Release v1.0.9

Aug 21, 2024

• Updated SBB ViT models trained on ImageNet-12k and fine-tuned on ImageNet-1k, challenging quite a number of much larger, slower models

model	top1	top5	param_count	img_size
vit_mediumd_patch16_reg4_gap_384.sbb2_e200_in12k_ft_in1k	87.438	98.256	64.11	384
vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k_ft_in1k	86.608	97.934	64.11	256
vit_betwixt_patch16_reg4_gap_384.sbb2_e200_in12k_ft_in1k	86.594	98.02	60.4	384
vit_betwixt_patch16_reg4_gap_256.sbb2_e200_in12k_ft_in1k	85.734	97.61	60.4	256

• MobileNet-V1 1.25, EfficientNet-B1, & ResNet50-D weights w/ MNV4 baseline challenge recipe

model	top1	top5	param_count	img_size
resnet50d.ra4_e3600_r224_in1k	81.838	95.922	25.58	288
efficientnet_b1.ra4_e3600_r240_in1k	81.440	95.700	7.79	288
resnet50d.ra4_e3600_r224_in1k	80.952	95.384	25.58	224
efficientnet_b1.ra4_e3600_r240_in1k	80.406	95.152	7.79	240
mobilenetv1_125.ra4_e3600_r224_in1k	77.600	93.804	6.27	256
mobilenetv1_125.ra4_e3600_r224_in1k	76.924	93.234	6.27	224

• Add SAM2 (HieraDet) backbone arch & weight loading support

• Add Hiera Small weights trained w/ abswin pos embed on in12k & fine-tuned on 1k

model	top1	top5	param_count
hiera_small_abswin_256.sbb2_e200_in12k_ft_in1k	84.912	97.260	35.01
hiera_small_abswin_256.sbb2_pd_e200_in12k_ft_in1k	84.560	97.106	35.01

Aug 8, 2024

• Add RDNet ('DenseNets Reloaded', https://arxiv.org/abs/2403.19588), thanks Donghyun Kim

Release v1.0.8

July 28, 2024

• Add mobilenet_edgetpu_v2_m weights w/ ra4 mnv4-small based recipe. 80.1% top-1 @ 224 and 80.7 @ 256.
• Release 1.0.8

July 26, 2024

• More MobileNet-v4 weights, ImageNet-12k pretrain w/ fine-tunes, and anti-aliased ConvLarge models

model	top1	top1_err	top5	top5_err	param_count	img_size
mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k	84.99	15.01	97.294	2.706	32.59	544
mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k	84.772	15.228	97.344	2.656	32.59	480
mobilenetv4_conv_aa_large.e230_r448_in12k_ft_in1k	84.64	15.36	97.114	2.886	32.59	448
mobilenetv4_conv_aa_large.e230_r384_in12k_ft_in1k	84.314	15.686	97.102	2.898	32.59	384
mobilenetv4_conv_aa_large.e600_r384_in1k	83.824	16.176	96.734	3.266	32.59	480
mobilenetv4_conv_aa_large.e600_r384_in1k	83.244	16.756	96.392	3.608	32.59	384
mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k	82.99	17.01	96.67	3.33	11.07	320
mobilenetv4_hybrid_medium.e200_r256_in12k_ft_in1k	82.364	17.636	96.256	3.744	11.07	256

• Impressive MobileNet-V1 and EfficientNet-B0 baseline challenges (https://huggingface.co/blog/rwightman/mobilenet-baselines)

model	top1	top1_err	top5	top5_err	param_count	img_size
efficientnet_b0.ra4_e3600_r224_in1k	79.364	20.636	94.754	5.246	5.29	256
efficientnet_b0.ra4_e3600_r224_in1k	78.584	21.416	94.338	5.662	5.29	224
mobilenetv1_100h.ra4_e3600_r224_in1k	76.596	23.404	93.272	6.728	5.28	256
mobilenetv1_100.ra4_e3600_r224_in1k	76.094	23.906	93.004	6.996	4.23	256
mobilenetv1_100h.ra4_e3600_r224_in1k	75.662	24.338	92.504	7.496	5.28	224
mobilenetv1_100.ra4_e3600_r224_in1k	75.382	24.618	92.312	7.688	4.23	224

• Prototype of set_input_size() added to vit and swin v1/v2 models to allow changing image size, patch size, window size after model creation.
• Improved support in swin for different size handling, in addition to set_input_size, always_partition and strict_img_size args have been added to __init__ to allow more flexible input size constraints
• Fix out of order indices info for intermediate 'Getter' feature wrapper, check out or range indices for same.
• Add several tiny < .5M param models for testing that are actually trained on ImageNet-1k

model	top1	top1_err	top5	top5_err	param_count	img_size	crop_pct
test_efficientnet.r160_in1k	47.156	52.844	71.726	28.274	0.36	192	1.0
test_byobnet.r160_in1k	46.698	53.302	71.674	28.326	0.46	192	1.0
test_efficientnet.r160_in1k	46.426	53.574	70.928	29.072	0.36	160	0.875
test_byobnet.r160_in1k	45.378	54.622	70.572	29.428	0.46	160	0.875
test_vit.r160_in1k	42.0	58.0	68.664	31.336	0.37	192	1.0
test_vit.r160_in1k	40.822	59.178	67.212	32.788	0.37	160	0.875

