Release v0.8.17dev0

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

v0.8.13dev0 Release

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)

v0.8.10dev0 Release

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

model	top1	top5	samples / sec	Params (M)	GMAC	Act (M)
maxvit_xlarge_tf_512.in21k_ft_in1k	88.53	98.64	21.76	475.77	534.14	1413.22
maxvit_xlarge_tf_384.in21k_ft_in1k	88.32	98.54	42.53	475.32	292.78	668.76
maxvit_base_tf_512.in21k_ft_in1k	88.20	98.53	50.87	119.88	138.02	703.99
maxvit_large_tf_512.in21k_ft_in1k	88.04	98.40	36.42	212.33	244.75	942.15
maxvit_large_tf_384.in21k_ft_in1k	87.98	98.56	71.75	212.03	132.55	445.84
maxvit_base_tf_384.in21k_ft_in1k	87.92	98.54	104.71	119.65	73.80	332.90
maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k	87.81	98.37	106.55	116.14	70.97	318.95
maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k	87.47	98.37	149.49	116.09	72.98	213.74
coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k	87.39	98.31	160.80	73.88	47.69	209.43
maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k	86.89	98.02	375.86	116.14	23.15	92.64
maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k	86.64	98.02	501.03	116.09	24.20	62.77
maxvit_base_tf_512.in1k	86.60	97.92	50.75	119.88	138.02	703.99
coatnet_2_rw_224.sw_in12k_ft_in1k	86.57	97.89	631.88	73.87	15.09	49.22
maxvit_large_tf_512.in1k	86.52	97.88	36.04	212.33	244.75	942.15
coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k	86.49	97.90	620.58	73.88	15.18	54.78
maxvit_base_tf_384.in1k	86.29	97.80	101.09	119.65	73.80	332.90
maxvit_large_tf_384.in1k	86.23	97.69	70.56	212.03	132.55	445.84
maxvit_small_tf_512.in1k	86.10	97.76	88.63	69.13	67.26	383.77
maxvit_tiny_tf_512.in1k	85.67	97.58	144.25	31.05	33.49	257.59
maxvit_small_tf_384.in1k	85.54	97.46	188.35	69.02	35.87	183.65
maxvit_tiny_tf_384.in1k	85.11	97.38	293.46	30.98	17.53	123.42
maxvit_large_tf_224.in1k	84.93	96.97	247.71	211.79	43.68	127.35
coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k	84.90	96.96	1025.45	41.72	8.11	40.13
maxvit_base_tf_224.in1k	84.85	96.99	358.25	119.47	24.04	95.01
maxxvit_rmlp_small_rw_256.sw_in1k	84.63	97.06	575.53	66.01	14.67	58.38
coatnet_rmlp_2_rw_224.sw_in1k	84.61	96.74	625.81	73.88	15.18	54.78
maxvit_rmlp_small_rw_224.sw_in1k	84.49	96.76	693.82	64.90	10.75	49.30
maxvit_small_tf_224.in1k	84.43	96.83	647.96	68.93	11.66	53.17
maxvit_rmlp_tiny_rw_256.sw_in1k	84.23	96.78	807.21	29.15	6.77	46.92
coatnet_1_rw_224.sw_in1k	83.62	96.38	989.59	41.72	8.04	34.60
maxvit_tiny_rw_224.sw_in1k	83.50	96.50	1100.53	29.06	5.11	33.11
maxvit_tiny_tf_224.in1k	83.41	96.59	1004.94	30.92	5.60	35.78
coatnet_rmlp_1_rw_224.sw_in1k	83.36	96.45	1093.03	41.69	7.85	35.47
maxxvitv2_nano_rw_256.sw_in1k	83.11	96.33	1276.88	23.70	6.26	23.05
maxxvit_rmlp_nano_rw_256.sw_in1k	83.03	96.34	1341.24	16.78	4.37	26.05
maxvit_rmlp_nano_rw_256.sw_in1k	82.96	96.26	1283.24	15.50	4.47	31.92
maxvit_nano_rw_256.sw_in1k	82.93	96.23	1218.17	15.45	4.46	30.28
coatnet_bn_0_rw_224.sw_in1k	82.39	96.19	1600.14	27.44	4.67	22.04
coatnet_0_rw_224.sw_in1k	82.39	95.84	1831.21	27.44	4.43	18.73
coatnet_rmlp_nano_rw_224.sw_in1k	82.05	95.87	2109.09	15.15	2.62	20.34
coatnext_nano_rw_224.sw_in1k	81.95	95.92	2525.52	14.70	2.47	12.80
coatnet_nano_rw_224.sw_in1k	81.70	95.64	2344.52	15.14	2.41	15.41
maxvit_rmlp_pico_rw_256.sw_in1k	80....

