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CrossFormer Detection

Our detection code is developed on top of MMDetection v2.8.0.

For more details please refer to our paper CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention.

Prerequisites

  1. Libraries (Python3.6-based)
pip3 install mmcv-full==1.2.7 mmdet==2.8.0

  1. Prepare COCO 2017 dataset according to guidelines in MMDetection v2.8.0

  2. Prepare CrossFormer models pre-trained on ImageNet-1K

import torch
ckpt = torch.load("crossformer-s.pth") ## load classification checkpoint
torch.save(ckpt["model"], "backbone-crossformer-s.pth") ## only model weights are needed

Getting Started

  1. Modify data_root in configs/_base_/datasets/coco_detection.py and detection/configs/_base_/datasets/coco_instance.py to your path to the COCO dataset.

  2. Training

## Use config in Results table listed below as <CONFIG_FILE>
./dist_train.sh <CONFIG_FILE> <GPUS> <PRETRAIN_MODEL>

## e.g. train model with 8 GPUs
./dist_train.sh configs/retinanet_crossformer_s_fpn_1x_coco.py 8 path/to/backbone-crossformer-s.pth
  1. Inference
./dist_test.sh <CONFIG_FILE> <GPUS> <DET_CHECKPOINT_FILE> --eval bbox [segm]

## e.g. evaluate detection model
./dist_test.sh configs/retinanet_crossformer_s_fpn_1x_coco.py 8 path/to/ckpt --eval bbox

## e.g. evaluate instance segmentation model
./dist_test.sh configs/mask_rcnn_crossformer_s_fpn_1x_coco.py 8 path/to/ckpt --eval bbox segm

Results

RetinaNet

Backbone Lr schd Params FLOPs box AP config Models
ResNet-101 1x 56.7M 315.0G 38.5 - -
PVT-M 1x 53.9M - 41.9 - -
Swin-T 1x 38.5M 245.0G 41.5 - -
CrossFormer-S 1x 40.8M 282.0G 44.4 config Google Drive/BaiduCloud, key: ift3
PVT-L 1x 71.1M 345.0G 42.6 - -
Swin-B 1x 98.4M 477.0G 44.7 - -
CrossFormer-B 1x 62.1M 389.0G 46.2 config Google Drive/BaiduCloud, key: hsd5

Mask R-CNN

Backbone Lr schd Params FLOPs box AP mask AP config Models
ResNet-101 1x 63.2M 336.0G 40.4 36.4 - -
PVT-M 1x 63.9M - 42.0 39.0 - -
Swin-T 1x 47.8M 264.0G 42.2 39.1 - -
CrossFormer-S 1x 50.2M 301.0G 45.4 41.4 config Google Drive/BaiduCloud, key: 4omu
PVT-L 1x 81.0M 364.0G 42.9 39.5 - -
Swin-B 1x 107.2M 496.0G 45.5 41.3 - -
CrossFormer-B 1x 71.5M 407.9G 47.2 42.7 config Google Drive/BaiduCloud, key: kjf4
CrossFormer-S 3x 50.2M 291.1G 48.7 43.9 - -
CrossFormer-B 3x 71.5M 398.1G 49.8 44.5 - -

Cascade Mask R-CNN

Backbone Lr schd Params FLOPs box AP mask AP config Models
CrossFormer-S 3x 88.0M 769.7G 52.2 45.2 - -

Notes:

  • Models are trained on the COCO train2017 (~118k images) and evaluated on the val2017(5k images). Backbones are initialized with weights pre-trained on ImageNet-1K.
  • Models are trained with batch size 16 on 8 V100 GPUs.
  • We adopt 1x training schedule, i.e., the models are trained for 12 epochs.
  • The training image is resized to the shorter side of 800 pixels, while the longer side does not exceed 1333 pixels.
  • More detailed training settings can be found in corresponding configs.
  • More results can be seen in our paper.

FLOPs and Params Calculation

use get_flops.py to calculate FLOPs and #parameters of the specified model.

python get_flops.py <CONFIG_FILE> --shape <height> <width>

## e.g. get FLOPs and #params of retinanet_crossformer_s with input image size [1280, 800]
python get_flops.py configs/retinanet_crossformer_s_fpn_1x_coco.py --shape 1280 800

Notes: Default input image size is [1280, 800]. For calculation with different input image size, you need to change <height> <width> in the above command and change img_size in crossformer_factory.py accordingly at the same time.

Citing Us

@inproceedings{wang2021crossformer,
  title = {CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention},
  author = {Wang, Wenxiao and Yao, Lu and Chen, Long and Lin, Binbin and Cai, Deng and He, Xiaofei and Liu, Wei},
  booktitle = {International Conference on Learning Representations, {ICLR}},
  url = {https://openreview.net/forum?id=_PHymLIxuI},
  year = {2022}
}