pytorch

ResNeSt14

Model description

A ResNest is a variant on a ResNet, which instead stacks Split-Attention blocks. The cardinal group representations are then concatenated along the channel dimension.As in standard residual blocks, the final output of otheur Split-Attention block is produced using a shortcut connection.

Step 1: Installing

pip3 install -r requirements.txt

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

Multiple GPUs on one machine (AMP)

Set data path by export DATA_PATH=/path/to/imagenet. The following command uses all cards to train:

bash train_resnest14_amp_dist.sh

Reference

https://github.com/zhanghang1989/ResNeSt