mobileone

MobileOne

An Improved One millisecond Mobile Backbone

Introduction

Mobileone is proposed by apple and based on reparameterization. On the apple chips, the accuracy of the model is close to 0.76 on the ImageNet dataset when the latency is less than 1ms. Its main improvements based on RepVGG are fllowing:

• Reparameterization using Depthwise convolution and Pointwise convolution instead of normal convolution.
• Removal of the residual structure which is not friendly to access memory.

Abstract

Show the paper's abstract

Efficient neural network backbones for mobile devices are often optimized for metrics such as FLOPs or parameter count. However, these metrics may not correlate well with latency of the network when deployed on a mobile device. Therefore, we perform extensive analysis of different metrics by deploying several mobile-friendly networks on a mobile device. We identify and analyze architectural and optimization bottlenecks in recent efficient neural networks and provide ways to mitigate these bottlenecks. To this end, we design an efficient backbone MobileOne, with variants achieving an inference time under 1 ms on an iPhone12 with 75.9% top-1 accuracy on ImageNet. We show that MobileOne achieves state-of-the-art performance within the efficient architectures while being many times faster on mobile. Our best model obtains similar performance on ImageNet as MobileFormer while being 38x faster. Our model obtains 2.3% better top-1 accuracy on ImageNet than EfficientNet at similar latency. Furthermore, we show that our model generalizes to multiple tasks - image classification, object detection, and semantic segmentation with significant improvements in latency and accuracy as compared to existing efficient architectures when deployed on a mobile device.

How to use it?

Predict image

from mmpretrain import inference_model

predict = inference_model('mobileone-s0_8xb32_in1k', 'demo/bird.JPEG')
print(predict['pred_class'])
print(predict['pred_score'])

Use the model

import torch
from mmpretrain import get_model

model = get_model('mobileone-s0_8xb32_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))

Train/Test Command

Prepare your dataset according to the docs.

Train:

python tools/train.py configs/mobileone/mobileone-s0_8xb32_in1k.py

Test:

python tools/test.py configs/mobileone/mobileone-s0_8xb32_in1k.py https://download.openmmlab.com/mmclassification/v0/mobileone/mobileone-s0_8xb32_in1k_20221110-0bc94952.pth

Models and results

Image Classification on ImageNet-1k

Model	Pretrain	Params (M)	Flops (G)	Top-1 (%)	Top-5 (%)	Config	Download
mobileone-s0_8xb32_in1k	From scratch	2.08	0.27	71.34	89.87	config	model | log
mobileone-s1_8xb32_in1k	From scratch	4.76	0.82	75.72	92.54	config	model | log
mobileone-s2_8xb32_in1k	From scratch	7.81	1.30	77.37	93.34	config	model | log
mobileone-s3_8xb32_in1k	From scratch	10.08	1.89	78.06	93.83	config	model | log
mobileone-s4_8xb32_in1k	From scratch	14.84	2.98	79.69	94.46	config	model | log

Citation

@article{mobileone2022,
  title={An Improved One millisecond Mobile Backbone},
  author={Vasu, Pavan Kumar Anasosalu and Gabriel, James and Zhu, Jeff and Tuzel, Oncel and Ranjan, Anurag},
  journal={arXiv preprint arXiv:2206.04040},
  year={2022}
}