PP-LCNet: A Lightweight CPU Convolutional Neural Network

Model description

We propose a lightweight CPU network based on the MKLDNN acceleration strategy, named PP-LCNet, which improves the performance of lightweight models on multiple tasks. This paper lists technologies which can improve network accuracy while the latency is almost constant. With these improvements, the accuracy of PP-LCNet can greatly surpass the previous network structure with the same inference time for classification. It outperforms the most state-of-the-art models. And for downstream tasks of computer vision, it also performs very well, such as object detection, semantic segmentation, etc. All our experiments are implemented based on PaddlePaddle. Code and pretrained models are available at PaddleClas.

Step 1: Installation

git clone --recursive  https://github.com/PaddlePaddle/PaddleClas.git
cd PaddleClas
pip3 install -r requirements.txt
python3 setup.py install

Step 2: Preparing datasets

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

# 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/PPLCNet/PPLCNet_x1_0.yaml -o Arch.pretrained=False -o Global.device=gpu

Results on BI-V100

Reference

• PaddleClas