You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection
This work addresses object detection in computer vision by demonstrating that Transformers can be effective with few modifications, though it is incremental in adapting existing architectures.
The authors tackled the problem of performing 2D object detection using Transformers with minimal 2D spatial knowledge, resulting in YOLOS models that achieve competitive performance, such as 42.0 box AP on COCO val.
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.