by Lucas Beyer, Pavel Izmailov, Alexander Kolesnikov, Mathilde Caron, Simon Kornblith, Xiaohua Zhai, Matthias Minderer, Michael Tschannen, Ibrahim Alabdulmohsin, Filip Pavetic

We publish all pre-trained FlexiViT models, and configurations for training those, as well as training logs for one run.

Please read the main big_vision README to learn how to run configs, and remember that each config file contains an example invocation in the top-level comment.

Here are the models that we used as backbones in the paper. See Tables in the appendix of the paper for expected scores at various patch-sizes and on various datasets.

First, the recommended models we used for all experiments. Remember that the input is 240px, not 224px:

These models can be used directly in our codebase by specifying model_name = "proj.flexi.vit" and model_init = "FlexiViT-L i1k" for example. See the file models/proj/flexi/vit.py for more names.

Important detail: When further re-using these models with a flexible patch size, it is recommended to keep the patch-embedding parameter buffer at its original size, and change patch-size on the fly using pi-resize, as opposed to changing the parameter buffer's size at load-time. For re-using the models with a fixed patch size, either way is fine. (The reason is that it is impossible to chain multiple resizes without loss, eg doing 32->8->32 does not result in the original weights.)

Second, the list of all released models for completeness:

We provide full training logs for a run with this public code on Cloud that reproduces the FlexiViT-S 90ep on i1k results: - metrics - config - or gs://big_vision/flexivit/deit3_i1k_s_90ep_12-15_2254.