v0.8.6dev0 Release

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)

v0.8.2dev0 Release

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

model	top1	param_count	gmac	macts	hub
eva_large_patch14_336.in22k_ft_in22k_in1k	89.2	304.5	191.1	270.2	link
eva_large_patch14_336.in22k_ft_in1k	88.7	304.5	191.1	270.2	link
eva_large_patch14_196.in22k_ft_in22k_in1k	88.6	304.1	61.6	63.5	link
eva_large_patch14_196.in22k_ft_in1k	87.9	304.1	61.6	63.5	link

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

model	top1	param_count	gmac	macts	hub
eva_giant_patch14_560.m30m_ft_in22k_in1k	89.8	1014.4	1906.8	2577.2	link
eva_giant_patch14_336.m30m_ft_in22k_in1k	89.6	1013	620.6	550.7	link
eva_giant_patch14_336.clip_ft_in1k	89.4	1013	620.6	550.7	link
eva_giant_patch14_224.clip_ft_in1k	89.1	1012.6	267.2	192.6	link

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

model	top1	param_count	gmac	macts	hub
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k	88.6	632.5	391	407.5	link
vit_large_patch14_clip_336.openai_ft_in12k_in1k	88.3	304.5	191.1	270.2	link
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k	88.2	632	167.4	139.4	link
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k	88.2	304.5	191.1	270.2	link
vit_large_patch14_clip_224.openai_ft_in12k_in1k	88.2	304.2	81.1	88.8	link
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k	87.9	304.2	81.1	88.8	link
vit_large_patch14_clip_224.openai_ft_in1k	87.9	304.2	81.1	88.8	link
vit_large_patch14_clip_336.laion2b_ft_in1k	87.9	304.5	191.1	270.2	link
vit_huge_patch14_clip_224.laion2b_ft_in1k	87.6	632	167.4	139.4	link
vit_large_patch14_clip_224.laion2b_ft_in1k	87.3	304.2	81.1	88.8	link
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k	87.2	86.9	55.5	101.6	link
vit_base_patch16_clip_384.openai_ft_in12k_in1k	87	86.9	55.5	101.6	link
vit_base_patch16_clip_384.laion2b_ft_in1k	86.6	86.9	55.5	101.6	link
vit_base_patch16_clip_384.openai_ft_in1k	86.2	86.9	55.5	101.6	link
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k	86.2	86.6	17.6	23.9	link
vit_base_patch16_clip_224.openai_ft_in12k_in1k	85.9	86.6	17.6	23.9	link
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k	85.8	88.3	17.9	23.9	link
vit_base_patch16_clip_224.laion2b_ft_in1k	85.5	86.6	17.6	23.9	link
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k	85.4	88.3	13.1	16.5	link
vit_base_patch16_clip_224.openai_ft_in1k	85.3	86.6	17.6	23.9	link
vit_base_patch32_clip_384.openai_ft_in12k_in1k	85.2	88.3	13.1	16.5	link
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k	83.3	88.2	4.4	5	link
vit_base_patch32_clip_224.laion2b_ft_in1k	82.6	88.2	4.4	5	link
vit_base_patch32_clip_224.openai_ft_in1k	81.9	88.2	4.4	5	link

• 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

model	top1	param_count	gmac	macts	hub
maxvit_xlarge_tf_512.in21k_ft_in1k	88.5	475.8	534.1	1413.2	link
maxvit_xlarge_tf_384.in21k_ft_in1k	88.3	475.3	292.8	668.8	[link](https://huggingface.co/timm/maxvit_xlar...

