First non pre-release in a loooong while, changelog from 0.6.x below...

May 11, 2023

• timm 0.9 released, transition from 0.8.xdev releases

May 10, 2023

• Hugging Face Hub downloading is now default, 1132 models on https://huggingface.co/timm, 1163 weights in timm
• DINOv2 vit feature backbone weights added thanks to Leng Yue
• FB MAE vit feature backbone weights added
• OpenCLIP DataComp-XL L/14 feat backbone weights added
• MetaFormer (poolformer-v2, caformer, convformer, updated poolformer (v1)) w/ weights added by Fredo Guan
• Experimental get_intermediate_layers function on vit/deit models for grabbing hidden states (inspired by DINO impl). This is WIP and may change significantly... feedback welcome.
• Model creation throws error if pretrained=True and no weights exist (instead of continuing with random initialization)
• Fix regression with inception / nasnet TF sourced weights with 1001 classes in original classifiers
• bitsandbytes (https://github.com/TimDettmers/bitsandbytes) optimizers added to factory, use bnb prefix, ie bnbadam8bit
• Misc cleanup and fixes
• Final testing before switching to a 0.9 and bringing timm out of pre-release state

April 27, 2023

• 97% of timm models uploaded to HF Hub and almost all updated to support multi-weight pretrained configs
• Minor cleanup and refactoring of another batch of models as multi-weight added. More fused_attn (F.sdpa) and features_only support, and torchscript fixes.

April 21, 2023

• Gradient accumulation support added to train script and tested (--grad-accum-steps), thanks Taeksang Kim
• More weights on HF Hub (cspnet, cait, volo, xcit, tresnet, hardcorenas, densenet, dpn, vovnet, xception_aligned)
• Added --head-init-scale and --head-init-bias to train.py to scale classiifer head and set fixed bias for fine-tune
• Remove all InplaceABN (inplace_abn) use, replaced use in tresnet with standard BatchNorm (modified weights accordingly).

April 12, 2023

• Add ONNX export script, validate script, helpers that I've had kicking around for along time. Tweak 'same' padding for better export w/ recent ONNX + pytorch.
• Refactor dropout args for vit and vit-like models, separate drop_rate into drop_rate (classifier dropout), proj_drop_rate (block mlp / out projections), pos_drop_rate (position embedding drop), attn_drop_rate (attention dropout). Also add patch dropout (FLIP) to vit and eva models.
• fused F.scaled_dot_product_attention support to more vit models, add env var (TIMM_FUSED_ATTN) to control, and config interface to enable/disable
• Add EVA-CLIP backbones w/ image tower weights, all the way up to 4B param 'enormous' model, and 336x336 OpenAI ViT mode that was missed.

April 5, 2023

• ALL ResNet models pushed to Hugging Face Hub with multi-weight support
  • All past timm trained weights added with recipe based tags to differentiate
  • All ResNet strikes back A1/A2/A3 (seed 0) and R50 example B/C1/C2/D weights available
  • Add torchvision v2 recipe weights to existing torchvision originals
  • See comparison table in https://huggingface.co/timm/seresnextaa101d_32x8d.sw_in12k_ft_in1k_288#model-comparison
• New ImageNet-12k + ImageNet-1k fine-tunes available for a few anti-aliased ResNet models
  • resnetaa50d.sw_in12k_ft_in1k - 81.7 @ 224, 82.6 @ 288
  • resnetaa101d.sw_in12k_ft_in1k - 83.5 @ 224, 84.1 @ 288
  • seresnextaa101d_32x8d.sw_in12k_ft_in1k - 86.0 @ 224, 86.5 @ 288
  • seresnextaa101d_32x8d.sw_in12k_ft_in1k_288 - 86.5 @ 288, 86.7 @ 320

March 31, 2023

• Add first ConvNext-XXLarge CLIP -> IN-1k fine-tune and IN-12k intermediate fine-tunes for convnext-base/large CLIP models.

• Add EVA-02 MIM pretrained and fine-tuned weights, push to HF hub and update model cards for all EVA models. First model over 90% top-1 (99% top-5)! Check out the original code & weights at https://github.com/baaivision/EVA for more details on their work blending MIM, CLIP w/ many model, dataset, and train recipe tweaks.

