Release v0.9.10

Nov 4

• Patch fix for 0.9.9 to fix FrozenBatchnorm2d import path for old torchvision (~2 years )

Nov 3, 2023

• DFN (Data Filtering Networks) and MetaCLIP ViT weights added
• DINOv2 'register' ViT model weights added
• Add quickgelu ViT variants for OpenAI, DFN, MetaCLIP weights that use it (less efficient)
• Improved typing added to ResNet, MobileNet-v3 thanks to Aryan
• ImageNet-12k fine-tuned (from LAION-2B CLIP) convnext_xxlarge
• 0.9.9 release

Release v0.9.9

Nov 3, 2023

• DFN (Data Filtering Networks) and MetaCLIP ViT weights added
• DINOv2 'register' ViT model weights added
• Add quickgelu ViT variants for OpenAI, DFN, MetaCLIP weights that use it (less efficient)
• Improved typing added to ResNet, MobileNet-v3 thanks to Aryan
• ImageNet-12k fine-tuned (from LAION-2B CLIP) convnext_xxlarge
• 0.9.9 release

Release v0.9.8

Oct 20, 2023

• SigLIP image tower weights supported in vision_transformer.py.
  • Great potential for fine-tune and downstream feature use.
• Experimental 'register' support in vit models as per Vision Transformers Need Registers
• Updated RepViT with new weight release. Thanks wangao
• Add patch resizing support (on pretrained weight load) to Swin models
• 0.9.8 release

Release v0.9.7

Small bug fix & extra model from v0.9.6

Sep 1, 2023

• TinyViT added by SeeFun
• Fix EfficientViT (MIT) to use torch.autocast so it works back to PT 1.10
• 0.9.7 release

Release v0.9.6

Aug 28, 2023

• Add dynamic img size support to models in vision_transformer.py, vision_transformer_hybrid.py, deit.py, and eva.py w/o breaking backward compat.
  • Add dynamic_img_size=True to args at model creation time to allow changing the grid size (interpolate abs and/or ROPE pos embed each forward pass).
  • Add dynamic_img_pad=True to allow image sizes that aren't divisible by patch size (pad bottom right to patch size each forward pass).
  • Enabling either dynamic mode will break FX tracing unless PatchEmbed module added as leaf.
  • Existing method of resizing position embedding by passing different img_size (interpolate pretrained embed weights once) on creation still works.
  • Existing method of changing patch_size (resize pretrained patch_embed weights once) on creation still works.
  • Example validation cmd python validate.py /imagenet --model vit_base_patch16_224 --amp --amp-dtype bfloat16 --img-size 255 --crop-pct 1.0 --model-kwargs dynamic_img_size=True dyamic_img_pad=True

Aug 25, 2023

• Many new models since last release
  • FastViT - https://arxiv.org/abs/2303.14189
  • MobileOne - https://arxiv.org/abs/2206.04040
  • InceptionNeXt - https://arxiv.org/abs/2303.16900
  • RepGhostNet - https://arxiv.org/abs/2211.06088 (thanks https://github.com/ChengpengChen)
  • GhostNetV2 - https://arxiv.org/abs/2211.12905 (thanks https://github.com/yehuitang)
  • EfficientViT (MSRA) - https://arxiv.org/abs/2305.07027 (thanks https://github.com/seefun)
  • EfficientViT (MIT) - https://arxiv.org/abs/2205.14756 (thanks https://github.com/seefun)
• Add --reparam arg to benchmark.py, onnx_export.py, and validate.py to trigger layer reparameterization / fusion for models with any one of reparameterize(), switch_to_deploy() or fuse()
  • Including FastViT, MobileOne, RepGhostNet, EfficientViT (MSRA), RepViT, RepVGG, and LeViT
• Preparing 0.9.6 'back to school' release

Aug 11, 2023

• Swin, MaxViT, CoAtNet, and BEiT models support resizing of image/window size on creation with adaptation of pretrained weights
• Example validation cmd to test w/ non-square resize python validate.py /imagenet --model swin_base_patch4_window7_224.ms_in22k_ft_in1k --amp --amp-dtype bfloat16 --input-size 3 256 320 --model-kwargs window_size=8,10 img_size=256,320

Release v0.9.5

Minor updates and bug fixes. New ResNeXT w/ highest ImageNet eval I'm aware of in the ResNe(X)t family (seresnextaa201d_32x8d.sw_in12k_ft_in1k_384)

Aug 3, 2023

• Add GluonCV weights for HRNet w18_small and w18_small_v2. Converted by SeeFun
• Fix selecsls* model naming regression
• Patch and position embedding for ViT/EVA works for bfloat16/float16 weights on load (or activations for on-the-fly resize)
• v0.9.5 release prep

July 27, 2023

• Added timm trained seresnextaa201d_32x8d.sw_in12k_ft_in1k_384 weights (and .sw_in12k pretrain) with 87.3% top-1 on ImageNet-1k, best ImageNet ResNet family model I'm aware of.
• RepViT model and weights (https://arxiv.org/abs/2307.09283) added by wangao
• I-JEPA ViT feature weights (no classifier) added by SeeFun
• SAM-ViT (segment anything) feature weights (no classifier) added by SeeFun
• Add support for alternative feat extraction methods and -ve indices to EfficientNet
• Add NAdamW optimizer
• Misc fixes

Release v0.9.2

• Fix _hub deprecation pass through import

Release v0.9.1

The first non pre-release since Oct 2022 with a long list of changes from 0.6.x releases...

