Papers › You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection

You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection

1 Jun 2021NeurIPS 2021 12arXiv:2106.00666archive 2025-07-28

Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu

Can Transformer perform 2D object- and region-level recognition from a pure sequence-to-sequence perspective with minimal knowledge about the 2D spatial structure? To answer this question, we present You Only Look at One Sequence (YOLOS), a series of object detection models based on the vanilla Vision Transformer with the fewest possible modifications, region priors, as well as inductive biases of the target task. We find that YOLOS pre-trained on the mid-sized ImageNet-1k dataset only can already achieve quite competitive performance on the challenging COCO object detection benchmark, e.g., YOLOS-Base directly adopted from BERT-Base architecture can obtain 42.0 box AP on COCO val. We also discuss the impacts as well as limitations of current pre-train schemes and model scaling strategies for Transformer in vision through YOLOS. Code and pre-trained models are available at https://github.com/hustvl/YOLOS.

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convert_to_xywh hustvl/YOLOS/datasets/coco_eval.py official repository ran · honoured contract fingerprinted MIT (permissive) · f31a58bf6457ced5 · report
crop hustvl/YOLOS/datasets/transforms.py official repository ran MIT (permissive) · 74f3f5c1bab38c14 · report
drop_path hustvl/YOLOS/models/layers/drop.py official repository ran · fixture could not drive it MIT (permissive) · 39eace7e2822504f · report
evaluate hustvl/YOLOS/datasets/coco_eval.py official repository ran · our draft was wrong MIT (permissive) · fe0ddcc2d420c9a0 · report
hflip hustvl/YOLOS/datasets/transforms.py official repository ran MIT (permissive) · 9be9e658b5bd688a · report
drop_block_2d hustvl/YOLOS/models/layers/drop.py official repository unverified MIT (permissive) · 653d5a3aa2c473be · report
drop_block_fast_2d hustvl/YOLOS/models/layers/drop.py official repository unverified MIT (permissive) · 97aa7c942cfb7d3d · report
small hustvl/YOLOS/models/backbone.py official repository unverified MIT (permissive) · a375c5fcb767baea · report
small_dWr hustvl/YOLOS/models/backbone.py official repository unverified MIT (permissive) · 6d26e8d0c3a1056a · report
tiny hustvl/YOLOS/models/backbone.py official repository unverified MIT (permissive) · 79c3d06885711f57 · report

Tasks

ObjectObject Detectionobject-detection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Object Detection COCO-O YOLOS-B (ViT-B) Average mAP 20.0 #30 of 45 Archive leaderboard report
Object Detection COCO-O YOLOS-B (ViT-B) Effective Robustness 1.05 #30 of 45 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

Absolute Position EncodingsAdamAttentionAttention DropoutBERTBPEConvolutionDense ConnectionsDetrDropoutFeedforward NetworkLabel SmoothingLayer NormalizationLinear LayerLinear Warmup With Linear DecayMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformerVision TransformerWeight DecayWordPiece

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