🎯 Timm Encoders

🎯 Timm Encoders#

Pytorch Image Models (a.k.a. timm) has a lot of pretrained models and interface which allows using these models as encoders in smp, however, not all models are supported

  • not all transformer models have features_only functionality implemented that is required for encoder

  • some models have inappropriate strides

Below is a table of suitable encoders (for DeepLabV3, DeepLabV3+, and PAN dilation support is needed also)

Total number of encoders: 812 (593+219)

Note

To use following encoders you have to add prefix tu-, e.g. tu-adv_inception_v3

Traditional-Style#

These models typically produce feature maps at the following downsampling scales relative to the input resolution: 1/2, 1/4, 1/8, 1/16, and 1/32

Encoder name

Support dilation

bat_resnext26ts

✅

botnet26t_256

✅

botnet50ts_256

✅

coatnet_0_224

coatnet_0_rw_224

coatnet_1_224

coatnet_1_rw_224

coatnet_2_224

coatnet_2_rw_224

coatnet_3_224

coatnet_3_rw_224

coatnet_4_224

coatnet_5_224

coatnet_bn_0_rw_224

coatnet_nano_cc_224

coatnet_nano_rw_224

coatnet_pico_rw_224

coatnet_rmlp_0_rw_224

coatnet_rmlp_1_rw2_224

coatnet_rmlp_1_rw_224

coatnet_rmlp_2_rw_224

coatnet_rmlp_2_rw_384

coatnet_rmlp_3_rw_224

coatnet_rmlp_nano_rw_224

coatnext_nano_rw_224

cs3darknet_focus_l

✅

cs3darknet_focus_m

✅

cs3darknet_focus_s

✅

cs3darknet_focus_x

✅

cs3darknet_l

✅

cs3darknet_m

✅

cs3darknet_s

✅

cs3darknet_x

✅

cs3edgenet_x

✅

cs3se_edgenet_x

✅

cs3sedarknet_l

✅

cs3sedarknet_x

✅

cs3sedarknet_xdw

✅

cspdarknet53

✅

cspresnet50

✅

cspresnet50d

✅

cspresnet50w

✅

cspresnext50

✅

darknet17

✅

darknet21

✅

darknet53

✅

darknetaa53

✅

densenet121

densenet161

densenet169

densenet201

densenet264d

densenetblur121d

dla34

dla46_c

dla46x_c

dla60

dla60_res2net

dla60_res2next

dla60x

dla60x_c

dla102

dla102x

dla102x2

dla169

dm_nfnet_f0

✅

dm_nfnet_f1

✅

dm_nfnet_f2

✅

dm_nfnet_f3

✅

dm_nfnet_f4

✅

dm_nfnet_f5

✅

dm_nfnet_f6

✅

dpn48b

dpn68

dpn68b

dpn92

dpn98

dpn107

dpn131

eca_botnext26ts_256

✅

eca_halonext26ts

✅

eca_nfnet_l0

✅

eca_nfnet_l1

✅

eca_nfnet_l2

✅

eca_nfnet_l3

✅

eca_resnet33ts

✅

eca_resnext26ts

✅

eca_vovnet39b

ecaresnet26t

✅

ecaresnet50d

✅

ecaresnet50d_pruned

✅

ecaresnet50t

✅

ecaresnet101d

✅

ecaresnet101d_pruned

✅

ecaresnet200d

✅

ecaresnet269d

✅

ecaresnetlight

✅

