This repository is the code for our paper CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention.


Existing vision transformers fail to build attention among objects/features of different scales (cross-scale attention), while such ability is very important to visual tasks. CrossFormer is a versatile vision transformer which solves this problem. Its core designs contain Cross-scale Embedding Layer (CEL), Long-Short Distance Attention (L/SDA), which work together to enable cross-scale attention.

CEL blends every input embedding with multiple-scale features. L/SDA split all embeddings into several groups, and the self-attention is only computed within each group (embeddings with the same color border belong to the same group.).

Further, we also propose a dynamic position bias (DPB) module, which makes the effective yet inflexible relative position bias apply to variable image size.

Now, experiments are done on four representative visual tasks, i.e., image classification, objection detection, and instance/semantic segmentation. Results show that CrossFormer outperforms existing vision transformers in these tasks, especially in dense prediction tasks (i.e., object detection and instance/semantic segmentation). We think it is because image classification only pays attention to one object and large-scale features, while dense prediction tasks rely more on cross-scale attention.


  1. Libraries (Python3.6-based)
pip3 install numpy scipy Pillow pyyaml torch==1.7.0 torchvision==0.8.1 timm==0.3.2
  1. Dataset: ImageNet

  2. Requirements for detection/instance segmentation and semantic segmentation are listed here: detection/ or segmentation/

Getting Started


## There should be two directories under the path_to_imagenet: train and validation

## CrossFormer-T
python -u -m torch.distributed.launch --nproc_per_node 8 --cfg configs/tiny_patch4_group7_224.yaml \
--batch-size 128 --data-path path_to_imagenet --output ./output

## CrossFormer-S
python -u -m torch.distributed.launch --nproc_per_node 8 --cfg configs/small_patch4_group7_224.yaml \
--batch-size 128 --data-path path_to_imagenet --output ./output

## CrossFormer-B
python -u -m torch.distributed.launch --nproc_per_node 8 --cfg configs/base_patch4_group7_224.yaml 
--batch-size 128 --data-path path_to_imagenet --output ./output

## CrossFormer-L
python -u -m torch.distributed.launch --nproc_per_node 8 --cfg configs/large_patch4_group7_224.yaml \
--batch-size 128 --data-path path_to_imagenet --output ./output


## Take CrossFormer-T as an example
python -u -m torch.distributed.launch --nproc_per_node 1 --cfg configs/tiny_patch4_group7_224.yaml \
--batch-size 128 --data-path path_to_imagenet --eval --resume path_to_crossformer-t.pth


Image Classification

Models trained on ImageNet-1K and evaluated on its validation set. The input image size is 224 x 224.

Architectures Params FLOPs Accuracy Models
ResNet-50 25.6M 4.1G 76.2% -
RegNetY-8G 39.0M 8.0G 81.7% -
CrossFormer-T 27.8M 2.9G 81.5% Google Drive/BaiduCloud, key: nkju
CrossFormer-S 30.7M 4.9G 82.5% Google Drive/BaiduCloud, key: fgqj
CrossFormer-B 52.0M 9.2G 83.4% Google Drive/BaiduCloud, key: 7md9
CrossFormer-L 92.0M 16.1G 84.0% TBD

More results compared with other vision transformers can be seen in the paper.

Objection Detection & Instance Segmentation

Models trained on COCO 2017. Backbones are initialized with weights pre-trained on ImageNet-1K.

Backbone Detection Head Learning Schedule Params FLOPs box AP mask AP
ResNet-101 RetinaNet 1x 56.7M 315.0G 38.5 -
CrossFormer-S RetinaNet 1x 40.8M 282.0G 44.4 -
CrossFormer-B RetinaNet 1x 62.1M 389.0G 46.2 -
ResNet-101 Mask-RCNN 1x 63.2M 336.0G 40.4 36.4
CrossFormer-S Mask-RCNN 1x 50.2M 301.0G 45.4 41.4
CrossFormer-B Mask-RCNN 1x 71.5M 407.9G 47.2 42.7

More results and pretrained models for objection detection: detection/

Semantic Segmentation

Models trained on ADE20K. Backbones are initialized with weights pre-trained on ImageNet-1K.

Backbone Segmentation Head Iterations Params FLOPs IOU MS IOU
CrossFormer-S FPN 80K 34.3M 209.8G 46.4 -
CrossFormer-B FPN 80K 55.6M 320.1G 48.0 -
CrossFormer-L FPN 80K 95.4M 482.7G 49.1 -
ResNet-101 UPerNet 160K 86.0M 1029.G 44.9 -
CrossFormer-S UPerNet 160K 62.3M 979.5G 47.6 48.4
CrossFormer-B UPerNet 160K 83.6M 1089.7G 49.7 50.6
CrossFormer-L UPerNet 160K 125.5M 1257.8G 50.4 51.4

MS IOU means IOU with multi-scale testing.