• Fix vit reg token init, thanks Promisery
• Other misc fixes

June 24, 2024

• 3 more MobileNetV4 hyrid weights with different MQA weight init scheme

model	top1	top1_err	top5	top5_err	param_count	img_size
mobilenetv4_hybrid_large.ix_e600_r384_in1k	84.356	15.644	96.892	3.108	37.76	448
mobilenetv4_hybrid_large.ix_e600_r384_in1k	83.990	16.010	96.702	3.298	37.76	384
mobilenetv4_hybrid_medium.ix_e550_r384_in1k	83.394	16.606	96.760	3.240	11.07	448
mobilenetv4_hybrid_medium.ix_e550_r384_in1k	82.968	17.032	96.474	3.526	11.07	384
mobilenetv4_hybrid_medium.ix_e550_r256_in1k	82.492	17.508	96.278	3.722	11.07	320
mobilenetv4_hybrid_medium.ix_e550_r256_in1k	81.446	18.554	95.704	4.296	11.07	256

• florence2 weight loading in DaViT model

Release v1.0.7

June 12, 2024

• MobileNetV4 models and initial set of timm trained weights added:

model	top1	top1_err	top5	top5_err	param_count	img_size
mobilenetv4_hybrid_large.e600_r384_in1k	84.266	15.734	96.936	3.064	37.76	448
mobilenetv4_hybrid_large.e600_r384_in1k	83.800	16.200	96.770	3.230	37.76	384
mobilenetv4_conv_large.e600_r384_in1k	83.392	16.608	96.622	3.378	32.59	448
mobilenetv4_conv_large.e600_r384_in1k	82.952	17.048	96.266	3.734	32.59	384
mobilenetv4_conv_large.e500_r256_in1k	82.674	17.326	96.31	3.69	32.59	320
mobilenetv4_conv_large.e500_r256_in1k	81.862	18.138	95.69	4.31	32.59	256
mobilenetv4_hybrid_medium.e500_r224_in1k	81.276	18.724	95.742	4.258	11.07	256
mobilenetv4_conv_medium.e500_r256_in1k	80.858	19.142	95.768	4.232	9.72	320
mobilenetv4_hybrid_medium.e500_r224_in1k	80.442	19.558	95.38	4.62	11.07	224
mobilenetv4_conv_blur_medium.e500_r224_in1k	80.142	19.858	95.298	4.702	9.72	256
mobilenetv4_conv_medium.e500_r256_in1k	79.928	20.072	95.184	4.816	9.72	256
mobilenetv4_conv_medium.e500_r224_in1k	79.808	20.192	95.186	4.814	9.72	256
mobilenetv4_conv_blur_medium.e500_r224_in1k	79.438	20.562	94.932	5.068	9.72	224
mobilenetv4_conv_medium.e500_r224_in1k	79.094	20.906	94.77	5.23	9.72	224
mobilenetv4_conv_small.e2400_r224_in1k	74.616	25.384	92.072	7.928	3.77	256
mobilenetv4_conv_small.e1200_r224_in1k	74.292	25.708	92.116	7.884	3.77	256
mobilenetv4_conv_small.e2400_r224_in1k	73.756	26.244	91.422	8.578	3.77	224
mobilenetv4_conv_small.e1200_r224_in1k	73.454	26.546	91.34	8.66	3.77	224

• Apple MobileCLIP (https://arxiv.org/pdf/2311.17049, FastViT and ViT-B) image tower model support & weights added (part of OpenCLIP support).
• ViTamin (https://arxiv.org/abs/2404.02132) CLIP image tower model & weights added (part of OpenCLIP support).
• OpenAI CLIP Modified ResNet image tower modelling & weight support (via ByobNet). Refactor AttentionPool2d.
• Refactoring & improvements, especially related to classifier_reset and num_features vs head_hidden_size for forward_features() vs pre_logits

Release v1.0.3

May 14, 2024

• Support loading PaliGemma jax weights into SigLIP ViT models with average pooling.
• Add Hiera models from Meta (https://github.com/facebookresearch/hiera).
• Add normalize= flag for transorms, return non-normalized torch.Tensor with original dytpe (for chug)
• Version 1.0.3 release

May 11, 2024

• Searching for Better ViT Baselines (For the GPU Poor) weights and vit variants released. Exploring model shapes between Tiny and Base.

