Transformer in computer vision has recently shown encouraging progress . In this work, we improve the original Pyramid Vision Transformer (  PVTv1) by adding three improvement designs, which include (1) overlapping patch embedding, (2) convolutional feed-forward networks, and (3) linear complexity attention layers. With these simple modifications, our PVTv2 significantly improves PVTv1 on  classification, detection, and segmentation. Moreover, PVTv2 achieves better performance than recent works , including Swin Transformer. We hope this work will make state-of-the-art vision Transformer research more accessible . Code is available at URL .
Transformer recently has shown encouraging progresses in computer vision . In this work, we present new baselines by improving the original Pyramid Vision Transformer ( abbreviated as PVTv1) by adding three designs, including (1) overlapping patch embedding, (2) convolutional feed-forward networks, and (3) linear complexity attention layers. With these modifications, our PVTv2 significantly improves PVTv1 on three tasks e.g., classification, detection, and segmentation. Moreover, PVTv2 achieves comparable or better performances than recent works such as Swin Transformer. We hope this work will facilitate state-of-the-art Transformer researches in computer vision . Code is available at URL .
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