VGG16

Model description

VGG is a classical convolutional neural network architecture. It was based on an analysis of how to increase the depth of such networks. The network utilises small 3 x 3 filters. Otherwise the network is characterized by its simplicity: the only other components being pooling layers and a fully connected layer.

Step 1: Installation

pip3 install -r requirements.txt

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

# Set data path
export DATA_PATH=/path/to/imagenet

# Multiple GPUs on one machine
bash train_vgg16_amp_dist.sh

Install zlib-1.2.9 if reports "iZLIB_1.2.9 not found" when run train_vgg16_amp_dist.sh

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/

Reference

• torchvision