model	top1	top5	param_count	img_size
vit_mediumd_patch16_reg4_gap_256.sbb_in12k_ft_in1k	86.202	97.874	64.11	256
vit_betwixt_patch16_reg4_gap_256.sbb_in12k_ft_in1k	85.418	97.48	60.4	256
vit_mediumd_patch16_rope_reg1_gap_256.sbb_in1k	84.322	96.812	63.95	256
vit_betwixt_patch16_rope_reg4_gap_256.sbb_in1k	83.906	96.684	60.23	256
vit_base_patch16_rope_reg1_gap_256.sbb_in1k	83.866	96.67	86.43	256
vit_medium_patch16_rope_reg1_gap_256.sbb_in1k	83.81	96.824	38.74	256
vit_betwixt_patch16_reg4_gap_256.sbb_in1k	83.706	96.616	60.4	256
vit_betwixt_patch16_reg1_gap_256.sbb_in1k	83.628	96.544	60.4	256
vit_medium_patch16_reg4_gap_256.sbb_in1k	83.47	96.622	38.88	256
vit_medium_patch16_reg1_gap_256.sbb_in1k	83.462	96.548	38.88	256
vit_little_patch16_reg4_gap_256.sbb_in1k	82.514	96.262	22.52	256
vit_wee_patch16_reg1_gap_256.sbb_in1k	80.256	95.360	13.42	256
vit_pwee_patch16_reg1_gap_256.sbb_in1k	80.072	95.136	15.25	256
vit_mediumd_patch16_reg4_gap_256.sbb_in12k	N/A	N/A	64.11	256
vit_betwixt_patch16_reg4_gap_256.sbb_in12k	N/A	N/A	60.4	256

• AttentionExtract helper added to extract attention maps from timm models. See example in #1232 (comment)
• forward_intermediates() API refined and added to more models including some ConvNets that have other extraction methods.
• 1017 of 1047 model architectures support features_only=True feature extraction. Remaining 34 architectures can be supported but based on priority requests.
• Remove torch.jit.script annotated functions including old JIT activations. Conflict with dynamo and dynamo does a much better job when used.

April 11, 2024

• Prepping for a long overdue 1.0 release, things have been stable for a while now.
• Significant feature that's been missing for a while, features_only=True support for ViT models with flat hidden states or non-std module layouts (so far covering 'vit_*', 'twins_*', 'deit*', 'beit*', 'mvitv2*', 'eva*', 'samvit_*', 'flexivit*')
• Above feature support achieved through a new forward_intermediates() API that can be used with a feature wrapping module or direclty.

model = timm.create_model('vit_base_patch16_224')
final_feat, intermediates = model.forward_intermediates(input) 
output = model.forward_head(final_feat)  # pooling + classifier head

print(final_feat.shape)
torch.Size([2, 197, 768])

for f in intermediates:
    print(f.shape)
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])
torch.Size([2, 768, 14, 14])

print(output.shape)
torch.Size([2, 1000])

model = timm.create_model('eva02_base_patch16_clip_224', pretrained=True, img_size=512, features_only=True, out_indices=(-3, -2,))
output = model(torch.randn(2, 3, 512, 512))

for o in output:    
    print(o.shape)   
torch.Size([2, 768, 32, 32])
torch.Size([2, 768, 32, 32])

• TinyCLIP vision tower weights added, thx Thien Tran

Release v0.9.16

Feb 19, 2024

• Next-ViT models added. Adapted from https://github.com/bytedance/Next-ViT
• HGNet and PP-HGNetV2 models added. Adapted from https://github.com/PaddlePaddle/PaddleClas by SeeFun
• Removed setup.py, moved to pyproject.toml based build supported by PDM
• Add updated model EMA impl using _for_each for less overhead
• Support device args in train script for non GPU devices
• Other misc fixes and small additions
• Min supported Python version increased to 3.8
• Release 0.9.16

Jan 8, 2024

Datasets & transform refactoring

• HuggingFace streaming (iterable) dataset support (--dataset hfids:org/dataset)
• Webdataset wrapper tweaks for improved split info fetching, can auto fetch splits from supported HF hub webdataset
• Tested HF datasets and webdataset wrapper streaming from HF hub with recent timm ImageNet uploads to https://huggingface.co/timm
• Make input & target column/field keys consistent across datasets and pass via args
• Full monochrome support when using e:g: --input-size 1 224 224 or --in-chans 1, sets PIL image conversion appropriately in dataset
• Improved several alternate crop & resize transforms (ResizeKeepRatio, RandomCropOrPad, etc) for use in PixParse document AI project
• Add SimCLR style color jitter prob along with grayscale and gaussian blur options to augmentations and args
• Allow train without validation set (--val-split '') in train script
• Add --bce-sum (sum over class dim) and --bce-pos-weight (positive weighting) args for training as they're common BCE loss tweaks I was often hard coding

Release v0.9.12

Nov 23, 2023

• Added EfficientViT-Large models, thanks SeeFun
• Fix Python 3.7 compat, will be dropping support for it soon
• Other misc fixes
• Release 0.9.12

Release v0.9.11

Nov 20, 2023

• Added significant flexibility for Hugging Face Hub based timm models via model_args config entry. model_args will be passed as kwargs through to models on creation.
  • See example at https://huggingface.co/gaunernst/vit_base_patch16_1024_128.audiomae_as2m_ft_as20k/blob/main/config.json
  • Usage: #2035
• Updated imagenet eval and test set csv files with latest models
• vision_transformer.py typing and doc cleanup by Laureηt
• 0.9.11 release

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