Papers › An Empirical Study of Training Self-Supervised Vision Transformers

An Empirical Study of Training Self-Supervised Vision Transformers

5 Apr 2021ICCV 2021 10arXiv:2104.02057archive 2025-07-28

Xinlei Chen, Saining Xie, Kaiming He

This paper does not describe a novel method. Instead, it studies a straightforward, incremental, yet must-know baseline given the recent progress in computer vision: self-supervised learning for Vision Transformers (ViT). While the training recipes for standard convolutional networks have been highly mature and robust, the recipes for ViT are yet to be built, especially in the self-supervised scenarios where training becomes more challenging. In this work, we go back to basics and investigate the effects of several fundamental components for training self-supervised ViT. We observe that instability is a major issue that degrades accuracy, and it can be hidden by apparently good results. We reveal that these results are indeed partial failure, and they can be improved when training is made more stable. We benchmark ViT results in MoCo v3 and several other self-supervised frameworks, with ablations in various aspects. We discuss the currently positive evidence as well as challenges and open questions. We hope that this work will provide useful data points and experience for future research.

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facebookresearch/moco-v3 officialmentioned in papermentioned on GitHubpytorchNOASSERTION report
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Tasks

Out-of-Distribution GeneralizationSelf-Supervised Image ClassificationSelf-Supervised Learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Self-Supervised Image Classification ImageNet MoCo v3 (ViT-BN-L/7) Number of Params 304M #18 of 144 Archive leaderboard report
Self-Supervised Image Classification ImageNet MoCo v3 (ViT-BN-L/7) Top 1 Accuracy 81.0% #18 of 144 Archive leaderboard report
Self-Supervised Image Classification ImageNet MoCo v3 (ViT-BN-H) Number of Params 700M #37 of 144 Archive leaderboard report
Self-Supervised Image Classification ImageNet MoCo v3 (ViT-BN-H) Top 1 Accuracy 79.1% #37 of 144 Archive leaderboard report
Self-Supervised Image Classification ImageNet MoCo v3 (ViT-H) Number of Params 632M #47 of 144 Archive leaderboard report
Self-Supervised Image Classification ImageNet MoCo v3 (ViT-H) Top 1 Accuracy 78.1% #47 of 144 Archive leaderboard report
Self-Supervised Image Classification ImageNet MoCo v3 (ViT-L) Number of Params 307M #51 of 144 Archive leaderboard report
Self-Supervised Image Classification ImageNet MoCo v3 (ViT-L) Top 1 Accuracy 77.6% #51 of 144 Archive leaderboard report
Self-Supervised Image Classification ImageNet MoCo v3 (ViT-B/16) Number of Params 86M #58 of 144 Archive leaderboard report
Self-Supervised Image Classification ImageNet MoCo v3 (ViT-B/16) Top 1 Accuracy 76.7% #58 of 144 Archive leaderboard report
Self-Supervised Image Classification ImageNet (finetuned) MoCo v3 (ViT-L/16) Number of Params 304M #35 of 65 Archive leaderboard report
Self-Supervised Image Classification ImageNet (finetuned) MoCo v3 (ViT-L/16) Top 1 Accuracy 84.1% #35 of 65 Archive leaderboard report
Self-Supervised Image Classification ImageNet (finetuned) MoCo v3 (ViT-B/16) Number of Params 86M #44 of 65 Archive leaderboard report
Self-Supervised Image Classification ImageNet (finetuned) MoCo v3 (ViT-B/16) Top 1 Accuracy 83.2% #44 of 65 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: MoCo v3

Batch NormalizationInfoNCEMoCoMoCo v3

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