Part way through the conversion of models to multi-weight support (model_arch.pretrain_tag), module reorg for future building, and lots of new weights and model additions as we go...

This is considered a development release. Please stick to 0.6.x if you need stability. Some of the model names, tags will shift a bit, some old names have already been deprecated and remapping support not added yet. For code 0.6.x branch is considered 'stable' https://github.com/rwightman/pytorch-image-models/tree/0.6.x

Dec 23, 2022 🎄☃

• Add FlexiViT models and weights from https://github.com/google-research/big_vision (check out paper at https://arxiv.org/abs/2212.08013)
  • NOTE currently resizing is static on model creation, on-the-fly dynamic / train patch size sampling is a WIP
• Many more models updated to multi-weight and downloadable via HF hub now (convnext, efficientnet, mobilenet, vision_transformer*, beit)
• More model pretrained tag and adjustments, some model names changed (working on deprecation translations, consider main branch DEV branch right now, use 0.6.x for stable use)
• More ImageNet-12k (subset of 22k) pretrain models popping up:
  • efficientnet_b5.in12k_ft_in1k - 85.9 @ 448x448
  • vit_medium_patch16_gap_384.in12k_ft_in1k - 85.5 @ 384x384
  • vit_medium_patch16_gap_256.in12k_ft_in1k - 84.5 @ 256x256
  • convnext_nano.in12k_ft_in1k - 82.9 @ 288x288

Dec 8, 2022

• Add 'EVA l' to vision_transformer.py, MAE style ViT-L/14 MIM pretrain w/ EVA-CLIP targets, FT on ImageNet-1k (w/ ImageNet-22k intermediate for some)
  • original source: https://github.com/baaivision/EVA

Dec 6, 2022

• Add 'EVA g', BEiT style ViT-g/14 model weights w/ both MIM pretrain and CLIP pretrain to beit.py.
  • original source: https://github.com/baaivision/EVA
  • paper: https://arxiv.org/abs/2211.07636

Dec 5, 2022

• Pre-release (0.8.0dev0) of multi-weight support (model_arch.pretrained_tag). Install with pip install --pre timm
  • vision_transformer, maxvit, convnext are the first three model impl w/ support
  • model names are changing with this (previous _21k, etc. fn will merge), still sorting out deprecation handling
  • bugs are likely, but I need feedback so please try it out
  • if stability is needed, please use 0.6.x pypi releases or clone from 0.6.x branch
• Support for PyTorch 2.0 compile is added in train/validate/inference/benchmark, use --torchcompile argument
• Inference script allows more control over output, select k for top-class index + prob json, csv or parquet output
• Add a full set of fine-tuned CLIP image tower weights from both LAION-2B and original OpenAI CLIP models

• Port of MaxViT Tensorflow Weights from official impl at https://github.com/google-research/maxvit
  • There was larger than expected drops for the upscaled 384/512 in21k fine-tune weights, possible detail missing, but the 21k FT did seem sensitive to small preprocessing

Oct 15, 2022

• Train and validation script enhancements
• Non-GPU (ie CPU) device support
• SLURM compatibility for train script
• HF datasets support (via ReaderHfds)
• TFDS/WDS dataloading improvements (sample padding/wrap for distributed use fixed wrt sample count estimate)
• in_chans !=3 support for scripts / loader
• Adan optimizer
• Can enable per-step LR scheduling via args
• Dataset 'parsers' renamed to 'readers', more descriptive of purpose
• AMP args changed, APEX via --amp-impl apex, bfloat16 supportedf via --amp-dtype bfloat16
• main branch switched to 0.7.x version, 0.6x forked for stable release of weight only adds
• master -> main branch rename