[NeurIPS 2022] Sequencer: Deep LSTM for Image Classification

Created by

• Yuki Tatsunami
  • 
  • 
• Masato Taki
  • 

This repository contains implementation for Sequencer.

Abstract

In recent computer vision research, the advent of the Vision Transformer (ViT) has rapidly revolutionized various architectural design efforts: ViT achieved state-of-the-art image classification performance using self-attention found in natural language processing, and MLP-Mixer achieved competitive performance using simple multi-layer perceptrons. In contrast, several studies have also suggested that carefully redesigned convolutional neural networks (CNNs) can achieve advanced performance comparable to ViT without resorting to these new ideas. Against this background, there is growing interest in what inductive bias is suitable for computer vision. Here we propose Sequencer, a novel and competitive architecture alternative to ViT that provides a new perspective on these issues. Unlike ViTs, Sequencer models long-range dependencies using LSTMs rather than self-attention layers. We also propose a two-dimensional version of Sequencer module, where an LSTM is decomposed into vertical and horizontal LSTMs to enhance performance. Despite its simplicity, several experiments demonstrate that Sequencer performs impressively well: Sequencer2D-L, with 54M parameters, realizes 84.6% top-1 accuracy on only ImageNet-1K. Not only that, we show that it has good transferability and the robust resolution adaptability on double resolution-band.

Schematic diagrams

The overall architecture of Sequencer2D is similar to the typical hierarchical ViT and Visual MLP. It uses Sequencer2D blocks instead of Transformer blocks:

Sequencer2D block replaces the Transformer's self-attention layer with an LSTM-based layer like BiLSTM2D layer:

BiLSTM2D includes a vertical LSTM and a horizontal LSTM:

Model Zoo

We provide our Sequencer models pretrained on ImageNet-1K:

Usage

Requirements

• torch>=1.10.0
• torchvision
• timm==0.5.4
• Pillow
• matplotlib
• scipy
• etc., see requirements.txt

Data preparation

Download and extract ImageNet images. The directory structure should be as follows.

│imagenet/
├──train/
│  ├── n01440764
│  │   ├── n01440764_10026.JPEG
│  │   ├── n01440764_10027.JPEG
│  │   ├── ......
│  ├── ......
├──val/
│  ├── n01440764
│  │   ├── ILSVRC2012_val_00000293.JPEG
│  │   ├── ILSVRC2012_val_00002138.JPEG
│  │   ├── ......
│  ├── ......

Traning

Command line for training Sequencer models on ImageNet from scratch.

./distributed_train.sh 8 /path/to/imagenet --model sequencer2d_s -b 256 -j 8 --opt adamw --epochs 300 --sched cosine --native-amp --img-size 224 --drop-path 0.1 --lr 2e-3 --weight-decay 0.05 --remode pixel --reprob 0.25 --aa rand-m9-mstd0.5-inc1 --smoothing 0.1 --mixup 0.8 --cutmix 1.0 --warmup-lr 1e-6 --warmup-epochs 20

Command line for fine-tuning a pre-trained model at higher resolution.

./distributed_train.sh 8 /path/to/imagenet --model sequencer2d_l --pretrained -b 64 -j 8 --opt adamw --epochs 30 --sched cosine --native-amp --input-size 3 392 392 --img-size 392 --crop-pct 1.0 --drop-path 0.4 --lr 5e-5 --weight-decay 1e-8 --remode pixel --reprob 0.25 --aa rand-m9-mstd0.5-inc1 --smoothing 0.1 --mixup 0.8 --cutmix 1.0 --warmup-epochs 0 --cooldown-epochs 0

Command line for fine-tuning a pre-trained model on a transfer learning dataset.

./distributed_train.sh 4 /path/to/cifar10 --model sequencer2d_s -b 128 -j 4 --num-classes 10 --dataset torch/cifar10 --pretrained --opt adamw --epochs 200 --sched cosine --native-amp --img-size 224 --clip-grad 1 --drop-path 0.1 --lr 0.0001 --weight-decay 1e-4 --remode pixel --aa rand-m9-mstd0.5-inc1 --smoothing 0.1 --mixup 0.8 --cutmix 1.0 --warmup-lr 1e-6 --warmup-epochs 5

Validation

To evaluate our Sequencer models, run:

python validate.py /path/to/imagenet --model sequencer2d_s -b 16 --input-size 3 224 224 --amp

Reference

You may want to cite:

@article{tatsunami2022sequencer,
  title={Sequencer: Deep LSTM for Image Classification},
  author={Tatsunami, Yuki and Taki, Masato},
  journal={Advances in Neural Information Processing Systems},
  year={2022}
}

Acknowledgment

This implementation is based on pytorch-image-models by Ross Wightman. We thank for his brilliant work.

We thank Graduate School of Artificial Intelligence and Science, Rikkyo University (Rikkyo AI) which supports us with computational resources, facilities, and others.
AnyTech Co. Ltd. provided valuable comments on the early versions and encouragement. We thank them for their cooperation. In particular, We thank Atsushi Fukuda for organizing discussion opportunities.