pytorch-backbone-benchmark icon indicating copy to clipboard operation
pytorch-backbone-benchmark copied to clipboard

Published 20 hours ago • verified publisher icon developer0hye
→

Metadata

Benchmarks for popular neural network models supported by timm

pytorch-backbone-benchmark

Benchmarks for popular neural network models supported by timm

  • CPU: Intel(R) Core(TM) i5-10400 CPU @ 2.90GHz 2.90 GHz
  • GPU: RTX 3070 8GB
  • RAM: 32.0 GB
  • Pytorch Version: 1.8.1
  • Input Tensor Shape: 1x3x608x608

All timing experiments are averaged over 100 times.

sorted by execution time

The graph is here.

image

model top1 param_count execution time(sec) fps
0 swsl_resnet18 73.276 11.69 0.0044 227
1 resnet18 69.748 11.69 0.0045 222
2 gluon_resnet18_v1b 70.836 11.69 0.0045 222
3 ssl_resnet18 72.61 11.69 0.0045 222
4 resnet18d 72.26 11.71 0.0048 208
5 tf_mobilenetv3_small_minimal_100 62.906 2.04 0.0055 181
6 legacy_seresnet18 71.742 11.78 0.0065 153
7 tf_mobilenetv3_large_minimal_100 72.248 3.92 0.0073 136
8 efficientnet_lite0 75.484 4.65 0.0074 135
9 ese_vovnet19b_dw 76.798 6.54 0.0074 135
10 gluon_resnet34_v1b 74.588 21.8 0.0074 135
11 tv_resnet34 73.312 21.8 0.0075 133
12 resnet34 75.11 21.8 0.0076 131
13 mobilenetv2_140 76.516 6.11 0.0076 131
14 regnetx_002 68.762 2.68 0.0076 131
15 dla34 74.63 15.74 0.0077 129
16 tf_efficientnet_lite0 74.83 4.65 0.0077 129
17 mnasnet_100 74.658 4.38 0.0078 128
18 efficientnet_es_pruned 75 5.44 0.0079 126
19 efficientnet_es 78.066 5.44 0.0079 126
20 mobilenetv2_100 72.97 3.5 0.008 125
21 tf_efficientnet_es 76.594 5.44 0.008 125
22 resnet26 75.292 16 0.008 125
23 dla46x_c 65.97 1.07 0.0083 120
24 resnet34d 77.116 21.82 0.0083 120
25 dla46_c 64.866 1.3 0.0084 119
26 gernet_s 76.916 8.17 0.0084 119
27 resnest14d 75.506 10.61 0.0084 119
28 hardcorenas_a 75.916 5.26 0.0086 116
29 selecsls42b 77.174 32.46 0.0086 116
30 resnet26d 76.696 16.01 0.0087 114
31 gernet_m 80.732 21.14 0.0088 113
32 semnasnet_100 75.448 3.89 0.0093 107
33 ecaresnet26t 79.854 16.01 0.0094 106
34 regnetx_006 73.852 6.2 0.0094 106
35 skresnet18 73.038 11.96 0.0094 106
36 spnasnet_100 74.084 4.42 0.0095 105
37 fbnetc_100 75.124 5.57 0.0096 104
38 mobilenetv2_110d 75.036 4.52 0.0096 104
39 tf_efficientnet_lite1 76.642 5.42 0.0097 103
40 regnetx_008 75.038 7.26 0.0097 103
41 tf_efficientnet_lite2 77.468 6.09 0.0098 102
42 tf_mobilenetv3_small_100 67.922 2.54 0.0105 95
43 hardcorenas_b 76.538 5.18 0.0106 94
44 efficientnet_em 79.252 6.9 0.0106 94
45 gernet_l 81.354 31.08 0.0107 93
46 dla60x_c 67.892 1.32 0.0108 92
47 cspresnet50 79.574 21.62 0.0108 92
48 tf_efficientnet_em 78.13 6.9 0.0108 92
49 selecsls60 77.982 30.67 0.011 90
50 tf_mobilenetv3_small_075 65.716 2.04 0.011 90
51 tf_efficientnet_lite3 79.82 8.2 0.0111 90
