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}
    }

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.

More implementations are welcomed.

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

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

For more details, please check the HomePage.

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.

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

./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

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

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