v0.6.12 Release

Minor bug fixes to HF push_to_hub, plus some more MaxVit weights

Oct 10, 2022

• More weights in maxxvit series, incl first ConvNeXt block based coatnext and maxxvit experiments:
  • coatnext_nano_rw_224 - 82.0 @ 224 (G) -- (uses ConvNeXt conv block, no BatchNorm)
  • maxxvit_rmlp_nano_rw_256 - 83.0 @ 256, 83.7 @ 320 (G) (uses ConvNeXt conv block, no BN)
  • maxvit_rmlp_small_rw_224 - 84.5 @ 224, 85.1 @ 320 (G)
  • maxxvit_rmlp_small_rw_256 - 84.6 @ 256, 84.9 @ 288 (G) -- could be trained better, hparams need tuning (uses ConvNeXt block, no BN)
  • coatnet_rmlp_2_rw_224 - 84.6 @ 224, 85 @ 320 (T)

v0.6.11 Release

Changes Since 0.6.7

Sept 23, 2022

• CLIP LAION-2B pretrained B/32, L/14, H/14, and g/14 image tower weights as vit models (for fine-tune)

Sept 7, 2022

• Hugging Face timm docs home now exists, look for more here in the future
• Add BEiT-v2 weights for base and large 224x224 models from https://github.com/microsoft/unilm/tree/master/beit2
• Add more weights in maxxvit series incl a pico (7.5M params, 1.9 GMACs), two tiny variants:
  • maxvit_rmlp_pico_rw_256 - 80.5 @ 256, 81.3 @ 320 (T)
  • maxvit_tiny_rw_224 - 83.5 @ 224 (G)
  • maxvit_rmlp_tiny_rw_256 - 84.2 @ 256, 84.8 @ 320 (T)

Aug 29, 2022

• MaxVit window size scales with img_size by default. Add new RelPosMlp MaxViT weight that leverages this:
  • maxvit_rmlp_nano_rw_256 - 83.0 @ 256, 83.6 @ 320 (T)

Aug 26, 2022

• CoAtNet (https://arxiv.org/abs/2106.04803) and MaxVit (https://arxiv.org/abs/2204.01697) timm original models
  • both found in maxxvit.py model def, contains numerous experiments outside scope of original papers
  • an unfinished Tensorflow version from MaxVit authors can be found https://github.com/google-research/maxvit
• Initial CoAtNet and MaxVit timm pretrained weights (working on more):
  • coatnet_nano_rw_224 - 81.7 @ 224 (T)
  • coatnet_rmlp_nano_rw_224 - 82.0 @ 224, 82.8 @ 320 (T)
  • coatnet_0_rw_224 - 82.4 (T) -- NOTE timm '0' coatnets have 2 more 3rd stage blocks
  • coatnet_bn_0_rw_224 - 82.4 (T)
  • maxvit_nano_rw_256 - 82.9 @ 256 (T)
  • coatnet_rmlp_1_rw_224 - 83.4 @ 224, 84 @ 320 (T)
  • coatnet_1_rw_224 - 83.6 @ 224 (G)
  • (T) = TPU trained with bits_and_tpu branch training code, (G) = GPU trained
• GCVit (weights adapted from https://github.com/NVlabs/GCVit, code 100% timm re-write for license purposes)
• MViT-V2 (multi-scale vit, adapted from https://github.com/facebookresearch/mvit)
• EfficientFormer (adapted from https://github.com/snap-research/EfficientFormer)
• PyramidVisionTransformer-V2 (adapted from https://github.com/whai362/PVT)
• 'Fast Norm' support for LayerNorm and GroupNorm that avoids float32 upcast w/ AMP (uses APEX LN if available for further boost)

Aug 15, 2022

• ConvNeXt atto weights added
  • convnext_atto - 75.7 @ 224, 77.0 @ 288
  • convnext_atto_ols - 75.9 @ 224, 77.2 @ 288

Aug 5, 2022

• More custom ConvNeXt smaller model defs with weights
  • convnext_femto - 77.5 @ 224, 78.7 @ 288
  • convnext_femto_ols - 77.9 @ 224, 78.9 @ 288
  • convnext_pico - 79.5 @ 224, 80.4 @ 288
  • convnext_pico_ols - 79.5 @ 224, 80.5 @ 288
  • convnext_nano_ols - 80.9 @ 224, 81.6 @ 288
• Updated EdgeNeXt to improve ONNX export, add new base variant and weights from original (https://github.com/mmaaz60/EdgeNeXt)

July 28, 2022

• Add freshly minted DeiT-III Medium (width=512, depth=12, num_heads=8) model weights. Thanks Hugo Touvron!

MaxxVit (CoAtNet, MaxVit, and related experimental weights)

CoAtNet (https://arxiv.org/abs/2106.04803) and MaxVit (https://arxiv.org/abs/2204.01697) timm trained weights

Weights were created reproducing the paper architectures and exploring timm sepcific additions such as ConvNeXt blocks, parallel partitioning, and other experiments.

