scenic is queued for analysis. Crawling candidates and ranking them takes a few minutes. This page updates itself when the list exists — you can also come back later.
What is known so far comes from the other direction:
These projects were analysed and named scenic among their alternatives. The relationship is not symmetric — how scenic rates them is a separate judgement, made when scenic is analysed in its own right.
calls scenic “JAX CV research library; choose it when your stack is JAX and you need production-grade ViT/other models.”
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
calls scenic “Choose it when you prefer a maintained JAX/Flax research library over the PyTorch Swin repo and need a broad set of vision architectures.”
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".
calls scenic “choose for a maintained JAX research library with broader CV training utilities rather than this minimal fine-tuning repo.”
calls scenic “When you need a JAX-based vision research library with model configs and training pipelines.”
CoreNet: A library for training deep neural networks