52 regnety_002 70.252 3.16 0.0112 89
53 repvgg_a2 76.46 28.21 0.0112 89
54 regnetx_016 76.95 9.19 0.0113 88
55 efficientnet_b0 77.698 5.29 0.0114 87
56 selecsls60b 78.412 32.77 0.0114 87
57 mobilenetv3_large_100 75.766 5.48 0.0115 86
58 mobilenetv3_large_100_miil 77.916 5.48 0.0115 86
59 hardcorenas_c 77.054 5.52 0.0115 86
60 mobilenetv2_120d 77.284 5.83 0.0116 86
61 legacy_seresnext26_32x4d 77.104 16.79 0.0116 86
62 tf_efficientnet_b0_ns 78.658 5.29 0.0116 86
63 tf_mobilenetv3_large_100 75.518 5.48 0.0116 86
64 resnest26d 78.478 17.07 0.0117 85
65 tf_efficientnet_b0 76.848 5.29 0.012 83
66 legacy_seresnet34 74.808 21.96 0.012 83
67 tf_efficientnet_b0_ap 77.086 5.29 0.012 83
68 mobilenetv3_rw 75.634 5.48 0.0122 81
69 seresnext26d_32x4d 77.602 16.81 0.0123 81
70 seresnext26t_32x4d 77.986 16.81 0.0123 81
71 regnetx_004 72.396 5.16 0.0124 80
72 ese_vovnet39b 79.32 24.57 0.0126 79
73 ecaresnet50d_pruned 79.716 19.94 0.0127 78
74 rexnet_100 77.858 4.8 0.0127 78
75 ecaresnetlight 80.462 30.16 0.0127 78
76 rexnet_130 79.5 7.56 0.0127 78
77 tv_resnet50 76.138 25.56 0.0127 78
78 swsl_resnet50 81.166 25.56 0.0127 78
79 tf_mobilenetv3_large_075 73.438 3.99 0.0128 78
80 repvgg_b0 75.152 15.82 0.0128 78
81 resnet50 79.038 25.56 0.0128 78
82 ssl_resnet50 79.222 25.56 0.0128 78
83 rexnet_150 80.31 9.73 0.0128 78
84 regnety_008 76.316 6.26 0.0128 78
85 gluon_resnet50_v1b 77.58 25.56 0.0129 77
86 dla60 77.032 22.04 0.013 76
87 rexnet_200 81.632 16.37 0.0131 76
88 gluon_resnet50_v1c 78.012 25.58 0.0133 75
89 regnety_006 75.246 6.06 0.0133 75
90 gluon_resnet50_v1d 79.074 25.58 0.0135 74
91 resnet50d 80.53 25.58 0.0136 73
92 regnety_004 74.034 4.34 0.0137 72
93 tf_efficientnet_lite4 81.536 13.01 0.0139 71
94 cspdarknet53 80.058 27.64 0.014 71
95 cspresnext50 80.04 20.57 0.0141 70
96 hardcorenas_e 77.794 8.07 0.0143 69
97 resnetblur50 79.286 25.56 0.0143 69
98 tf_efficientnet_cc_b0_4e 77.306 13.31 0.0144 69
99 tf_efficientnet_cc_b0_8e 77.908 24.01 0.0145 68
100 res2net50_48w_2s 77.522 25.29 0.0145 68
101 legacy_seresnet50 77.63 28.09 0.0147 68
102 regnetx_032 78.172 15.3 0.0147 68
103 hardcorenas_f 78.104 8.2 0.0147 68
104 ecaresnet50t 82.346 25.57 0.0147 68
105 ecaresnet50d 80.592 25.58 0.0148 67
106 mixnet_s 75.992 4.13 0.0148 67
107 gluon_resnet50_v1s 78.712 25.68 0.015 66
108 ghostnet_100 73.978 5.18 0.0151 66
109 seresnet50 80.274 28.09 0.0152 65
110 efficientnet_el_pruned 80.3 10.59 0.0153 65
111 efficientnet_el 81.316 10.59 0.0154 64
112 tf_efficientnet_el 80.25 10.59 0.0154 64
113 dpn68 76.318 12.61 0.0156 64
114 hardcorenas_d 77.432 7.5 0.0156 64
115 efficientnet_b1 78.794 7.79 0.0157 63
116 tf_mixnet_s 75.65 4.13 0.0158 63
117 tf_inception_v3 77.856 23.83 0.0159 62
118 inception_v3 77.438 23.83 0.016 62
