This repository contains PyTorch code for ViT-NeT. It builds on code from the Swin Transformer.
Install PyTorch 1.7.0+ and torchvision 0.8.1+ and apex:
pip install pytorch pytorch torchvision
Download and extract CUB_200_2011 images from https://drive.google.com/drive/folders/1cnQHqa8BkVx90-6-UojHnbMB0WhksSRc.
/path/to/data_root/
CUB_200_2011/
attributes/
dataset/
test_crop/
class1/
img1.jpg
test_full/
class1/
img1.jpg
train_corners/
class1/
img1.jpg
train_crop
class1/
img1.jpg
train_test_split.txt
To evaluate ViT-NeT on CUB_200_2011 test set, run:
python main.py --eval --resume /path/to/weight --cfg configs/swin_tree_patch4_window14_448_CUB.yaml
ViT-NeT CUB Pretrained Model GoogleDrive
To train ViT-NeT on CUB_200_2011 on a single node with 4 gpus for 300 epochs run:
python -m torch.distributed.launch --nproc_per_node=4 --use_env main.py --cfg configs/swin_tree_patch4_window14_448_CUB.yaml --batch-size 16
SwinT/B-224-ImageNet-22K Pretrained Model (https://github.com/microsoft/Swin-Transformer)
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