MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware- aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances.Nov 20, 2019.
Paper Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for mobilenetv3." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019.
# Install requirements
pip3 install easydict
yum install mesa-libGL
# Install openmpi
wget https://download.open-mpi.org/release/open-mpi/v4.0/openmpi-4.0.7.tar.gz
tar xf openmpi-4.0.7.tar.gz
cd openmpi-4.0.7/
./configure --prefix=/usr/local/bin --with-orte
make -j4 && make install
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib/
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
cd ../scripts
# 1 GPU
bash run_train.sh GPU 1 0 /path/to/imagenet/train/
# 8 GPUs
bash run_train.sh GPU 8 0,1,2,3,4,5,6,7 /path/to/imagenet/train/
bash run_infer.sh GPU /path/to/imagenet/val/ ../train/checkpointckpt_0/mobilenetv3-300_2135.ckpt
GPUS | ACC (ckpt107) | FPS |
---|---|---|
BI-V100 ×8 | 0.55 | 378.43 |
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