119 adv_inception_v3 77.582 23.83 0.016 62
120 xception 79.052 22.86 0.016 62
121 regnetx_040 78.482 22.12 0.016 62
122 hrnet_w18_small 72.342 13.19 0.016 62
123 gluon_inception_v3 78.806 23.83 0.0161 62
124 efficientnet_b2 80.612 9.11 0.0161 62
125 tf_efficientnet_b2_ap 80.3 9.11 0.0161 62
126 efficientnet_b1_pruned 78.236 6.33 0.0161 62
127 efficientnet_b2_pruned 79.916 8.31 0.0162 61
128 dla60x 78.246 17.35 0.0162 61
129 resnetv2_50x1_bitm 80.172 25.55 0.0163 61
130 tf_efficientnet_b1 78.826 7.79 0.0163 61
131 tf_efficientnet_b1_ns 81.388 7.79 0.0164 60
132 resnetrs50 79.892 35.69 0.0164 60
133 tf_efficientnet_b1_ap 79.28 7.79 0.0164 60
134 tf_efficientnet_b2_ns 82.38 9.11 0.0165 60
135 tf_efficientnet_b2 80.086 9.11 0.0165 60
136 res2net50_26w_4s 77.964 25.7 0.0165 60
137 tv_resnext50_32x4d 77.62 25.03 0.0167 59
138 ssl_resnext50_32x4d 80.318 25.03 0.0167 59
139 dpn68b 79.216 12.61 0.0167 59
140 resnest50d_1s4x24d 80.988 25.68 0.0168 59
141 swsl_resnext50_32x4d 82.182 25.03 0.0168 59
142 gluon_resnext50_32x4d 79.354 25.03 0.0168 59
143 resnext50_32x4d 79.768 25.03 0.0168 59
144 res2next50 78.246 24.67 0.0172 58
145 nf_resnet50 80.694 25.56 0.0173 57
146 skresnet34 76.912 22.28 0.0174 57
147 resnext50d_32x4d 79.676 25.05 0.0174 57
148 repvgg_b1g4 77.594 39.97 0.0175 57
149 mixnet_l 78.976 7.33 0.0177 56
150 dla60_res2net 78.464 20.85 0.0178 56
151 vgg11 69.024 132.86 0.0178 56
152 mixnet_m 77.26 5.01 0.0179 55
153 resnest50d 80.974 27.48 0.018 55
154 tf_efficientnet_b3_ns 84.048 12.23 0.0182 54
155 efficientnet_b3 82.242 12.23 0.0185 54
156 tf_efficientnet_b3 81.636 12.23 0.0186 53
157 vgg11_bn 70.36 132.87 0.0187 53
158 xception41 78.516 26.97 0.0188 53
159 tf_efficientnet_b3_ap 81.822 12.23 0.0189 52
160 efficientnet_b3_pruned 80.858 9.86 0.0189 52
161 tf_mixnet_m 76.942 5.01 0.019 52
162 tf_mixnet_l 78.774 7.33 0.0191 52
163 eca_nfnet_l0 82.588 24.14 0.0191 52
164 dla60_res2next 78.44 17.03 0.0192 52
165 gluon_seresnext50_32x4d 79.918 27.56 0.0194 51
166 seresnext50_32x4d 81.266 27.56 0.0194 51
167 legacy_seresnext50_32x4d 79.078 27.56 0.0195 51
168 regnety_032 82.724 19.44 0.0196 51
169 regnety_040 79.22 20.65 0.0199 50
170 regnetx_064 79.072 26.21 0.02 50
171 dla102 78.032 33.27 0.0201 49
172 regnetx_080 79.194 39.57 0.0203 49
173 tf_efficientnet_cc_b1_8e 79.308 39.72 0.0204 49
174 nfnet_l0 82.76 35.07 0.0205 48
175 resnest50d_4s2x40d 81.108 30.42 0.0214 46
176 tv_resnet101 77.374 44.55 0.0219 45
177 tv_densenet121 74.738 7.98 0.0219 45
178 densenet121 75.578 7.98 0.022 45
179 res2net50_26w_6s 78.57 37.05 0.0221 45
180 densenetblur121d 76.588 8 0.0221 45
181 gluon_resnet101_v1b 79.306 44.55 0.0221 45
182 mixnet_xl 80.476 11.9 0.0224 44
183 gluon_resnet101_v1c 79.534 44.57 0.0226 44
184 vgg13 69.926 133.05 0.0227 44
185 gluon_resnet101_v1d 80.414 44.57 0.0228 43
186 resnet101d 83.022 44.57 0.0228 43
187 efficientnet_b4 83.428 19.34 0.023 43
188 tf_efficientnet_b4_ns 85.162 19.34 0.0232 43
189 wide_resnet50_2 81.456 68.88 0.0232 43
190 tf_efficientnet_b4 83.022 19.34 0.0232 43
191 repvgg_b1 78.366 57.42 0.0233 42
192 tf_efficientnet_b4_ap 83.248 19.34 0.0235 42
193 regnety_016 77.862 11.2 0.0238 42
194 regnety_064 79.722 30.58 0.0239 41
195 skresnext50_32x4d 80.156 27.48 0.0243 41
196 repvgg_b2g4 79.366 61.76 0.0243 41
197 ecaresnet101d_pruned 80.818 24.88 0.0244 40
198 gluon_resnet101_v1s 80.302 44.67 0.0244 40
199 nf_regnet_b1 79.306 10.22 0.0245 40
200 vgg13_bn 71.594 133.05 0.0246 40
201 regnety_080 79.876 39.18 0.0247 40
202 ecaresnet101d 82.172 44.57 0.025 40
203 dla102x 78.51 26.31 0.0252 39
204 res2net50_14w_8s 78.15 25.06 0.0263 38
205 gluon_xception65 79.716 39.92 0.0265 37
206 xception65 79.552 39.92 0.0266 37
207 inception_v4 80.168 42.68 0.027 37
208 vgg16 71.594 138.36 0.0273 36
209 legacy_seresnet101 78.382 49.33 0.0274 36
210 tf_efficientnet_b5_ns 86.088 30.39 0.0276 36
211 tf_efficientnet_b5 83.812 30.39 0.0277 36
212 tf_efficientnet_b5_ap 84.252 30.39 0.0277 36
213 res2net50_26w_8s 79.198 48.4 0.0282 35
214 resnetrs101 82.288 63.62 0.0283 35
215 ssl_resnext101_32x4d 80.924 44.18 0.0285 35
216 swsl_resnext101_32x4d 83.23 44.18 0.0285 35
217 gluon_resnext101_32x4d 80.334 44.18 0.0287 34
218 regnetx_120 79.596 46.11 0.0288 34
219 hrnet_w18_small_v2 75.114 15.6 0.029 34
220 vgg16_bn 73.35 138.37 0.0293 34
221 resnetv2_101x1_bitm 82.212 44.54 0.0294 34
222 densenet169 75.906 14.15 0.0302 33
223 res2net101_26w_4s 79.198 45.21 0.0305 32
224 densenet161 77.358 28.68 0.0312 32
225 repvgg_b3g4 80.212 83.83 0.0313 31
226 regnety_120 80.366 51.82 0.0314 31
227 dla169 78.688 53.39 0.0315 31
228 tv_resnet152 78.312 60.19 0.0318 31
229 vgg19 72.368 143.67 0.0321 31
230 gluon_resnet152_v1b 79.686 60.19 0.0322 31
231 dpn92 80.008 37.67 0.0324 30
232 tf_efficientnet_b6 84.11 43.04 0.0325 30
233 tf_efficientnet_b6_ns 86.452 43.04 0.0325 30
234 gluon_resnet152_v1c 79.91 60.21 0.0326 30
235 tf_efficientnet_b6_ap 84.788 43.04 0.0326 30
236 resnet152d 83.68 60.21 0.0327 30
237 gluon_resnet152_v1d 80.474 60.21 0.0327 30
238 repvgg_b2 78.792 89.02 0.0332 30
239 gluon_seresnext101_32x4d 80.904 48.96 0.0333 30
240 dm_nfnet_f0 83.342 71.49 0.0334 29
241 legacy_seresnext101_32x4d 80.228 48.96 0.0337 29
242 xception71 79.874 42.34 0.034 29
243 gluon_resnet152_v1s 81.016 60.32 0.0341 29
244 vgg19_bn 74.214 143.68 0.0342 29
245 resnest101e 82.89 48.28 0.0342 29
246 eca_nfnet_l1 84.008 41.41 0.0352 28
247 densenet201 77.286 20.01 0.0361 27
248 dla102x2 79.448 41.28 0.0364 27
249 regnetx_160 79.856 54.28 0.0365 27
250 regnety_160 83.686 83.59 0.0396 25
251 legacy_seresnet152 78.66 66.82 0.0408 24
252 resnetrs152 83.712 86.62 0.041 24
253 seresnet152d 84.362 66.84 0.0413 24
254 inception_resnet_v2 80.458 55.84 0.0426 23
255 ens_adv_inception_resnet_v2 79.982 55.84 0.0427 23
256 resnet200d 83.962 64.69 0.0429 23
257 dpn98 79.642 61.57 0.0435 22
258 tf_efficientnet_b7 84.936 66.35 0.0441 22
259 tf_efficientnet_b7_ap 85.12 66.35 0.0441 22
260 tf_efficientnet_b7_ns 86.84 66.35 0.0441 22
261 wide_resnet101_2 78.856 126.89 0.0441 22
262 repvgg_b3 80.492 123.09 0.0443 22
263 swsl_resnext101_32x8d 84.284 88.79 0.0453 22
264 ig_resnext101_32x8d 82.688 88.79 0.0454 22
265 resnext101_32x8d 79.308 88.79 0.0454 22
266 ssl_resnext101_32x8d 81.616 88.79 0.0455 21
267 gluon_resnext101_64x4d 80.604 83.46 0.0459 21
268 gluon_seresnext101_64x4d 80.894 88.23 0.0505 19
269 resnetrs200 84.066 93.21 0.0537 18
270 tf_efficientnet_b8 85.37 87.41 0.0551 18
271 tf_efficientnet_b8_ap 85.37 87.41 0.0552 18
272 hrnet_w30 78.206 37.71 0.0556 17
273 pnasnet5large 82.782 86.06 0.0556 17
274 hrnet_w18 76.758 21.3 0.0564 17
275 hrnet_w32 78.45 41.23 0.0572 17
276 nasnetalarge 82.62 88.75 0.0588 17
277 dpn131 79.822 79.25 0.0593 16
278 dm_nfnet_f1 84.604 132.63 0.0601 16
279 hrnet_w48 79.3 77.47 0.0609 16
280 hrnet_w40 78.92 57.56 0.061 16
281 hrnet_w44 78.896 67.06 0.0614 16
282 hrnet_w64 79.474 128.06 0.0629 15
283 dpn107 80.156 86.92 0.0634 15
284 ecaresnet269d 84.976 102.09 0.0635 15
285 regnety_320 80.812 145.05 0.0651 15
286 resnest200e 83.832 70.2 0.0651 15
287 regnetx_320 80.246 107.81 0.0657 15
288 resnetrs270 84.434 129.86 0.0722 13
289 gluon_senet154 81.234 115.09 0.0723 13
290 legacy_senet154 81.31 115.09 0.0727 13
291 ig_resnext101_32x16d 84.17 194.03 0.0784 12
292 ssl_resnext101_32x16d 81.844 194.03 0.0785 12
293 swsl_resnext101_32x16d 83.346 194.03 0.0785 12
294 resnetv2_50x3_bitm 83.784 217.32 0.0823 12
295 resnest269e 84.518 110.93 0.0866 11
296 dm_nfnet_f2 84.99 193.78 0.0873 11
297 resnetrs350 84.72 163.96 0.0931 10
298 resnetrs420 85.008 191.89 0.1106 9
299 resnetv2_152x2_bitm 84.44 236.34 0.1108 9
300 dm_nfnet_f3 85.56 254.92 0.1143 8
301 dm_nfnet_f4 85.658 316.07 0.1413 7
302 resnetv2_101x3_bitm 84.394 387.93 0.1557 6
303 ig_resnext101_32x32d 85.094 468.53 0.1678 5
304 dm_nfnet_f5 85.714 377.21 0.1686 5
305 tf_efficientnet_l2_ns_475 88.234 480.31 0.1875 5
306 tf_efficientnet_l2_ns 88.352 480.31 0.1877 5
307 dm_nfnet_f6 86.296 438.36 0.1959 5
308 ig_resnext101_32x48d 85.428 828.41 0.2854 3
309 resnetv2_152x4_bitm 84.932 936.53 0.3693 2

← Metadata

17
Stars
0
Forks
Watchers

Owner

verified publisher icon developer0hye

Metadata

Benchmarks for popular neural network models supported by timm

Back