pytorch

DenseNet

Model description

A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers.

Step 1: Installing

pip install torch torchvision

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 2: Training

One single GPU

python3 train.py --data-path /path/to/imagenet --model densenet201 --batch-size 128

Multiple GPUs on one machine

python3 -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --data-path /path/to/imagenet --model densenet201 --batch-size 128

Reference

densenet