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 https://github.com/huggingface/pytorch-image-models/discussions/1232#discussioncomment-9320949
• 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