May 12, 2023

• Fix Python 3.7 import error re Final[] typing annotation

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.

model	top1	top5	img_size	param_count	gmacs	macts
convnext_xxlarge.clip_laion2b_soup_ft_in1k	88.612	98.704	256	846.47	198.09	124.45
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384	88.312	98.578	384	200.13	101.11	126.74
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320	87.968	98.47	320	200.13	70.21	88.02
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384	87.138	98.212	384	88.59	45.21	84.49
convnext_base.clip_laion2b_augreg_ft_in12k_in1k	86.344	97.97	256	88.59	20.09	37.55

• 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.

model	top1	top5	param_count	img_size
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k	90.054	99.042	305.08	448
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k	89.946	99.01	305.08	448
eva_giant_patch14_560.m30m_ft_in22k_in1k	89.792	98.992	1014.45	560
eva02_large_patch14_448.mim_in22k_ft_in1k	89.626	98.954	305.08	448
eva02_large_patch14_448.mim_m38m_ft_in1k	89.57	98.918	305.08	448
eva_giant_patch14_336.m30m_ft_in22k_in1k	89.56	98.956	1013.01	336
eva_giant_patch14_336.clip_ft_in1k	89.466	98.82	1013.01	336
eva_large_patch14_336.in22k_ft_in22k_in1k	89.214	98.854	304.53	336
eva_giant_patch14_224.clip_ft_in1k	88.882	98.678	1012.56	224
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k	88.692	98.722	87.12	448
eva_large_patch14_336.in22k_ft_in1k	88.652	98.722	304.53	336
eva_large_patch14_196.in22k_ft_in22k_in1k	88.592	98.656	304.14	196
eva02_base_patch14_448.mim_in22k_ft_in1k	88.23	98.564	87.12	448
eva_large_patch14_196.in22k_ft_in1k	87.934	98.504	304.14	196
eva02_small_patch14_336.mim_in22k_ft_in1k	85.74	97.614	22.13	336
eva02_tiny_patch14_336.mim_in22k_ft_in1k	80.658	95.524	5.76	336

• 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
  • `co...

Release v0.9.0

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.

model	top1	top5	img_size	param_count	gmacs	macts
convnext_xxlarge.clip_laion2b_soup_ft_in1k	88.612	98.704	256	846.47	198.09	124.45
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384	88.312	98.578	384	200.13	101.11	126.74
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320	87.968	98.47	320	200.13	70.21	88.02
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384	87.138	98.212	384	88.59	45.21	84.49
convnext_base.clip_laion2b_augreg_ft_in12k_in1k	86.344	97.97	256	88.59	20.09	37.55

• 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.

model	top1	top5	param_count	img_size
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k	90.054	99.042	305.08	448
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k	89.946	99.01	305.08	448
eva_giant_patch14_560.m30m_ft_in22k_in1k	89.792	98.992	1014.45	560
eva02_large_patch14_448.mim_in22k_ft_in1k	89.626	98.954	305.08	448
eva02_large_patch14_448.mim_m38m_ft_in1k	89.57	98.918	305.08	448
eva_giant_patch14_336.m30m_ft_in22k_in1k	89.56	98.956	1013.01	336
eva_giant_patch14_336.clip_ft_in1k	89.466	98.82	1013.01	336
eva_large_patch14_336.in22k_ft_in22k_in1k	89.214	98.854	304.53	336
eva_giant_patch14_224.clip_ft_in1k	88.882	98.678	1012.56	224
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k	88.692	98.722	87.12	448
eva_large_patch14_336.in22k_ft_in1k	88.652	98.722	304.53	336
eva_large_patch14_196.in22k_ft_in22k_in1k	88.592	98.656	304.14	196
eva02_base_patch14_448.mim_in22k_ft_in1k	88.23	98.564	87.12	448
eva_large_patch14_196.in22k_ft_in1k	87.934	98.504	304.14	196
eva02_small_patch14_336.mim_in22k_ft_in1k	85.74	97.614	22.13	336
eva02_tiny_patch14_336.mim_in22k_ft_in1k	80.658	95.524	5.76	336

• 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 ...

Release v0.6.13

Release from 0.6.x stable branch with fix for Python 3.11. NOTE original 0.6.13 release tag was against wrong branch.