ecaresnext26t_32x4d

✅

ecaresnext50t_32x4d

✅

efficientnet_b0

✅

efficientnet_b0_g8_gn

✅

efficientnet_b0_g16_evos

✅

efficientnet_b0_gn

✅

efficientnet_b1

✅

efficientnet_b1_pruned

✅

efficientnet_b2

✅

efficientnet_b2_pruned

✅

efficientnet_b3

✅

efficientnet_b3_g8_gn

✅

efficientnet_b3_gn

✅

efficientnet_b3_pruned

✅

efficientnet_b4

✅

efficientnet_b5

✅

efficientnet_b6

✅

efficientnet_b7

✅

efficientnet_b8

✅

efficientnet_blur_b0

✅

efficientnet_cc_b0_4e

✅

efficientnet_cc_b0_8e

✅

efficientnet_cc_b1_8e

✅

efficientnet_el

✅

efficientnet_el_pruned

✅

efficientnet_em

✅

efficientnet_es

✅

efficientnet_es_pruned

✅

efficientnet_l2

✅

efficientnet_lite0

✅

efficientnet_lite1

✅

efficientnet_lite2

✅

efficientnet_lite3

✅

efficientnet_lite4

✅

efficientnetv2_l

✅

efficientnetv2_m

✅

efficientnetv2_rw_m

✅

efficientnetv2_rw_s

✅

efficientnetv2_rw_t

✅

efficientnetv2_s

✅

efficientnetv2_xl

✅

ese_vovnet19b_dw

ese_vovnet19b_slim

ese_vovnet19b_slim_dw

ese_vovnet39b

ese_vovnet39b_evos

ese_vovnet57b

ese_vovnet99b

fbnetc_100

✅

fbnetv3_b

✅

fbnetv3_d

✅

fbnetv3_g

✅

gc_efficientnetv2_rw_t

✅

gcresnet33ts

✅

gcresnet50t

✅

gcresnext26ts

✅

gcresnext50ts

✅

gernet_l

✅

gernet_m

✅

gernet_s

✅

ghostnet_050

ghostnet_100

ghostnet_130

ghostnetv2_100

ghostnetv2_130

ghostnetv2_160

halo2botnet50ts_256

✅

halonet26t

✅

halonet50ts

✅

halonet_h1

✅

haloregnetz_b

✅

hardcorenas_a

✅

hardcorenas_b

✅

hardcorenas_c

✅

hardcorenas_d

✅

hardcorenas_e

✅

hardcorenas_f

✅

hrnet_w18

hrnet_w18_small

hrnet_w18_small_v2

hrnet_w18_ssld

hrnet_w30

hrnet_w32

hrnet_w40

hrnet_w44

hrnet_w48

hrnet_w48_ssld

hrnet_w64

lambda_resnet26rpt_256

✅

lambda_resnet26t

✅

lambda_resnet50ts

✅

lamhalobotnet50ts_256

✅

lcnet_035

✅

lcnet_050

✅

lcnet_075

✅

lcnet_100

✅

lcnet_150

✅

legacy_senet154

legacy_seresnet18

legacy_seresnet34

legacy_seresnet50

legacy_seresnet101

legacy_seresnet152

legacy_seresnext26_32x4d

legacy_seresnext50_32x4d

legacy_seresnext101_32x4d

maxvit_base_tf_224

maxvit_base_tf_384

maxvit_base_tf_512

maxvit_large_tf_224

maxvit_large_tf_384

maxvit_large_tf_512

maxvit_nano_rw_256

maxvit_pico_rw_256

maxvit_rmlp_base_rw_224

maxvit_rmlp_base_rw_384

maxvit_rmlp_nano_rw_256

maxvit_rmlp_pico_rw_256

maxvit_rmlp_small_rw_224

maxvit_rmlp_small_rw_256

maxvit_rmlp_tiny_rw_256

maxvit_small_tf_224

maxvit_small_tf_384

maxvit_small_tf_512

maxvit_tiny_pm_256

maxvit_tiny_rw_224

maxvit_tiny_rw_256

maxvit_tiny_tf_224

maxvit_tiny_tf_384

maxvit_tiny_tf_512

maxvit_xlarge_tf_224

maxvit_xlarge_tf_384

maxvit_xlarge_tf_512

maxxvit_rmlp_nano_rw_256

maxxvit_rmlp_small_rw_256

maxxvit_rmlp_tiny_rw_256

maxxvitv2_nano_rw_256

maxxvitv2_rmlp_base_rw_224

maxxvitv2_rmlp_base_rw_384

maxxvitv2_rmlp_large_rw_224

mixnet_l

✅

mixnet_m

✅

mixnet_s

✅

mixnet_xl

✅

mixnet_xxl

✅

mnasnet_050

✅

mnasnet_075

✅

mnasnet_100

✅

mnasnet_140

✅

mnasnet_small

✅

mobilenet_edgetpu_100

✅

mobilenet_edgetpu_v2_l

✅

mobilenet_edgetpu_v2_m

✅

mobilenet_edgetpu_v2_s

✅

mobilenet_edgetpu_v2_xs

✅

mobilenetv1_100

✅

mobilenetv1_100h

✅

mobilenetv1_125

✅

mobilenetv2_035

✅

mobilenetv2_050

✅

mobilenetv2_075

✅

mobilenetv2_100

✅

mobilenetv2_110d

✅

mobilenetv2_120d

✅

mobilenetv2_140

✅

mobilenetv3_large_075

✅

mobilenetv3_large_100

✅

mobilenetv3_large_150d

✅

mobilenetv3_rw

✅

mobilenetv3_small_050

✅

mobilenetv3_small_075

✅

mobilenetv3_small_100

✅

mobilenetv4_conv_aa_large

✅

mobilenetv4_conv_aa_medium

✅

mobilenetv4_conv_blur_medium

✅

mobilenetv4_conv_large

✅

mobilenetv4_conv_medium

✅

mobilenetv4_conv_small

✅

mobilenetv4_conv_small_035

✅

mobilenetv4_conv_small_050

✅

mobilenetv4_hybrid_large

✅

mobilenetv4_hybrid_large_075

✅

mobilenetv4_hybrid_medium

✅

mobilenetv4_hybrid_medium_075

✅

mobileone_s0

✅

mobileone_s1

✅

mobileone_s2

✅

mobileone_s3

✅

mobileone_s4

✅

mobilevit_s

✅

mobilevit_xs

✅

mobilevit_xxs

✅

mobilevitv2_050

✅

mobilevitv2_075

✅

mobilevitv2_100

✅

mobilevitv2_125

✅

mobilevitv2_150

✅

mobilevitv2_175

✅

mobilevitv2_200

✅

nf_ecaresnet26

✅

nf_ecaresnet50

✅

nf_ecaresnet101

✅

nf_regnet_b0

✅

nf_regnet_b1

✅

nf_regnet_b2

✅

nf_regnet_b3

✅

nf_regnet_b4

✅

nf_regnet_b5

✅

nf_resnet26

✅

nf_resnet50

✅

nf_resnet101

✅

nf_seresnet26

✅

nf_seresnet50

✅

nf_seresnet101

✅

nfnet_f0

✅

nfnet_f1

✅

nfnet_f2

✅

nfnet_f3

✅

nfnet_f4

✅

nfnet_f5

✅

nfnet_f6

✅

nfnet_f7

✅

nfnet_l0

✅

regnetv_040

✅

regnetv_064

✅

regnetx_002

✅

regnetx_004

✅

regnetx_004_tv

✅

regnetx_006

✅

regnetx_008

✅

regnetx_016

✅

regnetx_032

✅

regnetx_040

✅

regnetx_064

✅

regnetx_080

✅

regnetx_120

✅

regnetx_160

✅

regnetx_320

✅

regnety_002

✅

regnety_004

✅

regnety_006

✅

regnety_008

✅

regnety_008_tv

✅

regnety_016

✅

regnety_032

✅

regnety_040

✅

regnety_040_sgn

✅

regnety_064

✅

regnety_080

✅

regnety_080_tv

✅

regnety_120

✅

regnety_160

✅

regnety_320

✅

regnety_640

✅

regnety_1280

✅

regnety_2560

✅

regnetz_005

✅

regnetz_040

✅

regnetz_040_h

✅

regnetz_b16

✅

regnetz_b16_evos

✅

regnetz_c16

✅

regnetz_c16_evos

✅

regnetz_d8

✅

regnetz_d8_evos

✅

regnetz_d32

✅

regnetz_e8

✅

repghostnet_050

repghostnet_058

repghostnet_080

repghostnet_100

repghostnet_111

repghostnet_130

repghostnet_150

repghostnet_200

repvgg_a0

✅

repvgg_a1

✅

repvgg_a2

✅

repvgg_b0

✅

repvgg_b1

✅

repvgg_b1g4

✅

repvgg_b2

✅

repvgg_b2g4

✅

repvgg_b3

✅

repvgg_b3g4

✅

repvgg_d2se

✅

res2net50_14w_8s

✅

res2net50_26w_4s

✅

res2net50_26w_6s

✅

res2net50_26w_8s

✅

res2net50_48w_2s

✅

res2net50d

✅

res2net101_26w_4s

✅

res2net101d

✅

res2next50

✅

resnest14d

✅

resnest26d

✅

resnest50d

✅

resnest50d_1s4x24d

✅

resnest50d_4s2x40d

✅

resnest101e

✅

resnest200e

✅

resnest269e

✅

resnet10t

✅

resnet14t

✅

resnet18

✅

resnet18d

✅

resnet26

✅

resnet26d

✅

resnet26t

✅

resnet32ts

✅

resnet33ts

✅

resnet34

✅

resnet34d

✅

resnet50

✅

resnet50_clip

✅

resnet50_clip_gap

✅

resnet50_gn

✅

resnet50_mlp

✅

resnet50c

✅

resnet50d

✅

resnet50s

✅

resnet50t

✅

resnet50x4_clip

✅

resnet50x4_clip_gap

✅

resnet50x16_clip

✅

resnet50x16_clip_gap

✅

resnet50x64_clip

✅

resnet50x64_clip_gap

✅

resnet51q

✅

resnet61q

✅

resnet101

✅

resnet101_clip

✅

resnet101_clip_gap

✅

resnet101c

✅

resnet101d

✅

resnet101s

✅

resnet152

✅

resnet152c

✅

resnet152d

✅

resnet152s

✅

resnet200

✅

resnet200d

✅

resnetaa34d

✅

resnetaa50

✅

resnetaa50d

✅

resnetaa101d

✅

resnetblur18

✅

resnetblur50

✅

resnetblur50d

✅

resnetblur101d

✅

resnetrs50

✅

resnetrs101

✅

resnetrs152

✅

resnetrs200

✅

resnetrs270

✅

resnetrs350

✅

resnetrs420

✅

resnetv2_18

✅

resnetv2_18d

✅

resnetv2_34

✅

resnetv2_34d

✅

resnetv2_50

✅

resnetv2_50d

✅

resnetv2_50d_evos

✅

resnetv2_50d_frn

✅

resnetv2_50d_gn

✅

resnetv2_50t

✅

resnetv2_50x1_bit

✅

resnetv2_50x3_bit

✅

resnetv2_101

✅

resnetv2_101d

✅

resnetv2_101x1_bit

✅

resnetv2_101x3_bit

✅

resnetv2_152

✅

resnetv2_152d

✅

resnetv2_152x2_bit

✅

resnetv2_152x4_bit

✅

resnext26ts

✅

resnext50_32x4d

✅

resnext50d_32x4d

✅

resnext101_32x4d

✅

resnext101_32x8d

✅

resnext101_32x16d

✅

resnext101_32x32d

✅

resnext101_64x4d

✅

rexnet_100

✅

rexnet_130

✅

rexnet_150

✅

rexnet_200

✅

rexnet_300

✅

rexnetr_100

✅

rexnetr_130

✅

rexnetr_150

✅

rexnetr_200

✅

rexnetr_300

✅

sebotnet33ts_256

✅

sedarknet21

✅

sehalonet33ts

✅

selecsls42

selecsls42b

selecsls60

selecsls60b

selecsls84

semnasnet_050

✅

semnasnet_075

✅

semnasnet_100

✅

semnasnet_140

✅

senet154

✅

seresnet18

✅

seresnet33ts

✅

seresnet34

✅

seresnet50

✅

seresnet50t

✅

seresnet101

✅

seresnet152

✅

seresnet152d

✅

seresnet200d

✅

seresnet269d

✅

seresnetaa50d

✅

seresnext26d_32x4d

✅

seresnext26t_32x4d

✅

seresnext26ts

✅

seresnext50_32x4d

✅

seresnext101_32x4d

✅

seresnext101_32x8d

✅

seresnext101_64x4d

✅

seresnext101d_32x8d

✅

seresnextaa101d_32x8d

✅

seresnextaa201d_32x8d

✅

skresnet18

✅

skresnet34

✅

skresnet50

✅

skresnet50d

✅

skresnext50_32x4d

✅

spnasnet_100

✅

tf_efficientnet_b0

✅

tf_efficientnet_b1

✅

tf_efficientnet_b2

✅

tf_efficientnet_b3

✅

tf_efficientnet_b4

✅

tf_efficientnet_b5

✅

tf_efficientnet_b6

✅

tf_efficientnet_b7

✅

tf_efficientnet_b8

✅

tf_efficientnet_cc_b0_4e

✅

tf_efficientnet_cc_b0_8e

✅

tf_efficientnet_cc_b1_8e

✅

tf_efficientnet_el

✅

tf_efficientnet_em

✅

tf_efficientnet_es

✅

tf_efficientnet_l2

✅

tf_efficientnet_lite0

✅

tf_efficientnet_lite1

✅

tf_efficientnet_lite2

✅

tf_efficientnet_lite3

✅

tf_efficientnet_lite4

✅

tf_efficientnetv2_b0

✅

tf_efficientnetv2_b1

✅

tf_efficientnetv2_b2

✅

tf_efficientnetv2_b3

✅

tf_efficientnetv2_l

✅

tf_efficientnetv2_m

✅

tf_efficientnetv2_s

✅

tf_efficientnetv2_xl

✅

tf_mixnet_l

✅

tf_mixnet_m

✅

tf_mixnet_s

✅

tf_mobilenetv3_large_075

✅

tf_mobilenetv3_large_100

✅

tf_mobilenetv3_large_minimal_100

✅

tf_mobilenetv3_small_075

✅

tf_mobilenetv3_small_100

✅

tf_mobilenetv3_small_minimal_100

✅

tinynet_a

✅

tinynet_b

✅

tinynet_c

✅

tinynet_d

✅

tinynet_e

✅

vgg11

vgg11_bn

vgg13

vgg13_bn

vgg16

vgg16_bn

vgg19

vgg19_bn

vovnet39a

vovnet57a

wide_resnet50_2

✅

wide_resnet101_2

✅

xception41

✅

xception41p

✅

xception65

✅

xception65p

✅

xception71

✅

Transformer-Style#

Transformer-style models (e.g., Swin Transformer, ConvNeXt) typically produce feature maps starting at a 1/4 scale, followed by 1/8, 1/16, and 1/32 scales

Encoder name

Support dilation

caformer_b36

caformer_m36

caformer_s18

caformer_s36

convformer_b36

convformer_m36

convformer_s18

convformer_s36

convnext_atto

✅

convnext_atto_ols

✅

convnext_atto_rms

✅

convnext_base

✅

convnext_femto

✅

convnext_femto_ols

✅

convnext_large

✅

convnext_large_mlp

✅

convnext_nano

✅

convnext_nano_ols

✅

convnext_pico

✅

convnext_pico_ols

✅

convnext_small

✅

convnext_tiny

✅

convnext_tiny_hnf

✅

convnext_xlarge

✅

convnext_xxlarge

✅

convnext_zepto_rms

✅

convnext_zepto_rms_ols

✅

convnextv2_atto

✅

convnextv2_base

✅

convnextv2_femto

✅

convnextv2_huge

✅

convnextv2_large

✅

convnextv2_nano

✅

convnextv2_pico

✅

convnextv2_small

✅

convnextv2_tiny

✅

davit_base

davit_base_fl

davit_giant

davit_huge

davit_huge_fl

davit_large

davit_small

davit_tiny

edgenext_base

edgenext_small

edgenext_small_rw

edgenext_x_small

edgenext_xx_small

efficientformer_l1

efficientformer_l3

efficientformer_l7

efficientformerv2_l

efficientformerv2_s0

efficientformerv2_s1

efficientformerv2_s2

efficientvit_b0

efficientvit_b1

efficientvit_b2

efficientvit_b3

efficientvit_l1

efficientvit_l2

efficientvit_l3

fastvit_ma36

fastvit_mci0

fastvit_mci1

fastvit_mci2

fastvit_s12

fastvit_sa12

fastvit_sa24

fastvit_sa36

fastvit_t8

fastvit_t12

focalnet_base_lrf

focalnet_base_srf

focalnet_huge_fl3

focalnet_huge_fl4

focalnet_large_fl3

focalnet_large_fl4

focalnet_small_lrf

focalnet_small_srf

focalnet_tiny_lrf

focalnet_tiny_srf

focalnet_xlarge_fl3

focalnet_xlarge_fl4

hgnet_base

hgnet_small

hgnet_tiny

hgnetv2_b0

hgnetv2_b1

hgnetv2_b2

hgnetv2_b3

hgnetv2_b4

hgnetv2_b5

hgnetv2_b6

hiera_base_224

hiera_base_abswin_256

hiera_base_plus_224

hiera_huge_224

hiera_large_224

hiera_small_224

hiera_small_abswin_256

hiera_tiny_224

hieradet_small

inception_next_base

inception_next_small

inception_next_tiny

mambaout_base

mambaout_base_plus_rw

mambaout_base_short_rw

mambaout_base_tall_rw

mambaout_base_wide_rw

mambaout_femto

mambaout_kobe

mambaout_small

mambaout_small_rw

mambaout_tiny

mvitv2_base

mvitv2_base_cls

mvitv2_huge_cls

mvitv2_large

mvitv2_large_cls

mvitv2_small

mvitv2_small_cls

mvitv2_tiny

nest_base

nest_base_jx

nest_small

nest_small_jx

nest_tiny

nest_tiny_jx

nextvit_base

nextvit_large

nextvit_small

poolformer_m36

poolformer_m48

poolformer_s12

poolformer_s24

poolformer_s36

poolformerv2_m36

poolformerv2_m48

poolformerv2_s12

poolformerv2_s24

poolformerv2_s36

pvt_v2_b0

pvt_v2_b1

pvt_v2_b2

pvt_v2_b2_li

pvt_v2_b3

pvt_v2_b4

pvt_v2_b5

rdnet_base

rdnet_large

rdnet_small

rdnet_tiny

repvit_m0_9

repvit_m1

repvit_m1_0

repvit_m1_1

repvit_m1_5

repvit_m2

repvit_m2_3

repvit_m3

sam2_hiera_base_plus

sam2_hiera_large

sam2_hiera_small

sam2_hiera_tiny

swin_base_patch4_window7_224

swin_base_patch4_window12_384

swin_large_patch4_window7_224

swin_large_patch4_window12_384

swin_s3_base_224

swin_s3_small_224

swin_s3_tiny_224

swin_small_patch4_window7_224

swin_tiny_patch4_window7_224

swinv2_base_window8_256

swinv2_base_window12_192

swinv2_base_window12to16_192to256

swinv2_base_window12to24_192to384

swinv2_base_window16_256

swinv2_cr_base_224

swinv2_cr_base_384

swinv2_cr_base_ns_224

swinv2_cr_giant_224

swinv2_cr_giant_384

swinv2_cr_huge_224

swinv2_cr_huge_384

swinv2_cr_large_224

swinv2_cr_large_384

swinv2_cr_small_224

swinv2_cr_small_384

swinv2_cr_small_ns_224

swinv2_cr_small_ns_256

swinv2_cr_tiny_224

swinv2_cr_tiny_384

swinv2_cr_tiny_ns_224

swinv2_large_window12_192

swinv2_large_window12to16_192to256

swinv2_large_window12to24_192to384

swinv2_small_window8_256

swinv2_small_window16_256

swinv2_tiny_window8_256

swinv2_tiny_window16_256

tiny_vit_5m_224

tiny_vit_11m_224

tiny_vit_21m_224

tiny_vit_21m_384

tiny_vit_21m_512

tresnet_l

tresnet_m

tresnet_v2_l

tresnet_xl

twins_pcpvt_base

twins_pcpvt_large

twins_pcpvt_small

twins_svt_base

twins_svt_large

twins_svt_small
Contents