Awesome
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
- All of the pretrained weights and ImageNet-1K weights.
- All of the Cityscapes/ADE20K/COCO weights and code.
- An example of using our efficient conv implementation with PyTorch.
- Training script and reproducible commands.
- 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.