Conformer¶

Conformer: Local Features Coupling Global Representations for Visual Recognition

Abstract¶

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.

How to use it?¶

from mmpretrain import inference_model

predict = inference_model('conformer-tiny-p16_3rdparty_in1k', 'demo/bird.JPEG')
print(predict['pred_class'])
print(predict['pred_score'])

import torch
from mmpretrain import get_model

model = get_model('conformer-tiny-p16_3rdparty_in1k', pretrained=True)
inputs = torch.rand(1, 3, 224, 224)
out = model(inputs)
print(type(out))
# To extract features.
feats = model.extract_feat(inputs)
print(type(feats))

Prepare your dataset according to the docs.

Train:

python tools/train.py configs/conformer/conformer-small-p32_8xb128_in1k.py

Test:

python tools/test.py configs/conformer/conformer-tiny-p16_8xb128_in1k.py https://download.openmmlab.com/mmclassification/v0/conformer/conformer-tiny-p16_3rdparty_8xb128_in1k_20211206-f6860372.pth

Models and results¶

Image Classification on ImageNet-1k¶

Model	Pretrain	Params (M)	Flops (G)	Top-1 (%)	Top-5 (%)	Config	Download
`conformer-tiny-p16_3rdparty_in1k`*	From scratch	23.52	4.90	81.31	95.60	config	model
`conformer-small-p16_3rdparty_in1k`*	From scratch	37.67	10.31	83.32	96.46	config	model
`conformer-small-p32_8xb128_in1k`	From scratch	38.85	7.09	81.96	96.02	config	model
`conformer-base-p16_3rdparty_in1k`*	From scratch	83.29	22.89	83.82	96.59	config	model

Models with * are converted from the official repo. The config files of these models are only for inference. We haven’t reproduce the training results.

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},
}