Conformer: Local Features Coupling Global Representations for Visual Recognition

Accpeted to ICCV21!

This repository is built upon DeiT, timm, and mmdetction.

Introduction

Within Convolutional Neural Network (CNN), the convolution operations are good at extracting local features but experience difficulty to capture global representations. Within visual transformer, the cascaded self-attention modules can capture long-distance feature dependencies but unfortunately deteriorate local feature details. In this paper, we propose a hybrid network structure, termed Conformer, to take advantage of convolutional operations and self-attention mechanisms for enhanced representation learning. Conformer roots in the Feature Coupling Unit (FCU), which fuses local features and global representations under different resolutions in an interactive fashion. Conformer adopts a concurrent structure so that local features and global representations are retained to the maximum extent. Experiments show that Conformer, under the comparable parameter complexity, outperforms the visual transformer (DeiT-B) by 2.3% on ImageNet. On MSCOCO, it outperforms ResNet-101 by 3.7% and 3.6% mAPs for object detection and instance segmentation, respectively, demonstrating the great potential to be a general backbone network.

The basic architecture of the Conformer is shown as following:

We also show the comparison of feature maps of CNN (ResNet-101), Visual Transformer (DeiT-S), and the proposed Conformer as following. The patch embeddings in transformer are reshaped to feature maps for visualization. While CNN activates discriminative local regions ($e.g.$, the peacock's head in (a) and tail in (e)), the CNN branch of Conformer takes advantage of global cues from the visual transformer and thereby activates complete object ($e.g.$, full extent of the peacock in (b) and (f)). Compared with CNN, local feature details of the visual transformer are deteriorated ($e.g.$, (c) and (g)). In contrast, the transformer branch of Conformer retains the local feature details from CNN while depressing the background ($e.g.$, the peacock contours in (d) and (h) are more complete than those in(c) and (g).

Getting started

Install

First, install PyTorch 1.7.0+ and torchvision 0.8.1+ and pytorch-image-models 0.3.2:

conda install -c pytorch pytorch torchvision
pip install timm==0.3.2

Data preparation

Download and extract ImageNet train and val images from http://image-net.org/. The directory structure is the standard layout for the torchvision datasets.ImageFolder, and the training and validation data is expected to be in the train/ folder and val folder respectively:

/path/to/imagenet/
  train/
    class1/
      img1.jpeg
    class2/
      img2.jpeg
  val/
    class1/
      img3.jpeg
    class/2
      img4.jpeg

Training and test

Training

To train Conformer-S on ImageNet on a single node with 8 gpus for 300 epochs run:

export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
OUTPUT='./output/Conformer_small_patch16_batch_1024_lr1e-3_300epochs'

python -m torch.distributed.launch --master_port 50130 --nproc_per_node=8 --use_env main.py \
                                   --model Conformer_small_patch16 \
                                   --data-set IMNET \
                                   --batch-size 128 \
                                   --lr 0.001 \
                                   --num_workers 4 \
                                   --data-path /data/user/Dataset/ImageNet_ILSVRC2012/ \
                                   --output_dir ${OUTPUT} \
                                   --epochs 300

Test

To test Conformer-S on ImageNet on a single gpu run:

CUDA_VISIBLE_DEVICES=0, python main.py  --model Conformer_small_patch16 --eval --batch-size 64 \
                --input-size 224 \
                --data-set IMNET \
                --num_workers 4 \
                --data-path /data/user/Dataset/ImageNet_ILSVRC2012/ \
                --epochs 100 \
                --resume ../Conformer_small_patch16.pth

Model zoo

Model	Parameters	MACs	Top-1 Acc	Link
Conformer-Ti	23.5 M	5.2 G	81.3 %	baidu(code: hzhm) google
Conformer-S	37.7 M	10.6 G	83.4 %	baidu(code: qvu8) google
Conformer-B	83.3 M	23.3 G	84.1 %	baidu(code: b4z9) google

Citation

@article{peng2021conformer,
      title={Conformer: Local Features Coupling Global Representations for Visual Recognition}, 
      author={Zhiliang Peng and Wei Huang and Shanzhi Gu and Lingxi Xie and Yaowei Wang and Jianbin Jiao and Qixiang Ye},
      journal={arXiv preprint arXiv:2105.03889},
      year={2021},
}