encoders_timm.rst

🪐 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: 549

Note

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

Encoder name	Support dilation
SelecSls42
SelecSls42b
SelecSls60
SelecSls60b
SelecSls84
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	✅
cspresnet50	✅
cspresnet50d	✅
cspresnet50w	✅
cspresnext50	✅
densenet121
densenet161
densenet169
densenet201
densenet264d
densenetblur121d
dla102
dla102x
dla102x2
dla169
dla34
dla46_c
dla46x_c
dla60
dla60_res2net
dla60_res2next
dla60x
dla60x_c
dm_nfnet_f0	✅
dm_nfnet_f1	✅
dm_nfnet_f2	✅
dm_nfnet_f3	✅
dm_nfnet_f4	✅
dm_nfnet_f5	✅
dm_nfnet_f6	✅
dpn107
dpn131
dpn48b
dpn68
dpn68b
dpn92
dpn98
eca_botnext26ts_256	✅
eca_halonext26ts	✅
eca_nfnet_l0	✅
eca_nfnet_l1	✅
eca_nfnet_l2	✅
eca_nfnet_l3	✅
eca_resnet33ts	✅
eca_resnext26ts	✅
eca_vovnet39b
ecaresnet101d	✅
ecaresnet101d_pruned	✅
ecaresnet200d	✅
ecaresnet269d	✅
ecaresnet26t	✅
ecaresnet50d	✅
ecaresnet50d_pruned	✅
ecaresnet50t	✅
ecaresnetlight	✅
ecaresnext26t_32x4d	✅
ecaresnext50t_32x4d	✅
efficientnet_b0	✅
efficientnet_b0_g16_evos	✅
efficientnet_b0_g8_gn	✅
efficientnet_b0_gn	✅
efficientnet_b1	✅
efficientnet_b1_pruned	✅
efficientnet_b2	✅
efficientnet_b2_pruned	✅
efficientnet_b2a	✅
efficientnet_b3	✅
efficientnet_b3_g8_gn	✅
efficientnet_b3_gn	✅
efficientnet_b3_pruned	✅
efficientnet_b3a	✅
efficientnet_b4	✅
efficientnet_b5	✅
efficientnet_b6	✅
efficientnet_b7	✅
efficientnet_b8	✅
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
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
inception_resnet_v2
inception_v3
inception_v4
lambda_resnet26rpt_256	✅
lambda_resnet26t	✅
lambda_resnet50ts	✅
lamhalobotnet50ts_256	✅
lcnet_035	✅
lcnet_050	✅
lcnet_075	✅
lcnet_100	✅
lcnet_150	✅
legacy_senet154
legacy_seresnet101
legacy_seresnet152
legacy_seresnet18
legacy_seresnet34
legacy_seresnet50
legacy_seresnext101_32x4d
legacy_seresnext26_32x4d
legacy_seresnext50_32x4d
legacy_xception
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_a1	✅
mnasnet_b1	✅
mnasnet_small	✅
mobilenetv2_035	✅
mobilenetv2_050	✅
mobilenetv2_075	✅
mobilenetv2_100	✅
mobilenetv2_110d	✅
mobilenetv2_120d	✅
mobilenetv2_140	✅
mobilenetv3_large_075	✅
mobilenetv3_large_100	✅
mobilenetv3_rw	✅
mobilenetv3_small_050	✅
mobilenetv3_small_075	✅
mobilenetv3_small_100	✅
mobilevit_s	✅
mobilevit_xs	✅
mobilevit_xxs	✅
mobilevitv2_050	✅
mobilevitv2_075	✅
mobilevitv2_100	✅
mobilevitv2_125	✅
mobilevitv2_150	✅
mobilevitv2_175	✅
mobilevitv2_200	✅
nasnetalarge
nf_ecaresnet101	✅
nf_ecaresnet26	✅
nf_ecaresnet50	✅
nf_regnet_b0	✅
nf_regnet_b1	✅
nf_regnet_b2	✅
nf_regnet_b3	✅
nf_regnet_b4	✅
nf_regnet_b5	✅
nf_resnet101	✅
nf_resnet26	✅
nf_resnet50	✅
nf_seresnet101	✅
nf_seresnet26	✅
nf_seresnet50	✅
nfnet_f0	✅
nfnet_f1	✅
nfnet_f2	✅
nfnet_f3	✅
nfnet_f4	✅
nfnet_f5	✅
nfnet_f6	✅
nfnet_f7	✅
nfnet_l0	✅
pnasnet5large
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_1280	✅
regnety_160	✅
regnety_2560	✅
regnety_320	✅
regnety_640	✅
regnetz_005	✅
regnetz_040	✅
regnetz_040_h	✅
regnetz_b16	✅
regnetz_b16_evos	✅
regnetz_c16	✅
regnetz_c16_evos	✅
regnetz_d32	✅
regnetz_d8	✅
regnetz_d8_evos	✅
regnetz_e8	✅
repvgg_a2	✅
repvgg_b0	✅
repvgg_b1	✅
repvgg_b1g4	✅
repvgg_b2	✅
repvgg_b2g4	✅
repvgg_b3	✅
repvgg_b3g4	✅
res2net101_26w_4s	✅
res2net101d	✅
res2net50_14w_8s	✅
res2net50_26w_4s	✅
res2net50_26w_6s	✅
res2net50_26w_8s	✅
res2net50_48w_2s	✅
res2net50d	✅
res2next50	✅
resnest101e	✅
resnest14d	✅
resnest200e	✅
resnest269e	✅
resnest26d	✅
resnest50d	✅
resnest50d_1s4x24d	✅
resnest50d_4s2x40d	✅
resnet101	✅
resnet101c	✅
resnet101d	✅
resnet101s	✅
resnet10t	✅
resnet14t	✅
resnet152	✅
resnet152c	✅
resnet152d	✅
resnet152s	✅
resnet18	✅
resnet18d	✅
resnet200	✅
resnet200d	✅
resnet26	✅
resnet26d	✅
resnet26t	✅
resnet32ts	✅
resnet33ts	✅
resnet34	✅
resnet34d	✅
resnet50	✅
resnet50_gn	✅
resnet50c	✅
resnet50d	✅
resnet50s	✅
resnet50t	✅
resnet51q	✅
resnet61q	✅
resnetaa101d	✅
resnetaa34d	✅
resnetaa50	✅
resnetaa50d	✅
resnetblur101d	✅
resnetblur18	✅
resnetblur50	✅
resnetblur50d	✅
resnetrs101	✅
resnetrs152	✅
resnetrs200	✅
resnetrs270	✅
resnetrs350	✅
resnetrs420	✅
resnetrs50	✅
resnetv2_101	✅
resnetv2_101d	✅
resnetv2_101x1_bit	✅
resnetv2_101x3_bit	✅
resnetv2_152	✅
resnetv2_152d	✅
resnetv2_152x2_bit	✅
resnetv2_152x4_bit	✅
resnetv2_50	✅
resnetv2_50d	✅
resnetv2_50d_evos	✅
resnetv2_50d_frn	✅
resnetv2_50d_gn	✅
resnetv2_50t	✅
resnetv2_50x1_bit	✅
resnetv2_50x3_bit	✅
resnext101_32x16d	✅
resnext101_32x32d	✅
resnext101_32x4d	✅
resnext101_32x8d	✅
resnext101_64x4d	✅
resnext26ts	✅
resnext50_32x4d	✅
resnext50d_32x4d	✅
rexnet_100	✅
rexnet_130	✅
rexnet_150	✅
rexnet_200	✅
rexnet_300	✅
rexnetr_100	✅
rexnetr_130	✅
rexnetr_150	✅
rexnetr_200	✅
rexnetr_300	✅
sebotnet33ts_256	✅
sehalonet33ts	✅
semnasnet_050	✅
semnasnet_075	✅
semnasnet_100	✅
semnasnet_140	✅
senet154	✅
seresnet101	✅
seresnet152	✅
seresnet152d	✅
seresnet18	✅
seresnet200d	✅
seresnet269d	✅
seresnet33ts	✅
seresnet34	✅
seresnet50	✅
seresnet50t	✅
seresnetaa50d	✅
seresnext101_32x4d	✅
seresnext101_32x8d	✅
seresnext101_64x4d	✅
seresnext101d_32x8d	✅
seresnext26d_32x4d	✅
seresnext26t_32x4d	✅
seresnext26tn_32x4d	✅
seresnext26ts	✅
seresnext50_32x4d	✅
seresnextaa101d_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	✅
vovnet39a
vovnet57a
wide_resnet101_2	✅
wide_resnet50_2	✅
xception41	✅
xception41p	✅
xception65	✅
xception65p	✅
xception71	✅