[R] UniRepLKNet: Large-Kernel CNN Unifies Multi Modalities, ImageNet 88%, SOTA in Global Weather Forecasting

Paper: [https://arxiv.org/abs/2311.15599](https://arxiv.org/abs/2311.15599)

Huggingface: [https://huggingface.co/DingXiaoH/UniRepLKNet/tree/main](https://huggingface.co/DingXiaoH/UniRepLKNet/tree/main)

Project page: [https://invictus717.github.io/UniRepLKNet/](https://invictus717.github.io/UniRepLKNet/)

GitHub (code, models, reproducible scripts released): [https://github.com/AILab-CVC/UniRepLKNet](https://github.com/AILab-CVC/UniRepLKNet)

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https://preview.redd.it/clo0keoe6u5c1.png?width=865&format=png&auto=webp&s=0b246d5ef60dbd3dd3b3d485e9e750f12ffd0b12

# TL;DR

**Q**: What's the main contribution of this research?

**A**: We've developed four guidelines specifically for large-kernel CNN architecture design and a powerful backbone called **UniRepLKNet**. With **only ImageNet-22K pre-training**, it achieves state-of-the-art (SOTA) performance in accuracy and speed on **ImageNet (88% accuracy), COCO (56.4 box AP), and ADE20K (55.6 mIoU)**. It also shows significant actual speed advantage and reaches SOTA levels in …

accuracy architecture art box cnn coco design guidelines imagenet kernel machinelearning performance pre-training research shows sota speed state training