MobileOne

An Improved One millisecond Mobile Backbone

Model description

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.

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.

Step 1: Installation

## install libGL
yum install mesa-libGL

## install zlib
wget http://www.zlib.net/fossils/zlib-1.2.9.tar.gz
tar xvf zlib-1.2.9.tar.gz
cd zlib-1.2.9/
./configure && make install
cd ..
rm -rf zlib-1.2.9.tar.gz zlib-1.2.9/

# install mmcv
pushd ../../../../toolbox/MMDetection/patch/mmcv/v2.0.0rc4/
bash clean_mmcv.sh
bash build_mmcv.sh
bash install_mmcv.sh
popd

# clone mmpretrain
cd deepsparkhub/cv/classification/mobileone/pytorch
git clone https://github.com/open-mmlab/mmpretrain.git
cd mmpretrain
git checkout 4d1dbafaa28af29f5cb907588c019ae4878c2d24

pip3 install -r requirements.txt

## To avoid errors, let's disable version assert temporarily.
sed -i '9,26s/^/# /' mmpretrain/__init__.py

## using python3
sed -i 's/python /python3 /g' tools/dist_train.sh

# install mmpretrain
pip3 install mmengine==0.8.3
python3 setup.py install

Step 2: Preparing datasets

Prepare your dataset according to the docs. Sign up and login in ImageNet official website, then choose 'Download' to download the whole ImageNet dataset. Specify /path/to/imagenet to your ImageNet path in later training process.

The ImageNet dataset path structure should look like:

imagenet
├── train
│   └── n01440764
│       ├── n01440764_10026.JPEG
│       └── ...
├── train_list.txt
├── val
│   └── n01440764
│       ├── ILSVRC2012_val_00000293.JPEG
│       └── ...
└── val_list.txt

Step 3: Training

bash tools/dist_train.sh configs/mobileone/mobileone-s0_8xb32_in1k.py 8

Results

Reference

• mmpretrain