Weights were trained on a mix of TPU and GPU systems. Bulk of weights were trained on TPU via the TRC program (https://sites.research.google/trc/about/).

CoAtNet variants run particularly well on TPU, it's a great combination. MaxVit is better suited to GPU due to the window partitioning, although there are some optimizations that can be made to improve TPU padding/utilization incl using 256x256 image size (8, 8) windo/grid size, and keeping format in NCHW for partition attention when using PyTorch XLA.

Glossary:

• coatnet - CoAtNet (MBConv + transformer blocks)
• coatnext - CoAtNet w/ ConvNeXt conv blocks
• maxvit - MaxViT (MBConv + block (ala swin) and grid partioning transformer blocks)
• maxxvit - MaxViT w/ ConvNeXt conv blocks
• rmlp - relative position embedding w/ MLP (can be resized) -- if this isn't in model name, it's using relative position bias (ala swin)
• rw - my variations on the model, slight differences in sizing / pooling / etc from Google paper spec

Results:

• maxvit_rmlp_pico_rw_256 - 80.5 @ 256, 81.3 @ 320 (T)
• coatnet_nano_rw_224 - 81.7 @ 224 (T)
• coatnext_nano_rw_224 - 82.0 @ 224 (G) -- (uses convnext block, no BatchNorm)
• coatnet_rmlp_nano_rw_224 - 82.0 @ 224, 82.8 @ 320 (T)
• coatnet_0_rw_224 - 82.4 (T) -- NOTE timm '0' coatnets have 2 more 3rd stage blocks
• coatnet_bn_0_rw_224 - 82.4 (T) -- all BatchNorm, no LayerNorm
• maxvit_nano_rw_256 - 82.9 @ 256 (T)
• maxvit_rmlp_nano_rw_256 - 83.0 @ 256, 83.6 @ 320 (T)
• maxxvit_rmlp_nano_rw_256 - 83.0 @ 256, 83.7 @ 320 (G) (uses convnext conv block, no BatchNorm)
• coatnet_rmlp_1_rw_224 - 83.4 @ 224, 84 @ 320 (T)
• maxvit_tiny_rw_224 - 83.5 @ 224 (G)
• coatnet_1_rw_224 - 83.6 @ 224 (G)
• maxvit_rmlp_tiny_rw_256 - 84.2 @ 256, 84.8 @ 320 (T)
• maxvit_rmlp_small_rw_224 - 84.5 @ 224, 85.1 @ 320 (G)
• maxxvit_rmlp_small_rw_256 - 84.6 @ 256, 84.9 @ 288 (G) -- could be trained better, hparms need tuning (uses convnext conv block, no BN)
• coatnet_rmlp_2_rw_224 - 84.6 @ 224, 85 @ 320 (T)

(T) = TPU trained with bits_and_tpu branch training code, (G) = GPU trained

More 3rd party ViT / ViT-hybrid weights

More weights for 3rd party ViT / ViT-CNN hybrids that needed remapping / re-hosting

EfficientFormer

Rehosted and remaped checkpoints from https://github.com/snap-research/EfficientFormer (originals in Google Drive)

GCViT

Heavily remaped from originals at https://github.com/NVlabs/GCVit due to from-scratch re-write of model code

NOTE: these checkpoints have a non-commercial CC-BY-NC-SA-4.0 license.

v0.6.7 Release

Minor bug fixes and a few more weights since 0.6.5

• A few more weights & model defs added:
  • darknetaa53 - 79.8 @ 256, 80.5 @ 288
  • convnext_nano - 80.8 @ 224, 81.5 @ 288
  • cs3sedarknet_l - 81.2 @ 256, 81.8 @ 288
  • cs3darknet_x - 81.8 @ 256, 82.2 @ 288
  • cs3sedarknet_x - 82.2 @ 256, 82.7 @ 288
  • cs3edgenet_x - 82.2 @ 256, 82.7 @ 288
  • cs3se_edgenet_x - 82.8 @ 256, 83.5 @ 320
• cs3* weights above all trained on TPU w/ bits_and_tpu branch. Thanks to TRC program!
• Add output_stride=8 and 16 support to ConvNeXt (dilation)
• deit3 models not being able to resize pos_emb fixed