AILab-CVC/UniRepLKNet

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1072 stars · 63 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

UniRepLKNet is a large-kernel ConvNet architecture (CVPR 2024, TPAMI 2025) that provides universal perception across image, audio, video, point cloud, and time-series modalities with a unified architecture. It ships PyTorch implementations and pretrained models achieving state-of-the-art ImageNet, COCO, ADE20K, audio recognition, and weather forecasting results.

Use cases

• train a large-kernel CNN for image classification on ImageNet
• use one unified architecture for audio, video, and image recognition
• run time-series forecasting like global temperature and wind speed prediction
• get pretrained backbone weights for object detection or semantic segmentation
• compare ConvNet vs transformer performance across multiple modalities

When to choose

• you need a fast, accurate ConvNet backbone for vision tasks like detection or segmentation
• you want a single architecture that handles multiple modalities without transformers
• you need state-of-the-art large-kernel ConvNet results with pretrained weights

When to avoid

• you need a general-purpose deep learning framework rather than a specific model
• your project requires transformer-based multimodal models
• you work outside PyTorch or lack GPU resources

Facets

library · maturity stable

machine-learning deep-learning image-processing audio-processing video-processing nlp deep-learning computer-vision artificial-intelligence image-processing python cross-platform convnet large-kernel structural-reparameterization multimodal image-classification time-series-forecasting cvpr-2024 pretrained-models audio video gpu

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