ECA-Net: Efficient Channel Attention

ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

This is an implementation of ECA-Net(CVPR2020,paper), created by Banggu Wu.

Introduction

Recently, channel attention mechanism has demonstrated to offer great potential in improving the performance of deep convolutional neuralnetworks (CNNs). However, most existing methods dedicate to developing more sophisticated attention modules for achieving better performance,which inevitably increase model complexity. To overcome the paradox of performance and complexity trade-off, this paper proposes an EfficientChannel Attention (ECA) module, which only involves a handful of parameters while bringing clear performance gain. By dissecting the channelattention module in SENet, we empirically show avoiding dimensionality reduction is important for learning channel attention, and appropriatecross-channel interaction can preserve performance while significantly decreasing model complexity. Therefore, we propose a localcross-channel interaction strategy without dimensionality reduction, which can be efficiently implemented via 1D convolution. Furthermore,we develop a method to adaptively select kernel size of 1D convolution, determining coverage of local cross-channel interaction. Theproposed ECA module is efficient yet effective, e.g., the parameters and computations of our modules against backbone of ResNet50 are 80 vs.24.37M and 4.7e-4 GFLOPs vs. 3.86 GFLOPs, respectively, and the performance boost is more than 2% in terms of Top-1 accuracy. We extensivelyevaluate our ECA module on image classification, object detection and instance segmentation with backbones of ResNets and MobileNetV2. Theexperimental results show our module is more efficient while performing favorably against its counterparts.

Citation

@InProceedings{wang2020eca,
   title={ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks},
   author={Qilong Wang, Banggu Wu, Pengfei Zhu, Peihua Li, Wangmeng Zuo and Qinghua Hu},
   booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
   year={2020}
 }

Changelog

2020/02/26 Upload ECA-Resnet34 model.

2020/03/05 Upload RetinaNet-ecanet50 and RetinaNet-ecanet101 model.

2020/03/24 Update the Introduction and Citation.

2020/03/30 Upload ECA-Resnet18 model.

2020/05/06 Update the poster.

ECA module

Comparison of (a) SE block and (b) our efficient channel attention (ECA) module. Given the aggregated feature using global average pooling (GAP), SE block computes weights using two FC layers. Differently, ECA generates channel weights by performing a fast 1D convolution of size k, where k is adaptively determined via a function of channel dimension C.

Installation

Requirements

• Python 3.5+
• PyTorch 1.0+
• thop

Our environments

• OS: Ubuntu 16.04
• CUDA: 9.0/10.0
• Toolkit: PyTorch 1.0/1.1
• GPU: GTX 2080Ti/TiTan XP

Start Up

Train with ResNet

You can run the main.py to train or evaluate as follow:

CUDA_VISIBLE_DEVICES={device_ids} python main -a {model_name} --ksize {eca_kernel_size} {the path of you datasets}

For example:

CUDA_VISIBLE_DEVICES=0,1,2,3 python main -a eca_resnet50 --ksize 3557 ./datasets/ILSVRC2012/images

Train with MobileNet_v2

It is same with above ResNet replace main.py by light_main.py.

Compute the parameters and FLOPs

If you have install thop, you can paras_flosp.py to compute the parameters and FLOPs of our models. The usage is below:

python paras_flops.py -a {model_name}

Experiments

ImageNet

Model	Param.	FLOPs	Top-1(%)	Top-5(%)	BaiduDrive(models)	Extract code	GoogleDrive
ECA-Net18	11.15M	1.70G	70.92	89.93	eca_resnet18_k3577	utsy	eca_resnet18_k3577
ECA-Net34	20.79M	3.43G	74.21	91.83	eca_resnet34_k3357	o4dh	eca_resnet34_k3357
ECA-Net50	24.37M	3.86G	77.42	93.62	eca_resnet50_k3557	no6u	eca_resnet50_k3557
ECA-Net101	42.49M	7.35G	78.65	94.34	eca_resnet101_k3357	iov1	eca_resnet101_k3357
ECA-Net152	57.41M	10.83G	78.92	94.55	eca_resnet152_k3357	xaft	eca_resnet152_k3357
ECA-MobileNet_v2	3.34M	319.9M	72.56	90.81	eca_mobilenetv2_k13	atpt	eca_mobilenetv2_k13

COCO 2017

Detection with Faster R-CNN and Mask R-CNN

Model	Param.	FLOPs	AP	AP_50	AP_75	Pre trained models	Extract code	GoogleDrive
Fast_R-CNN_ecanet50	41.53M	207.18G	38.0	60.6	40.9	faster_rcnn_ecanet50_k5_bs8_lr0.01	divf	faster_rcnn_ecanet50_k5_bs8_lr0.01
Fast_R-CNN_ecanet101	60.52M	283.32G	40.3	62.9	44.0	faster_rcnn_ecanet101_3357_bs8_lr0.01	d3kd	faster_rcnn_ecanet101_3357_bs8_lr0.01
Mask_R-CNN_ecanet50	44.18M	275.69G	39.0	61.3	42.1	mask_rcnn_ecanet50_k3377_bs8_lr0.01	xe19	mask_rcnn_ecanet50_k3377_bs8_lr0.01
Mask_R-CNN_ecanet101	63.17M	351.83G	41.3	63.1	44.8	mask_rcnn_ecanet101_k3357_bs8_lr0.01	y5e9	mask_rcnn_ecanet101_k3357_bs8_lr0.01
RetinaNet_ecanet50	37.74M	239.43G	37.3	57.7	39.6	RetinaNet_ecanet50_k3377_bs8_lr0.01	my44	RetinaNet_ecanet50_k3377_bs8_lr0.01
RetinaNet_ecanet101	56.74M	315.57G	39.1	59.9	41.8	RetinaNet_ecanet101_k3357_bs8_lr0.01	2eu5	RetinaNet_ecanet101_k3357_bs8_lr0.01

Instance segmentation with Mask R-CNN

Model	Param.	FLOPs	AP	AP_50	AP_75	Pre trained models	Extract code	GoogleDrive
Mask_R-CNN_ecanet50	44.18M	275.69G	35.6	58.1	37.7	mask_rcnn_ecanet50_k3377_bs8_lr0.01	xe19	mask_rcnn_ecanet50_k3377_bs8_lr0.01
Mask_R-CNN_ecanet101	63.17M	351.83G	37.4	59.9	39.8	mask_rcnn_ecanet101_k3357_bs8_lr0.01	y5e9	mask_rcnn_ecanet101_k3357_bs8_lr0.01
RetinaNet_ecanet50	37.74M	239.43G	35.6	58.1	37.7	RetinaNet_ecanet50_k3377_bs8_lr0.01	my44	RetinaNet_ecanet50_k3377_bs8_lr0.01
RetinaNet_ecanet101	56.74M	315.57G	37.4	59.9	39.8	RetinaNet_ecanet101_k3357_bs8_lr0.01	2eu5	RetinaNet_ecanet101_k3357_bs8_lr0.01

Contact Information

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