Update README.md

README.md

@@ -139,28 +139,30 @@ My open-sourced papers and repos:
 
 The **Structural Re-parameterization Universe**:
 
-1. RepMLP (preprint, 2021) **MLP-style building block and Architecture**\
+1. RepLKNet (CVPR 2022) **Powerful efficient architecture with very large kernels (31x31) and guidelines for using large kernels in model CNNs**\
+[Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs](https://arxiv.org/abs/2203.06717)\
+[code](https://github.com/DingXiaoH/RepLKNet-pytorch).
+
+2. RepMLP (CVPR 2022) **MLP-style building block and Architecture**\
 [RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality](https://arxiv.org/abs/2112.11081)\
 [code](https://github.com/DingXiaoH/RepMLP).
 
-2. RepVGG (CVPR 2021) **A super simple and powerful VGG-style ConvNet architecture**. Up to **84.16%** ImageNet top-1 accuracy!\
+3. RepVGG (CVPR 2021) **A super simple and powerful VGG-style ConvNet architecture**. Up to **84.16%** ImageNet top-1 accuracy!\
 [RepVGG: Making VGG-style ConvNets Great Again](https://arxiv.org/abs/2101.03697)\
 [code](https://github.com/DingXiaoH/RepVGG).
 
-3. ResRep (ICCV 2021) **State-of-the-art** channel pruning (Res50, 55\% FLOPs reduction, 76.15\% acc)\
+4. ResRep (ICCV 2021) **State-of-the-art** channel pruning (Res50, 55\% FLOPs reduction, 76.15\% acc)\
 [ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting](https://openaccess.thecvf.com/content/ICCV2021/papers/Ding_ResRep_Lossless_CNN_Pruning_via_Decoupling_Remembering_and_Forgetting_ICCV_2021_paper.pdf)\
 [code](https://github.com/DingXiaoH/ResRep).
 
-4. ACB (ICCV 2019) is a CNN component without any inference-time costs. The first work of our Structural Re-parameterization Universe.\
+5. ACB (ICCV 2019) is a CNN component without any inference-time costs. The first work of our Structural Re-parameterization Universe.\
 [ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks](http://openaccess.thecvf.com/content_ICCV_2019/papers/Ding_ACNet_Strengthening_the_Kernel_Skeletons_for_Powerful_CNN_via_Asymmetric_ICCV_2019_paper.pdf).\
 [code](https://github.com/DingXiaoH/ACNet). 
 
-5. DBB (CVPR 2021) is a CNN component with higher performance than ACB and still no inference-time costs. Sometimes I call it ACNet v2 because "DBB" is 2 bits larger than "ACB" in ASCII (lol).\
+6. DBB (CVPR 2021) is a CNN component with higher performance than ACB and still no inference-time costs. Sometimes I call it ACNet v2 because "DBB" is 2 bits larger than "ACB" in ASCII (lol).\
 [Diverse Branch Block: Building a Convolution as an Inception-like Unit](https://arxiv.org/abs/2103.13425)\
 [code](https://github.com/DingXiaoH/DiverseBranchBlock).
 
-6. COMING SOON
-
 7. COMING SOON
 
 **Model compression and acceleration**: