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huggingface/pytorch-image-models

版本发布时间: 2024-05-16 02:19:44

huggingface/pytorch-image-models最新发布版本:v1.0.9(2024-08-24 07:42:07)

May 14, 2024

May 11, 2024

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

April 11, 2024

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])

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