• Multi-weight and HF hub for DeiT and MLP-Mixer based models

March 22, 2023

• More weights pushed to HF hub along with multi-weight support, including: regnet.py, rexnet.py, byobnet.py, resnetv2.py, swin_transformer.py, swin_transformer_v2.py, swin_transformer_v2_cr.py
• Swin Transformer models support feature extraction (NCHW feat maps for swinv2_cr_*, and NHWC for all others) and spatial embedding outputs.
• FocalNet (from https://github.com/microsoft/FocalNet) models and weights added with significant refactoring, feature extraction, no fixed resolution / sizing constraint
• RegNet weights increased with HF hub push, SWAG, SEER, and torchvision v2 weights. SEER is pretty poor wrt to performance for model size, but possibly useful.
• More ImageNet-12k pretrained and 1k fine-tuned timm weights:
  • rexnetr_200.sw_in12k_ft_in1k - 82.6 @ 224, 83.2 @ 288
  • rexnetr_300.sw_in12k_ft_in1k - 84.0 @ 224, 84.5 @ 288
  • regnety_120.sw_in12k_ft_in1k - 85.0 @ 224, 85.4 @ 288
  • regnety_160.lion_in12k_ft_in1k - 85.6 @ 224, 86.0 @ 288
  • regnety_160.sw_in12k_ft_in1k - 85.6 @ 224, 86.0 @ 288 (compare to SWAG PT + 1k FT this is same BUT much lower res, blows SEER FT away)
• Model name deprecation + remapping functionality added (a milestone for bringing 0.8.x out of pre-release). Mappings being added...
• Minor bug fixes and improvements.

Feb 26, 2023

• Add ConvNeXt-XXLarge CLIP pretrained image tower weights for fine-tune & features (fine-tuning TBD) -- see model card
• Update convnext_xxlarge default LayerNorm eps to 1e-5 (for CLIP weights, improved stability)
• 0.8.15dev0

Feb 20, 2023

• Add 320x320 convnext_large_mlp.clip_laion2b_ft_320 and convnext_lage_mlp.clip_laion2b_ft_soup_320 CLIP image tower weights for features & fine-tune
• 0.8.13dev0 pypi release for latest changes w/ move to huggingface org

Feb 16, 2023

• safetensor checkpoint support added
• Add ideas from 'Scaling Vision Transformers to 22 B. Params' (https://arxiv.org/abs/2302.05442) -- qk norm, RmsNorm, parallel block
• Add F.scaled_dot_product_attention support (PyTorch 2.0 only) to vit_*, vit_relpos*, coatnet / maxxvit (to start)
• Lion optimizer (w/ multi-tensor option) added (https://arxiv.org/abs/2302.06675)
• gradient checkpointing works with features_only=True

Feb 7, 2023

• New inference benchmark numbers added in results folder.
• Add convnext LAION CLIP trained weights and initial set of in1k fine-tunes
  • convnext_base.clip_laion2b_augreg_ft_in1k - 86.2% @ 256x256
  • convnext_base.clip_laiona_augreg_ft_in1k_384 - 86.5% @ 384x384
  • convnext_large_mlp.clip_laion2b_augreg_ft_in1k - 87.3% @ 256x256
  • convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384 - 87.9% @ 384x384
• Add DaViT models. Supports features_only=True. Adapted from https://github.com/dingmyu/davit by Fredo.
• Use a common NormMlpClassifierHead across MaxViT, ConvNeXt, DaViT
• Add EfficientFormer-V2 model, update EfficientFormer, and refactor LeViT (closely related architectures). Weights on HF hub.
  • New EfficientFormer-V2 arch, significant refactor from original at (https://github.com/snap-research/EfficientFormer). Supports features_only=True.
  • Minor updates to EfficientFormer.
  • Refactor LeViT models to stages, add features_only=True support to new conv variants, weight remap required.
• Move ImageNet meta-data (synsets, indices) from /results to timm/data/_info.
• Add ImageNetInfo / DatasetInfo classes to provide labelling for various ImageNet classifier layouts in timm
  • Update inference.py to use, try: python inference.py /folder/to/images --model convnext_small.in12k --label-type detail --topk 5
• Ready for 0.8.10 pypi pre-release (final testing).

Jan 20, 2023

• Add two convnext 12k -> 1k fine-tunes at 384x384

  • convnext_tiny.in12k_ft_in1k_384 - 85.1 @ 384
  • convnext_small.in12k_ft_in1k_384 - 86.2 @ 384

• Push all MaxxViT weights to HF hub, and add new ImageNet-12k -> 1k fine-tunes for rw base MaxViT and CoAtNet 1/2 models

Jan 11, 2023

• Update ConvNeXt ImageNet-12k pretrain series w/ two new fine-tuned weights (and pre FT .in12k tags)
  • convnext_nano.in12k_ft_in1k - 82.3 @ 224, 82.9 @ 288 (previously released)
  • convnext_tiny.in12k_ft_in1k - 84.2 @ 224, 84.5 @ 288
  • convnext_small.in12k_ft_in1k - 85.2 @ 224, 85.3 @ 288

Jan 6, 2023

• Finally got around to adding --model-kwargs and --opt-kwargs to scripts to pass through rare args directly to model classes from cmd line
  • train.py /imagenet --model resnet50 --amp --model-kwargs output_stride=16 act_layer=silu
  • train.py /imagenet --model vit_base_patch16_clip_224 --img-size 240 --amp --model-kwargs img_size=240 patch_size=12
• Cleanup some popular models to better support arg passthrough / merge with model configs, more to go.

Jan 5, 2023

• ConvNeXt-V2 models and weights added to existing convnext.py
  • Paper: ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
  • Reference impl: https://github.com/facebookresearch/ConvNeXt-V2 (NOTE: weights currently CC-BY-NC)

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