convnext v2

We have hosted the application convnext v2 in order to run this application in our online workstations with Wine or directly.

Quick description about convnext v2:

ConvNeXt V2 is an evolution of the ConvNeXt architecture that co-designs convolutional networks alongside self-supervised learning. The V2 version introduces a fully convolutional masked autoencoder (FCMAE) framework where parts of the image are masked and the network reconstructs the missing content, marrying convolutional inductive bias with powerful pretraining. A key innovation is a new Global Response Normalization (GRN) layer added to the ConvNeXt backbone, which enhances feature competition across channels. The result is a convnet that competes strongly with transformer architectures on recognition benchmarks while being efficient and hardware-friendly. The repository provides official PyTorch implementations for multiple model sizes (Atto, Femto, Pico, up through Huge), conversion from JAX weights, code for pretraining/fine-tuning, and pretrained checkpoints. It supports both self-supervised pretraining and supervised fine-tuning.

Features:

• Fully convolutional masked autoencoder pretraining (FCMAE)
• Global Response Normalization (GRN) to improve channel competition
• Multiple model sizes (Atto, Femto, Pico, Tiny, Base, Large, Huge)
• Support for self-supervised and supervised learning pipelines
• Pretrained checkpoints (converted from JAX) and PyTorch implementation
• Training/fine-tuning utilities and code for both pretrain and eval

Programming Language: Python.
Categories: