Migrations
/
pytorch-image-models
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Update benchmark with latest model adds

Browse Source
pull/1606/head
Ross Wightman 2 years ago
parent 30bd1746c5
commit c865028c34
4 changed files with 854 additions and 838 deletions
Show Stats Download Patch File Download Diff File

4
results/benchmark-infer-amp-nchw-pt112-cu113-rtx3090.csv
Unescape View File

@ -300,6 +300,7 @@ seresnet50t,2333.68,438.78,1024,224,4.32,11.83,28.1
ecaresnet50d,2316.33,442.066,1024,224,4.35,11.93,25.58 ecaresnet50d,2316.33,442.066,1024,224,4.35,11.93,25.58
resnetblur50,2298.66,445.465,1024,224,5.16,12.02,25.56 resnetblur50,2298.66,445.465,1024,224,5.16,12.02,25.56
mobilevit_s,2279.76,336.866,768,256,2.03,19.94,5.58 mobilevit_s,2279.76,336.866,768,256,2.03,19.94,5.58
convnext_nano,2276.19,449.862,1024,288,4.06,13.84,15.59
resnetrs50,2276.18,449.864,1024,224,4.48,12.14,35.69 resnetrs50,2276.18,449.864,1024,224,4.48,12.14,35.69
vit_base_resnet26d_224,2262.15,452.654,1024,224,6.97,13.16,101.4 vit_base_resnet26d_224,2262.15,452.654,1024,224,6.97,13.16,101.4
gluon_resnet50_v1s,2257.16,453.655,1024,224,5.47,13.52,25.68 gluon_resnet50_v1s,2257.16,453.655,1024,224,5.47,13.52,25.68
@ -461,6 +462,7 @@ resnetv2_50d_evob,1383.3,740.244,1024,224,4.33,11.92,25.59
res2net50_26w_8s,1373.33,745.622,1024,224,8.37,17.95,48.4 res2net50_26w_8s,1373.33,745.622,1024,224,8.37,17.95,48.4
halo2botnet50ts_256,1372.33,559.619,768,256,5.02,21.78,22.64 halo2botnet50ts_256,1372.33,559.619,768,256,5.02,21.78,22.64
regnetx_080,1370.56,747.125,1024,224,8.02,14.06,39.57 regnetx_080,1370.56,747.125,1024,224,8.02,14.06,39.57
cs3sedarknet_x,1368.84,748.067,1024,288,10.6,14.37,35.4
jx_nest_tiny,1362.62,563.605,768,224,5.83,25.48,17.06 jx_nest_tiny,1362.62,563.605,768,224,5.83,25.48,17.06
convit_small,1355.18,755.603,1024,224,5.76,17.87,27.78 convit_small,1355.18,755.603,1024,224,5.76,17.87,27.78
res2net101_26w_4s,1353.43,756.586,1024,224,8.1,18.45,45.21 res2net101_26w_4s,1353.43,756.586,1024,224,8.1,18.45,45.21
@ -471,6 +473,7 @@ vgg16_bn,1335.02,383.503,512,224,15.5,13.56,138.37
mixer_b16_224,1328.05,771.041,1024,224,12.62,14.53,59.88 mixer_b16_224,1328.05,771.041,1024,224,12.62,14.53,59.88
twins_svt_base,1307.2,783.34,1024,224,8.59,26.33,56.07 twins_svt_base,1307.2,783.34,1024,224,8.59,26.33,56.07
dpn92,1299.67,787.878,1024,224,6.54,18.21,37.67 dpn92,1299.67,787.878,1024,224,6.54,18.21,37.67
cs3edgenet_x,1289.05,794.37,1024,288,14.59,16.36,47.82
ese_vovnet99b_iabn,1282.63,798.345,1024,224,16.49,11.27,63.2 ese_vovnet99b_iabn,1282.63,798.345,1024,224,16.49,11.27,63.2
crossvit_18_240,1272.74,804.553,1024,240,9.05,26.26,43.27 crossvit_18_240,1272.74,804.553,1024,240,9.05,26.26,43.27
regnety_040s_gn,1271.39,805.405,1024,224,4.03,12.29,20.65 regnety_040s_gn,1271.39,805.405,1024,224,4.03,12.29,20.65
@ -556,6 +559,7 @@ pit_b_distilled_224,985.16,519.696,512,224,12.5,33.07,74.79
tresnet_xl,983.38,1041.292,1024,224,15.17,15.34,78.44 tresnet_xl,983.38,1041.292,1024,224,15.17,15.34,78.44
efficientnetv2_s,976.0,1049.166,1024,384,8.44,35.77,21.46 efficientnetv2_s,976.0,1049.166,1024,384,8.44,35.77,21.46
dla102x2,973.1,526.138,512,224,9.34,29.91,41.28 dla102x2,973.1,526.138,512,224,9.34,29.91,41.28
cs3se_edgenet_x,972.26,1053.196,1024,320,18.01,20.21,50.72
vit_small_patch16_36x1_224,972.14,1053.329,1024,224,13.71,35.69,64.67 vit_small_patch16_36x1_224,972.14,1053.329,1024,224,13.71,35.69,64.67
swinv2_cr_small_224,966.28,1059.712,1024,224,9.07,50.27,49.7 swinv2_cr_small_224,966.28,1059.712,1024,224,9.07,50.27,49.7
swinv2_cr_small_ns_224,955.69,1071.465,1024,224,9.08,50.27,49.7 swinv2_cr_small_ns_224,955.69,1071.465,1024,224,9.08,50.27,49.7

1 model infer_samples_per_sec infer_step_time infer_batch_size infer_img_size infer_gmacs infer_macts param_count
300 ecaresnet50d 2316.33 442.066 1024 224 4.35 11.93 25.58
301 resnetblur50 2298.66 445.465 1024 224 5.16 12.02 25.56
302 mobilevit_s 2279.76 336.866 768 256 2.03 19.94 5.58
303 convnext_nano 2276.19 449.862 1024 288 4.06 13.84 15.59
304 resnetrs50 2276.18 449.864 1024 224 4.48 12.14 35.69
305 vit_base_resnet26d_224 2262.15 452.654 1024 224 6.97 13.16 101.4
306 gluon_resnet50_v1s 2257.16 453.655 1024 224 5.47 13.52 25.68
462 res2net50_26w_8s 1373.33 745.622 1024 224 8.37 17.95 48.4
463 halo2botnet50ts_256 1372.33 559.619 768 256 5.02 21.78 22.64
464 regnetx_080 1370.56 747.125 1024 224 8.02 14.06 39.57
465 cs3sedarknet_x 1368.84 748.067 1024 288 10.6 14.37 35.4
466 jx_nest_tiny 1362.62 563.605 768 224 5.83 25.48 17.06
467 convit_small 1355.18 755.603 1024 224 5.76 17.87 27.78
468 res2net101_26w_4s 1353.43 756.586 1024 224 8.1 18.45 45.21
473 mixer_b16_224 1328.05 771.041 1024 224 12.62 14.53 59.88
474 twins_svt_base 1307.2 783.34 1024 224 8.59 26.33 56.07
475 dpn92 1299.67 787.878 1024 224 6.54 18.21 37.67
476 cs3edgenet_x 1289.05 794.37 1024 288 14.59 16.36 47.82
477 ese_vovnet99b_iabn 1282.63 798.345 1024 224 16.49 11.27 63.2
478 crossvit_18_240 1272.74 804.553 1024 240 9.05 26.26 43.27
479 regnety_040s_gn 1271.39 805.405 1024 224 4.03 12.29 20.65
559 tresnet_xl 983.38 1041.292 1024 224 15.17 15.34 78.44
560 efficientnetv2_s 976.0 1049.166 1024 384 8.44 35.77 21.46
561 dla102x2 973.1 526.138 512 224 9.34 29.91 41.28
562 cs3se_edgenet_x 972.26 1053.196 1024 320 18.01 20.21 50.72
563 vit_small_patch16_36x1_224 972.14 1053.329 1024 224 13.71 35.69 64.67
564 swinv2_cr_small_224 966.28 1059.712 1024 224 9.07 50.27 49.7
565 swinv2_cr_small_ns_224 955.69 1071.465 1024 224 9.08 50.27 49.7

4
results/benchmark-infer-amp-nhwc-pt112-cu113-rtx3090.csv
Unescape View File

@ -339,6 +339,7 @@ convnext_tiny_in22ft1k,2380.94,430.068,1024,224,4.47,13.44,28.59
convnext_tiny,2379.5,430.329,1024,224,4.47,13.44,28.59 convnext_tiny,2379.5,430.329,1024,224,4.47,13.44,28.59
regnetz_b16,2348.93,435.929,1024,288,2.39,16.43,9.72 regnetz_b16,2348.93,435.929,1024,288,2.39,16.43,9.72
resnetv2_101,2321.74,441.036,1024,224,7.83,16.23,44.54 resnetv2_101,2321.74,441.036,1024,224,7.83,16.23,44.54
convnext_nano,2304.31,444.37,1024,288,4.06,13.84,15.59
tf_efficientnet_cc_b0_4e,2293.39,446.488,1024,224,0.41,9.42,13.31 tf_efficientnet_cc_b0_4e,2293.39,446.488,1024,224,0.41,9.42,13.31
semobilevit_s,2279.96,336.836,768,256,2.03,19.95,5.74 semobilevit_s,2279.96,336.836,768,256,2.03,19.95,5.74
gluon_resnet101_v1b,2249.95,455.108,1024,224,7.83,16.23,44.55 gluon_resnet101_v1b,2249.95,455.108,1024,224,7.83,16.23,44.55
@ -456,6 +457,7 @@ mobilevitv2_175,1554.07,329.447,512,256,5.54,28.13,14.25
swinv2_cr_tiny_ns_224,1551.84,659.847,1024,224,4.66,28.45,28.33 swinv2_cr_tiny_ns_224,1551.84,659.847,1024,224,4.66,28.45,28.33
mobilevitv2_175_in22ft1k,1551.58,329.973,512,256,5.54,28.13,14.25 mobilevitv2_175_in22ft1k,1551.58,329.973,512,256,5.54,28.13,14.25
vit_base_patch32_384,1550.87,660.263,1024,384,13.06,16.5,88.3 vit_base_patch32_384,1550.87,660.263,1024,384,13.06,16.5,88.3
cs3sedarknet_x,1549.02,661.048,1024,288,10.6,14.37,35.4
resnetv2_152d,1545.41,662.596,1024,224,11.8,23.36,60.2 resnetv2_152d,1545.41,662.596,1024,224,11.8,23.36,60.2
nf_ecaresnet101,1540.66,664.639,1024,224,8.01,16.27,44.55 nf_ecaresnet101,1540.66,664.639,1024,224,8.01,16.27,44.55
nf_seresnet101,1538.7,665.483,1024,224,8.02,16.27,49.33 nf_seresnet101,1538.7,665.483,1024,224,8.02,16.27,49.33
@ -466,6 +468,7 @@ gluon_resnet152_v1d,1508.62,678.753,1024,224,11.8,23.36,60.21
vgg16_bn,1504.44,340.315,512,224,15.5,13.56,138.37 vgg16_bn,1504.44,340.315,512,224,15.5,13.56,138.37
twins_pcpvt_base,1494.56,685.139,1024,224,6.68,25.25,43.83 twins_pcpvt_base,1494.56,685.139,1024,224,6.68,25.25,43.83
tf_efficientnet_el,1481.51,345.582,512,300,8.0,30.7,10.59 tf_efficientnet_el,1481.51,345.582,512,300,8.0,30.7,10.59
cs3edgenet_x,1479.52,692.101,1024,288,14.59,16.36,47.82
vit_base_r26_s32_224,1479.31,692.202,1024,224,6.81,12.36,101.38 vit_base_r26_s32_224,1479.31,692.202,1024,224,6.81,12.36,101.38
skresnext50_32x4d,1465.64,698.657,1024,224,4.5,17.18,27.48 skresnext50_32x4d,1465.64,698.657,1024,224,4.5,17.18,27.48
convnext_small,1452.43,705.012,1024,224,8.71,21.56,50.22 convnext_small,1452.43,705.012,1024,224,8.71,21.56,50.22
@ -537,6 +540,7 @@ dla60_res2next,1127.83,907.922,1024,224,3.49,13.17,17.03
eca_nfnet_l0,1126.64,908.88,1024,288,7.12,17.29,24.14 eca_nfnet_l0,1126.64,908.88,1024,288,7.12,17.29,24.14
resnetrs101,1123.2,911.667,1024,288,13.56,28.53,63.62 resnetrs101,1123.2,911.667,1024,288,13.56,28.53,63.62
nfnet_l0,1123.1,911.747,1024,288,7.13,17.29,35.07 nfnet_l0,1123.1,911.747,1024,288,7.13,17.29,35.07
cs3se_edgenet_x,1120.46,913.898,1024,320,18.01,20.21,50.72
deit_base_distilled_patch16_224,1119.36,914.798,1024,224,17.68,24.05,87.34 deit_base_distilled_patch16_224,1119.36,914.798,1024,224,17.68,24.05,87.34
vit_base_patch16_rpn_224,1111.53,921.235,1024,224,17.49,23.75,86.54 vit_base_patch16_rpn_224,1111.53,921.235,1024,224,17.49,23.75,86.54
inception_resnet_v2,1108.79,923.511,1024,299,13.18,25.06,55.84 inception_resnet_v2,1108.79,923.511,1024,299,13.18,25.06,55.84

1 model infer_samples_per_sec infer_step_time infer_batch_size infer_img_size infer_gmacs infer_macts param_count
339 convnext_tiny 2379.5 430.329 1024 224 4.47 13.44 28.59
340 regnetz_b16 2348.93 435.929 1024 288 2.39 16.43 9.72
341 resnetv2_101 2321.74 441.036 1024 224 7.83 16.23 44.54
342 convnext_nano 2304.31 444.37 1024 288 4.06 13.84 15.59
343 tf_efficientnet_cc_b0_4e 2293.39 446.488 1024 224 0.41 9.42 13.31
344 semobilevit_s 2279.96 336.836 768 256 2.03 19.95 5.74
345 gluon_resnet101_v1b 2249.95 455.108 1024 224 7.83 16.23 44.55
457 swinv2_cr_tiny_ns_224 1551.84 659.847 1024 224 4.66 28.45 28.33
458 mobilevitv2_175_in22ft1k 1551.58 329.973 512 256 5.54 28.13 14.25
459 vit_base_patch32_384 1550.87 660.263 1024 384 13.06 16.5 88.3
460 cs3sedarknet_x 1549.02 661.048 1024 288 10.6 14.37 35.4
461 resnetv2_152d 1545.41 662.596 1024 224 11.8 23.36 60.2
462 nf_ecaresnet101 1540.66 664.639 1024 224 8.01 16.27 44.55
463 nf_seresnet101 1538.7 665.483 1024 224 8.02 16.27 49.33
468 vgg16_bn 1504.44 340.315 512 224 15.5 13.56 138.37
469 twins_pcpvt_base 1494.56 685.139 1024 224 6.68 25.25 43.83
470 tf_efficientnet_el 1481.51 345.582 512 300 8.0 30.7 10.59
471 cs3edgenet_x 1479.52 692.101 1024 288 14.59 16.36 47.82
472 vit_base_r26_s32_224 1479.31 692.202 1024 224 6.81 12.36 101.38
473 skresnext50_32x4d 1465.64 698.657 1024 224 4.5 17.18 27.48
474 convnext_small 1452.43 705.012 1024 224 8.71 21.56 50.22
540 eca_nfnet_l0 1126.64 908.88 1024 288 7.12 17.29 24.14
541 resnetrs101 1123.2 911.667 1024 288 13.56 28.53 63.62
542 nfnet_l0 1123.1 911.747 1024 288 7.13 17.29 35.07
543 cs3se_edgenet_x 1120.46 913.898 1024 320 18.01 20.21 50.72
544 deit_base_distilled_patch16_224 1119.36 914.798 1024 224 17.68 24.05 87.34
545 vit_base_patch16_rpn_224 1111.53 921.235 1024 224 17.49 23.75 86.54
546 inception_resnet_v2 1108.79 923.511 1024 299 13.18 25.06 55.84

12
results/benchmark-train-amp-nchw-pt112-cu113-rtx3090.csv
Unescape View File

@ -145,6 +145,7 @@ crossvit_9_240,1260.08,303.33,384,240,8.55
convnext_nano_hnf,1235.34,413.703,512,224,15.59 convnext_nano_hnf,1235.34,413.703,512,224,15.59
convnext_nano_ols,1234.94,413.902,512,224,15.6 convnext_nano_ols,1234.94,413.902,512,224,15.6
poolformer_s12,1234.11,414.201,512,224,11.92 poolformer_s12,1234.11,414.201,512,224,11.92
convnext_nano,1233.61,414.261,512,224,15.59
resmlp_12_distilled_224,1232.37,414.645,512,224,15.35 resmlp_12_distilled_224,1232.37,414.645,512,224,15.35
resmlp_12_224,1232.04,414.762,512,224,15.35 resmlp_12_224,1232.04,414.762,512,224,15.35
fbnetv3_b,1226.89,415.617,512,224,8.6 fbnetv3_b,1226.89,415.617,512,224,8.6
@ -422,6 +423,7 @@ swinv2_cr_tiny_224,505.83,504.823,256,224,28.33
halonet50ts,503.76,380.122,192,256,22.73 halonet50ts,503.76,380.122,192,256,22.73
repvgg_b1,503.57,1015.623,512,224,57.42 repvgg_b1,503.57,1015.623,512,224,57.42
swinv2_cr_tiny_ns_224,502.5,508.144,256,224,28.33 swinv2_cr_tiny_ns_224,502.5,508.144,256,224,28.33
cs3sedarknet_x,501.96,508.547,256,256,35.4
dla102,497.28,513.14,256,224,33.27 dla102,497.28,513.14,256,224,33.27
wide_resnet50_2,495.68,773.85,384,224,68.88 wide_resnet50_2,495.68,773.85,384,224,68.88
res2net50_26w_6s,493.57,516.914,256,224,37.05 res2net50_26w_6s,493.57,516.914,256,224,37.05
@ -433,6 +435,7 @@ vit_relpos_medium_patch16_rpn_224,485.5,526.221,256,224,38.73
efficientnet_lite3,484.47,263.098,128,300,8.2 efficientnet_lite3,484.47,263.098,128,300,8.2
gluon_resnet101_v1s,483.47,527.891,256,224,44.67 gluon_resnet101_v1s,483.47,527.891,256,224,44.67
seresnet101,480.65,530.38,256,224,49.33 seresnet101,480.65,530.38,256,224,49.33
cs3edgenet_x,477.59,535.019,256,256,47.82
nest_tiny,476.68,267.593,128,224,17.06 nest_tiny,476.68,267.593,128,224,17.06
nf_seresnet101,474.46,537.213,256,224,49.33 nf_seresnet101,474.46,537.213,256,224,49.33
mobilevitv2_150_in22ft1k,473.86,269.132,128,256,10.59 mobilevitv2_150_in22ft1k,473.86,269.132,128,256,10.59
@ -452,12 +455,13 @@ efficientnet_b3,456.81,278.448,128,288,12.23
regnetx_080,454.56,843.629,384,224,39.57 regnetx_080,454.56,843.629,384,224,39.57
regnetx_064,452.43,564.953,256,224,26.21 regnetx_064,452.43,564.953,256,224,26.21
halo2botnet50ts_256,451.35,424.392,192,256,22.64 halo2botnet50ts_256,451.35,424.392,192,256,22.64
densenet201,447.44,425.987,192,224,20.01
ecaresnet101d,447.44,570.33,256,224,44.57 ecaresnet101d,447.44,570.33,256,224,44.57
densenet201,447.44,425.987,192,224,20.01
nf_regnet_b4,445.8,428.533,192,320,30.21 nf_regnet_b4,445.8,428.533,192,320,30.21
convit_small,443.63,431.737,192,224,27.78 convit_small,443.63,431.737,192,224,27.78
efficientnetv2_s,433.27,293.157,128,288,21.46 efficientnetv2_s,433.27,293.157,128,288,21.46
skresnext50_32x4d,432.3,590.802,256,224,27.48 skresnext50_32x4d,432.3,590.802,256,224,27.48
cs3se_edgenet_x,431.68,443.324,192,256,50.72
botnet50ts_256,428.8,297.529,128,256,22.74 botnet50ts_256,428.8,297.529,128,256,22.74
ssl_resnext101_32x4d,427.28,447.74,192,224,44.18 ssl_resnext101_32x4d,427.28,447.74,192,224,44.18
resnext101_32x4d,427.18,447.921,192,224,44.18 resnext101_32x4d,427.18,447.921,192,224,44.18
@ -662,8 +666,8 @@ dpn107,187.14,682.151,128,224,86.92
convnext_large_in22ft1k,187.05,511.402,96,224,197.77 convnext_large_in22ft1k,187.05,511.402,96,224,197.77
convnext_large,187.01,511.523,96,224,197.77 convnext_large,187.01,511.523,96,224,197.77
nf_regnet_b5,186.49,512.09,96,384,49.74 nf_regnet_b5,186.49,512.09,96,384,49.74
xcit_tiny_24_p8_224,183.21,520.609,96,224,12.11
xcit_tiny_24_p8_224_dist,183.21,520.533,96,224,12.11 xcit_tiny_24_p8_224_dist,183.21,520.533,96,224,12.11
xcit_tiny_24_p8_224,183.21,520.609,96,224,12.11
halonet_h1,177.48,359.151,64,256,8.1 halonet_h1,177.48,359.151,64,256,8.1
hrnet_w64,176.04,722.362,128,224,128.06 hrnet_w64,176.04,722.362,128,224,128.06
mobilevitv2_175_384_in22ft1k,175.76,363.135,64,384,14.25 mobilevitv2_175_384_in22ft1k,175.76,363.135,64,384,14.25
@ -739,8 +743,8 @@ eca_nfnet_l3,84.61,563.702,48,352,72.04
resnetv2_101x1_bitm,84.61,187.399,16,448,44.54 resnetv2_101x1_bitm,84.61,187.399,16,448,44.54
beit_base_patch16_384,83.67,381.291,32,384,86.74 beit_base_patch16_384,83.67,381.291,32,384,86.74
resnest200e,83.33,570.802,48,320,70.2 resnest200e,83.33,570.802,48,320,70.2
efficientnetv2_l,83.27,379.867,32,384,118.52
tf_efficientnetv2_l_in21ft1k,83.27,379.678,32,384,118.52 tf_efficientnetv2_l_in21ft1k,83.27,379.678,32,384,118.52
efficientnetv2_l,83.27,379.867,32,384,118.52
tf_efficientnetv2_l,82.74,382.367,32,384,118.52 tf_efficientnetv2_l,82.74,382.367,32,384,118.52
ecaresnet269d,82.15,579.477,48,320,102.09 ecaresnet269d,82.15,579.477,48,320,102.09
tresnet_xl_448,78.31,1222.487,96,448,78.44 tresnet_xl_448,78.31,1222.487,96,448,78.44
@ -752,8 +756,8 @@ pnasnet5large,68.77,461.392,32,331,86.06
resnetrs350,68.24,460.967,32,288,163.96 resnetrs350,68.24,460.967,32,288,163.96
nfnet_f3,67.87,703.087,48,320,254.92 nfnet_f3,67.87,703.087,48,320,254.92
nasnetalarge,67.38,469.785,32,331,88.75 nasnetalarge,67.38,469.785,32,331,88.75
resmlp_big_24_224_in22ft1k,67.03,475.857,32,224,129.14
resmlp_big_24_distilled_224,67.03,475.867,32,224,129.14 resmlp_big_24_distilled_224,67.03,475.867,32,224,129.14
resmlp_big_24_224_in22ft1k,67.03,475.857,32,224,129.14
resmlp_big_24_224,67.02,475.97,32,224,129.14 resmlp_big_24_224,67.02,475.97,32,224,129.14
cait_xs24_384,65.59,485.229,32,384,26.67 cait_xs24_384,65.59,485.229,32,384,26.67
convnext_large_384_in22ft1k,63.62,501.159,32,384,197.77 convnext_large_384_in22ft1k,63.62,501.159,32,384,197.77

1 model train_samples_per_sec train_step_time train_batch_size train_img_size param_count
145 convnext_nano_hnf 1235.34 413.703 512 224 15.59
146 convnext_nano_ols 1234.94 413.902 512 224 15.6
147 poolformer_s12 1234.11 414.201 512 224 11.92
148 convnext_nano 1233.61 414.261 512 224 15.59
149 resmlp_12_distilled_224 1232.37 414.645 512 224 15.35
150 resmlp_12_224 1232.04 414.762 512 224 15.35
151 fbnetv3_b 1226.89 415.617 512 224 8.6
423 halonet50ts 503.76 380.122 192 256 22.73
424 repvgg_b1 503.57 1015.623 512 224 57.42
425 swinv2_cr_tiny_ns_224 502.5 508.144 256 224 28.33
426 cs3sedarknet_x 501.96 508.547 256 256 35.4
427 dla102 497.28 513.14 256 224 33.27
428 wide_resnet50_2 495.68 773.85 384 224 68.88
429 res2net50_26w_6s 493.57 516.914 256 224 37.05
435 efficientnet_lite3 484.47 263.098 128 300 8.2
436 gluon_resnet101_v1s 483.47 527.891 256 224 44.67
437 seresnet101 480.65 530.38 256 224 49.33
438 cs3edgenet_x 477.59 535.019 256 256 47.82
439 nest_tiny 476.68 267.593 128 224 17.06
440 nf_seresnet101 474.46 537.213 256 224 49.33
441 mobilevitv2_150_in22ft1k 473.86 269.132 128 256 10.59
455 regnetx_080 454.56 843.629 384 224 39.57
456 regnetx_064 452.43 564.953 256 224 26.21
457 halo2botnet50ts_256 451.35 424.392 192 256 22.64
densenet201 447.44 425.987 192 224 20.01
458 ecaresnet101d 447.44 570.33 256 224 44.57
459 densenet201 447.44 425.987 192 224 20.01
460 nf_regnet_b4 445.8 428.533 192 320 30.21
461 convit_small 443.63 431.737 192 224 27.78
462 efficientnetv2_s 433.27 293.157 128 288 21.46
463 skresnext50_32x4d 432.3 590.802 256 224 27.48
464 cs3se_edgenet_x 431.68 443.324 192 256 50.72
465 botnet50ts_256 428.8 297.529 128 256 22.74
466 ssl_resnext101_32x4d 427.28 447.74 192 224 44.18
467 resnext101_32x4d 427.18 447.921 192 224 44.18
666 convnext_large_in22ft1k 187.05 511.402 96 224 197.77
667 convnext_large 187.01 511.523 96 224 197.77
668 nf_regnet_b5 186.49 512.09 96 384 49.74
xcit_tiny_24_p8_224 183.21 520.609 96 224 12.11
669 xcit_tiny_24_p8_224_dist 183.21 520.533 96 224 12.11
670 xcit_tiny_24_p8_224 183.21 520.609 96 224 12.11
671 halonet_h1 177.48 359.151 64 256 8.1
672 hrnet_w64 176.04 722.362 128 224 128.06
673 mobilevitv2_175_384_in22ft1k 175.76 363.135 64 384 14.25
743 resnetv2_101x1_bitm 84.61 187.399 16 448 44.54
744 beit_base_patch16_384 83.67 381.291 32 384 86.74
745 resnest200e 83.33 570.802 48 320 70.2
efficientnetv2_l 83.27 379.867 32 384 118.52
746 tf_efficientnetv2_l_in21ft1k 83.27 379.678 32 384 118.52
747 efficientnetv2_l 83.27 379.867 32 384 118.52
748 tf_efficientnetv2_l 82.74 382.367 32 384 118.52
749 ecaresnet269d 82.15 579.477 48 320 102.09
750 tresnet_xl_448 78.31 1222.487 96 448 78.44
756 resnetrs350 68.24 460.967 32 288 163.96
757 nfnet_f3 67.87 703.087 48 320 254.92
758 nasnetalarge 67.38 469.785 32 331 88.75
resmlp_big_24_224_in22ft1k 67.03 475.857 32 224 129.14
759 resmlp_big_24_distilled_224 67.03 475.867 32 224 129.14
760 resmlp_big_24_224_in22ft1k 67.03 475.857 32 224 129.14
761 resmlp_big_24_224 67.02 475.97 32 224 129.14
762 cait_xs24_384 65.59 485.229 32 384 26.67
763 convnext_large_384_in22ft1k 63.62 501.159 32 384 197.77

10
results/benchmark-train-amp-nhwc-pt112-cu113-rtx3090.csv
Unescape View File

@ -167,6 +167,7 @@ resnetv2_50,1288.61,396.553,512,224,25.55
regnetx_006,1286.44,397.176,512,224,6.2 regnetx_006,1286.44,397.176,512,224,6.2
crossvit_9_240,1258.73,303.637,384,240,8.55 crossvit_9_240,1258.73,303.637,384,240,8.55
convnext_nano_ols,1252.33,408.151,512,224,15.6 convnext_nano_ols,1252.33,408.151,512,224,15.6
convnext_nano,1249.89,408.864,512,224,15.59
convnext_nano_hnf,1249.05,409.138,512,224,15.59 convnext_nano_hnf,1249.05,409.138,512,224,15.59
resnet26t,1237.34,413.275,512,256,16.01 resnet26t,1237.34,413.275,512,256,16.01
tf_mixnet_s,1236.15,412.905,512,224,4.13 tf_mixnet_s,1236.15,412.905,512,224,4.13
@ -421,7 +422,9 @@ botnet50ts_256,534.71,238.412,128,256,22.74
mobilevitv2_150,533.38,238.97,128,256,10.59 mobilevitv2_150,533.38,238.97,128,256,10.59
vit_base_r26_s32_224,533.28,358.697,192,224,101.38 vit_base_r26_s32_224,533.28,358.697,192,224,101.38
mobilevitv2_150_in22ft1k,532.99,239.183,128,256,10.59 mobilevitv2_150_in22ft1k,532.99,239.183,128,256,10.59
cs3sedarknet_x,531.85,479.872,256,256,35.4
nf_ecaresnet101,531.3,479.947,256,224,44.55 nf_ecaresnet101,531.3,479.947,256,224,44.55
cs3edgenet_x,529.37,482.632,256,256,47.82
res2next50,528.59,483.023,256,224,24.67 res2next50,528.59,483.023,256,224,24.67
res2net101_26w_4s,527.01,483.179,256,224,45.21 res2net101_26w_4s,527.01,483.179,256,224,45.21
vit_large_patch32_224,524.74,486.172,256,224,306.54 vit_large_patch32_224,524.74,486.172,256,224,306.54
@ -476,6 +479,7 @@ efficientnet_el,461.97,275.94,128,300,10.59
coat_lite_small,461.36,414.606,192,224,19.84 coat_lite_small,461.36,414.606,192,224,19.84
nf_regnet_b4,457.97,417.049,192,320,30.21 nf_regnet_b4,457.97,417.049,192,320,30.21
vgg19,457.37,839.347,384,224,143.67 vgg19,457.37,839.347,384,224,143.67
cs3se_edgenet_x,457.05,418.615,192,256,50.72
dla169,455.51,419.044,192,224,53.39 dla169,455.51,419.044,192,224,53.39
convit_small,454.72,421.197,192,224,27.78 convit_small,454.72,421.197,192,224,27.78
gluon_resnet152_v1s,452.53,421.917,192,224,60.32 gluon_resnet152_v1s,452.53,421.917,192,224,60.32
@ -734,8 +738,8 @@ vit_large_r50_s32_384,77.34,411.186,32,384,329.09
nasnetalarge,77.32,408.633,32,331,88.75 nasnetalarge,77.32,408.633,32,331,88.75
swinv2_cr_small_384,76.42,416.277,32,384,49.7 swinv2_cr_small_384,76.42,416.277,32,384,49.7
swin_base_patch4_window12_384,74.73,426.327,32,384,87.9 swin_base_patch4_window12_384,74.73,426.327,32,384,87.9
resmlp_big_24_224_in22ft1k,70.88,449.933,32,224,129.14
resmlp_big_24_distilled_224,70.88,449.95,32,224,129.14 resmlp_big_24_distilled_224,70.88,449.95,32,224,129.14
resmlp_big_24_224_in22ft1k,70.88,449.933,32,224,129.14
resmlp_big_24_224,70.41,452.99,32,224,129.14 resmlp_big_24_224,70.41,452.99,32,224,129.14
regnety_320,66.33,1928.357,128,224,145.05 regnety_320,66.33,1928.357,128,224,145.05
xcit_tiny_24_p8_384_dist,66.29,479.361,32,384,12.11 xcit_tiny_24_p8_384_dist,66.29,479.361,32,384,12.11
@ -757,8 +761,8 @@ efficientnetv2_xl,62.18,251.438,16,384,208.12
tf_efficientnetv2_xl_in21ft1k,62.14,251.721,16,384,208.12 tf_efficientnetv2_xl_in21ft1k,62.14,251.721,16,384,208.12
xcit_small_12_p8_384_dist,61.84,386.224,24,384,26.21 xcit_small_12_p8_384_dist,61.84,386.224,24,384,26.21
vit_base_r50_s16_384,61.01,391.67,24,384,98.95 vit_base_r50_s16_384,61.01,391.67,24,384,98.95
swinv2_large_window12to16_192to256_22kft1k,60.98,391.098,24,256,196.74
vit_base_resnet50_384,60.98,391.903,24,384,98.95 vit_base_resnet50_384,60.98,391.903,24,384,98.95
swinv2_large_window12to16_192to256_22kft1k,60.98,391.098,24,256,196.74
eca_nfnet_l2,58.72,1632.112,96,320,56.72 eca_nfnet_l2,58.72,1632.112,96,320,56.72
volo_d2_384,58.5,271.766,16,384,58.87 volo_d2_384,58.5,271.766,16,384,58.87
resnetrs420,56.49,415.629,24,320,191.89 resnetrs420,56.49,415.629,24,320,191.89
@ -820,8 +824,8 @@ ig_resnext101_32x48d,11.2,1427.437,16,224,828.41
tf_efficientnet_l2_ns_475,10.96,267.912,3,475,480.31 tf_efficientnet_l2_ns_475,10.96,267.912,3,475,480.31
dm_nfnet_f5,9.76,1222.345,12,416,377.21 dm_nfnet_f5,9.76,1222.345,12,416,377.21
beit_large_patch16_512,9.42,422.548,4,512,305.67 beit_large_patch16_512,9.42,422.548,4,512,305.67
nfnet_f5,8.0,1992.337,16,416,377.21
volo_d5_512,8.0,371.847,3,512,296.09 volo_d5_512,8.0,371.847,3,512,296.09
nfnet_f5,8.0,1992.337,16,416,377.21
dm_nfnet_f6,7.45,1065.231,8,448,438.36 dm_nfnet_f6,7.45,1065.231,8,448,438.36
nfnet_f6,5.82,2052.248,12,448,438.36 nfnet_f6,5.82,2052.248,12,448,438.36
nfnet_f7,5.73,1387.07,8,480,499.5 nfnet_f7,5.73,1387.07,8,480,499.5

1 model train_samples_per_sec train_step_time train_batch_size train_img_size param_count
167 regnetx_006 1286.44 397.176 512 224 6.2
168 crossvit_9_240 1258.73 303.637 384 240 8.55
169 convnext_nano_ols 1252.33 408.151 512 224 15.6
170 convnext_nano 1249.89 408.864 512 224 15.59
171 convnext_nano_hnf 1249.05 409.138 512 224 15.59
172 resnet26t 1237.34 413.275 512 256 16.01
173 tf_mixnet_s 1236.15 412.905 512 224 4.13
422 mobilevitv2_150 533.38 238.97 128 256 10.59
423 vit_base_r26_s32_224 533.28 358.697 192 224 101.38
424 mobilevitv2_150_in22ft1k 532.99 239.183 128 256 10.59
425 cs3sedarknet_x 531.85 479.872 256 256 35.4
426 nf_ecaresnet101 531.3 479.947 256 224 44.55
427 cs3edgenet_x 529.37 482.632 256 256 47.82
428 res2next50 528.59 483.023 256 224 24.67
429 res2net101_26w_4s 527.01 483.179 256 224 45.21
430 vit_large_patch32_224 524.74 486.172 256 224 306.54
479 coat_lite_small 461.36 414.606 192 224 19.84
480 nf_regnet_b4 457.97 417.049 192 320 30.21
481 vgg19 457.37 839.347 384 224 143.67
482 cs3se_edgenet_x 457.05 418.615 192 256 50.72
483 dla169 455.51 419.044 192 224 53.39
484 convit_small 454.72 421.197 192 224 27.78
485 gluon_resnet152_v1s 452.53 421.917 192 224 60.32
738 nasnetalarge 77.32 408.633 32 331 88.75
739 swinv2_cr_small_384 76.42 416.277 32 384 49.7
740 swin_base_patch4_window12_384 74.73 426.327 32 384 87.9
resmlp_big_24_224_in22ft1k 70.88 449.933 32 224 129.14
741 resmlp_big_24_distilled_224 70.88 449.95 32 224 129.14
742 resmlp_big_24_224_in22ft1k 70.88 449.933 32 224 129.14
743 resmlp_big_24_224 70.41 452.99 32 224 129.14
744 regnety_320 66.33 1928.357 128 224 145.05
745 xcit_tiny_24_p8_384_dist 66.29 479.361 32 384 12.11
761 tf_efficientnetv2_xl_in21ft1k 62.14 251.721 16 384 208.12
762 xcit_small_12_p8_384_dist 61.84 386.224 24 384 26.21
763 vit_base_r50_s16_384 61.01 391.67 24 384 98.95
swinv2_large_window12to16_192to256_22kft1k 60.98 391.098 24 256 196.74
764 vit_base_resnet50_384 60.98 391.903 24 384 98.95
765 swinv2_large_window12to16_192to256_22kft1k 60.98 391.098 24 256 196.74
766 eca_nfnet_l2 58.72 1632.112 96 320 56.72
767 volo_d2_384 58.5 271.766 16 384 58.87
768 resnetrs420 56.49 415.629 24 320 191.89
824 tf_efficientnet_l2_ns_475 10.96 267.912 3 475 480.31
825 dm_nfnet_f5 9.76 1222.345 12 416 377.21
826 beit_large_patch16_512 9.42 422.548 4 512 305.67
nfnet_f5 8.0 1992.337 16 416 377.21
827 volo_d5_512 8.0 371.847 3 512 296.09
828 nfnet_f5 8.0 1992.337 16 416 377.21
829 dm_nfnet_f6 7.45 1065.231 8 448 438.36
830 nfnet_f6 5.82 2052.248 12 448 438.36
831 nfnet_f7 5.73 1387.07 8 480 499.5
Write Preview
Loading…
Cancel
Save