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MobileNetV3 is a convolutional neural network that 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. Advances include (1) complementary search techniques, (2) new efficient versions of nonlinearities practical for the mobile setting, (3) new efficient network design.
git clone https://github.com/PaddlePaddle/PaddleClas.git
cd PaddleClas
yum install mesa-libGL -y
pip3 install -r requirements.txt
pip3 install protobuf==3.20.3
pip3 install urllib3==1.26.13
python3 setup.py install
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
# Make sure your dataset path is the same as above
cd PaddleClas
# Link your dataset to default location
ln -s /path/to/imagenet ./dataset/ILSVRC2012
export FLAGS_cudnn_exhaustive_search=True
export FLAGS_cudnn_batchnorm_spatial_persistent=True
export CUDA_VISIBLE_DEVICES=0,1,2,3
python3 -u -m paddle.distributed.launch --gpus=0,1,2,3 tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Arch.pretrained=False -o Global.device=gpu
| GPUs | Top1 | Top5 | ips |
|---|---|---|---|
| BI-V100 x 4 | 0.749 | 0.922 | 512 samples/s |
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