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This repo contains the PyTorch version of 8 model definitions (Atto, Femto, Pico, Nano, Tiny, Base, Large, Huge), pre-training/fine-tuning code and pre-trained weights (converted from JAX weights trained on TPU) for our ConvNeXt V2 paper.
The main features of facebookresearch/convnext-v2 are: Attention Free Architectures, Vision Transformers.
Open-source alternatives to facebookresearch/convnext-v2 include: huawei-noah/cv-backbones — CV-Backbones is a computer vision backbone library and model zoo providing a collection of pre-defined neural network… aliborji/splitmixer. alinlab/remixer. amazon-research/anti-aliasing-transformer. amoza-theodore/mdmlp. ailab-cvc/groupmixformer.
CV-Backbones is a computer vision backbone library and model zoo providing a collection of pre-defined neural network architectures for extracting visual features and processing image data. It serves as a PyTorch vision framework of reusable deep learning components designed for image analysis and visual representation learning. The library focuses on efficient neural network architectures to reduce computational overhead while maintaining feature extraction performance. This is achieved through the implementation of lightweight model designs such as GhostNet and MLP. The project covers a br