SBCFormer

SBCFormer: Lightweight Network Capable of Full-size ImageNet Classification at 1 FPS on Single Board Computers

Introduction

• This paper introduces a CNN-ViT hybrid network called SBCFormer, which achieves high accuracy and fast computation on such low-end CPUs.
• We compare our SBCFormers against a wide range of relevant and up-todate alternatives.
• SBCFormer uses the proposed hourglass attention computation to aggregate global information from the entire image while minimizing computational costs.
• SBCFormer achieves the highest trade-off between accuracy and speed on a Raspberry Pi 4 Model B with an ARM-Cortex A72 CPU.
• SBCFormer as a new backbone is available for various tasks: ImageNet-1K Classification, object detection, and monocular depth estimation.[Successful Implementation].

Architecture

Classification on ImageNet-1K

Performance

Models are trained on ImageNet-1K and measured the latency performance on ARM and Intel CPUs.

Data Preparation

Download and extract ImageNet train and val images from http://image-net.org/. The training and validation data are 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

Training the SBCFormer_B on ImageNet with an 4-gpu server for 300 epochs:

OMP_NUM_THREADS=1 torchrun --nnodes 1 --nproc_per_node=4 --master_port 29502 main.py --lr 2.5e-4 --model "SBCFormer_B"  --resume "" --data-set "IMNET" --data-path "/path/to/imagenet" --input-size 224 --batch-size 1024  --epochs 300

Evaluation

Evaluating the trained SBCFormer_B on ImageNet is available:

python main.py --model "SBCFormer_B"  --eval --resume "/path/to/checkpoint" --data-set "IMNET" --data-path "/path/to/imagenet" --input-size 224 --batch-size 1024 --epochs 300 

The trained SBCFormer_B model can be downloaded from [SBCFormer_B, 80.0%]

Acknowledgement

This repository is built using the timm library and the DeiT repository.

Citation

If our code or models help your work, please cite SBCFormer (WACV2024):

@inproceedings{lu2024sbcformer,
  title={SBCFormer: Lightweight Network Capable of Full-size ImageNet Classification at 1 FPS on Single Board Computers},
  author={Lu, Xiangyong and Suganuma, Masanori and Okatani, Takayuki},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={1123--1133},
  year={2024}
}