RepLKNet (CVPR 2022)

This is the official MegEngine implementation of RepLKNet, from the following CVPR-2022 paper:

Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs.

The paper is released on arXiv: https://arxiv.org/abs/2203.06717.

If you find the paper or this repository helpful, please consider citing

    @article{replknet,
    title={Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs},
    author={Ding, Xiaohan and Zhang, Xiangyu and Zhou, Yizhuang and Han, Jungong and Ding, Guiguang and Sun, Jian},
    journal={arXiv preprint arXiv:2203.06717},
    year={2022}
    }

Official PyTorch implementation

Our official PyTorch repository https://github.com/DingXiaoH/RepLKNet-pytorch contains

1. All of the pretrained weights and ImageNet-1K weights.
2. All of the Cityscapes/ADE20K/COCO weights and code.
3. An example of using our efficient conv implementation with PyTorch.
4. Training script and reproducible commands.
5. A script to visualize the Effective Receptive Field and instructions on obtaining the shape bias.

Other implementations

framework	link
Tensorflow	https://github.com/shkarupa-alex/tfreplknet
...

More implementations are welcomed.

Catalog

• Model code
• MegEngine pretrained models
• MegEngine training code
• MegEngine downstream models
• MegEngine downstream code

Results and Pre-trained Models

ImageNet-1K Models

name	resolution	acc	#params	FLOPs	download
RepLKNet-31B	224x224	83.58	79M	15.3G	0de394

ImageNet-22K Models

name	resolution	acc	#params	FLOPs	22K model	1K model

MegData-73M Models

name	resolution	acc@1	#params	FLOPs	MegData-73M model	1K model

Installation of MegEngine

pip3 install megengine -f https://megengine.org.cn/whl/mge.html --user

For more details, please check the HomePage.

Installation of BaseCls

BaseCls is an image classification framework built upon MegEngine. We ultilize BaseCls for ImageNet pretraining and finetuning.

pip3 install basecls --user

Training and evaluation are configured through file. All default configurations are listed here.

Evaluation

./main_imagenet_test.py -f configs/replknet31_base_224_pt1k.py -w [weights] batch_size 64 data.val_path /path/to/imagenet/val

Training

./main_imagenet_train.py -f configs/replknet31_base_224_pt1k.py data.train_path /path/to/imagenet/train data.val_path /path/to/imagenet/val

Benchmark large depth-wise kernels

We can compare the kernel speed of MegEngine against PyTorch. A minimum version of megengine 1.8.2 is required for optimized large depth-wise convolutions.

./main_benchmark.py

License

This project is released under the MIT license. Please see the LICENSE file for more information.