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The ConvNeXT model was proposed in A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie. ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them.
pip install timm==0.4.12 tensorboardX six 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
python3 -m torch.distributed.launch --nproc_per_node=8 main.py \
--model convnext_tiny \
--drop_path 0.1 \
--batch_size 128 \
--lr 4e-3 \
--update_freq 4 \
--model_ema true \
--model_ema_eval true \
--data_path /path/to/imagenet \
--output_dir /path/to/save_results
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