# RepLKNet > [Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs](https://arxiv.org/abs/2203.06717) ## Abstract We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a few large convolutional kernels instead of a stack of small kernels could be a more powerful paradigm. We suggested five guidelines, e.g., applying re-parameterized large depth-wise convolutions, to design efficient highperformance large-kernel CNNs. Following the guidelines, we propose RepLKNet, a pure CNN architecture whose kernel size is as large as 31×31, in contrast to commonly used 3×3. RepLKNet greatly closes the performance gap between CNNs and ViTs, e.g., achieving comparable or superior results than Swin Transformer on ImageNet and a few typical downstream tasks, with lower latency. RepLKNet also shows nice scalability to big data and large models, obtaining 87.8% top-1 accuracy on ImageNet and 56.0% mIoU on ADE20K, which is very competitive among the state-of-the-arts with similar model sizes. Our study further reveals that, in contrast to small-kernel CNNs, large kernel CNNs have much larger effective receptive fields and higher shape bias rather than texture bias.
## How to use it? **Predict image** ```python from mmpretrain import inference_model, get_model model = get_model('replknet-31B_3rdparty_in1k', pretrained=True) model.backbone.switch_to_deploy() predict = inference_model(model, 'demo/bird.JPEG') print(predict['pred_class']) print(predict['pred_score']) ``` **Use the model** ```python import torch from mmpretrain import get_model model = get_model('replknet-31B_3rdparty_in1k', pretrained=True) inputs = torch.rand(1, 3, 224, 224) out = model(inputs) print(type(out)) # To extract features. feats = model.extract_feat(inputs) print(type(feats)) ``` **Test Command** Prepare your dataset according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset). Test: ```shell python tools/test.py configs/replknet/replknet-31B_32xb64_in1k.py https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31B_3rdparty_in1k_20221118-fd08e268.pth ``` **Reparameterization** The checkpoints provided are all `training-time` models. Use the reparameterize tool to switch them to more efficient `inference-time` architecture, which not only has fewer parameters but also less calculations. ```bash python tools/convert_models/reparameterize_model.py ${CFG_PATH} ${SRC_CKPT_PATH} ${TARGET_CKPT_PATH} ``` `${CFG_PATH}` is the config file, `${SRC_CKPT_PATH}` is the source chenpoint file, `${TARGET_CKPT_PATH}` is the target deploy weight file path. To use reparameterized weights, the config file must switch to the deploy config files. ```bash python tools/test.py ${deploy_cfg} ${deploy_checkpoint} --metrics accuracy ``` You can also use `backbone.switch_to_deploy()` to switch to the deploy mode in Python code. For example: ```python from mmpretrain.models import RepLKNet backbone = RepLKNet(arch='31B') backbone.switch_to_deploy() ``` ## Models and results ### Image Classification on ImageNet-1k | Model | Pretrain | Params (M) | Flops (G) | Top-1 (%) | Top-5 (%) | Config | Download | | :--------------------------------------------- | :----------: | :--------: | :-------: | :-------: | :-------: | :-----------------------------------------: | :------------------------------------------------------------: | | `replknet-31B_3rdparty_in1k`\* | From scratch | 79.86 | 15.64 | 83.48 | 96.57 | [config](replknet-31B_32xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31B_3rdparty_in1k_20221118-fd08e268.pth) | | `replknet-31B_3rdparty_in1k-384px`\* | From scratch | 79.86 | 45.95 | 84.84 | 97.34 | [config](replknet-31B_32xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31B_3rdparty_in1k-384px_20221118-03a170ce.pth) | | `replknet-31B_in21k-pre_3rdparty_in1k`\* | ImageNet-21k | 79.86 | 15.64 | 85.20 | 97.56 | [config](replknet-31B_32xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31B_in21k-pre_3rdparty_in1k_20221118-54ed5c46.pth) | | `replknet-31B_in21k-pre_3rdparty_in1k-384px`\* | ImageNet-21k | 79.86 | 45.95 | 85.99 | 97.75 | [config](replknet-31B_32xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31B_in21k-pre_3rdparty_in1k-384px_20221118-76c92b24.pth) | | `replknet-31L_in21k-pre_3rdparty_in1k-384px`\* | ImageNet-21k | 172.67 | 97.24 | 86.63 | 98.00 | [config](replknet-31L_32xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/replknet/replknet-31L_in21k-pre_3rdparty_in1k-384px_20221118-dc3fc07c.pth) | | `replknet-XL_meg73m-pre_3rdparty_in1k-320px`\* | MEG73M | 335.44 | 129.57 | 87.57 | 98.39 | [config](replknet-XL_32xb64_in1k-320px.py) | [model](https://download.openmmlab.com/mmclassification/v0/replknet/replknet-XL_meg73m-pre_3rdparty_in1k-320px_20221118-88259b1d.pth) | *Models with * are converted from the [official repo](https://github.com/DingXiaoH/RepLKNet-pytorch/blob/main/replknet.py). The config files of these models are only for inference. We haven't reprodcue the training results.* ## Citation ```bibtex @inproceedings{ding2022scaling, title={Scaling up your kernels to 31x31: Revisiting large kernel design in cnns}, author={Ding, Xiaohan and Zhang, Xiangyu and Han, Jungong and Ding, Guiguang}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={11963--11975}, year={2022} } ```