-10-28T13:56:57.481Z

Authors: Zhijie Jia, Lin Chen, Kaiwen Hu, Lechao Cheng, Zunlei Feng, Mingli Song

Published: 2023-02-17T16:35:24.000Z

## Abstract

Despite the remarkable progress in semantic segmentation tasks with the advancement of deep neural networks, existing U-shaped hierarchical typical segmentation networks still suffer from local misclassification of categories and inaccurate target boundaries. In an effort to alleviate this issue, we propose a Model Doctor for semantic segmentation problems. The Model Doctor is designed to diagnose the aforementioned problems in existing pre-trained models and treat them without introducing additional data, with the goal of refining the parameters to achieve better performance. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our method. Code is available at \\url{https://github.com/zhijiejia/SegDoctor}.

## 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/2302.08980>)

[PDF](<https://arxiv.org/pdf/2302.08980.pdf>)