paddlepaddle

EfficientNetB0

Model description

This model is the B0 version of the EfficientNet series, whitch can be used for image classification tasks, such as cat and dog classification, flower classification, and so on.

Step 1: Installation

git clone -b release/2.5 https://github.com/PaddlePaddle/PaddleClas.git

cd PaddleClas
pip3 install -r requirements.txt
pip3 install paddleclas
pip3 install protobuf==3.20.3
yum install mesa-libGL 
pip3 install urllib3==1.26.15

Step 2: Preparing datasets

Sign up and login in ImageNet official website, then choose 'Download' to download the whole ImageNet dataset.

The ImageNet dataset path structure should look like:

PaddleClas/dataset/ILSVRC2012/
├── train
│   └── n01440764
│       ├── n01440764_10026.JPEG
│       └── ...
├── train_list.txt
├── val
│   └── n01440764
│       ├── ILSVRC2012_val_00000293.JPEG
│       └── ...
└── val_list.txt

Tips

For PaddleClas training, the image path in train_list.txt and val_list.txt must contain train/ and val/ directories:

• train_list.txt: train/n01440764/n01440764_10026.JPEG 0
• val_list.txt: val/n01667114/ILSVRC2012_val_00000229.JPEG 35

# add "train/" and "val/" to head of lines
sed -i 's#^#train/#g' train_list.txt
sed -i 's#^#val/#g' val_list.txt

Step 3: Training

# Link your dataset to default location
cd PaddleClas/
ln -s /path/to/imagenet ./dataset/ILSVRC2012

# 8 GPUs
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python3 -m paddle.distributed.launch tools/train.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB0.yaml

# 1 GPU
export CUDA_VISIBLE_DEVICES=0
python3 tools/train.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB0.yaml

Results

GPUs	ips	Top1	Top5
BI-V100 x8	1065.28	0.7683	0.9316

Reference

• PaddleClas