This is the abstract for the ICSE 2022 artifact evaluation of paper icse22-main-1429, ``ReMoS: Reducing Defect Inheritance in Transfer Learning via Relevant Model Slicing''. In this abstraction, we will introduce the basic information of the submitted artifact, including the paper title, the purpose of the artifact, the badges to claim, and the technology skills that the reviewers are assumed to evaluate the artifact. \textbf{Paper title.} The corresponding paper title of this artifact is ``ReMoS: Reducing Defect Inheritance in Transfer Learning via Relevant Model Slicing''. The unique paper ID is icse22-main-1429. \textbf{Artifact purpose.} The purpose of the submitted artifact is to reproduce and validate the results in the paper icse22-main-1429. We have prepared the trained model and running logs for the experiments in the paper. The included reproducible results include Figure 5, Figure 6, Figure 7, Figure 8, and Table 2. \textbf{The badge to claim.} This artifact aims to claim the ``available'' and ``functional'' badges. \textbf{Required environments.} To review this artifact, the reviewers should have a server with Ubuntu 18.04. If the reviewer wants to train the models, the server should have at least one GPU and CUDA 11.4. \textbf{Required reviewer's technology skills.} To review this artifact, the reviewers should be familiar with Python, the popular deep learning frameworks, and PyTorch. The paper's results are validated by python scripts, thus the reviewers should understand the python