PaddlePaddle reimplementation of facebookresearch's repository for the ConvneXt model that was released with the paper A ConvNet for the 2020s.
To enjoy some new features, PaddlePaddle 2.4 is required. For more installation tutorials refer to installation.md
# Note: Set the following environment variables
# and then need to run the script on each node.
#export PADDLE_NNODES=4
#export PADDLE_MASTER="xxx.xxx.xxx.xxx:12538"
#export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python -m paddle.distributed.launch \
--nnodes=$PADDLE_NNODES \
--master=$PADDLE_MASTER \
--devices=$CUDA_VISIBLE_DEVICES \
plsc-train \
-c ./configs/ConvNeXt_base_224_in1k_4n32c_dp_fp32.yaml# [Optional] Download checkpoint
mkdir -p pretrained/
wget -O ./pretrained/ConvNeXt_base_224_in1k_dp_fp32.pdparams https://plsc.bj.bcebos.com/models/convnext/v2.5/ConvNeXt_base_224_in1k_dp_fp32.pdparams
export PADDLE_NNODES=1
export PADDLE_MASTER="127.0.0.1:12538"
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python -m paddle.distributed.launch \
--nnodes=$PADDLE_NNODES \
--master=$PADDLE_MASTER \
--devices=$CUDA_VISIBLE_DEVICES \
plsc-eval \
-c ./configs/ConvNeXt_base_224_in1k_1n8c_dp_fp32.yaml \
-o Global.pretrained_model=pretrained/ConvNeXt_base_224_in1k_dp_fp32 \
-o Global.finetune=FalseWe provide more directly runnable configurations, see ConvNeXt Configurations.
| Model | DType | Phase | Dataset | Configs | GPUs | Img/sec | Top1 Acc | Pre-trained checkpoint | Log |
|---|---|---|---|---|---|---|---|---|---|
| convnext_base | FP32 | pretrain | ImageNet2012 | config | A100*N4C32 | 7800 | 0.838 | download | log |
@Article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}