From 419defade47a4f562be537bf905b5eba46b98cf6 Mon Sep 17 00:00:00 2001 From: littletomatodonkey Date: Mon, 13 Apr 2020 14:43:27 +0000 Subject: [PATCH] fix table --- docs/zh_CN/models/DPN_DenseNet.md | 38 +++---- .../models/EfficientNet_and_ResNeXt101_wsl.md | 52 +++------ docs/zh_CN/models/HRNet.md | 31 ++---- docs/zh_CN/models/Inception.md | 30 ++---- docs/zh_CN/models/Mobile.md | 102 ++++++------------ docs/zh_CN/models/Others.md | 37 +++---- docs/zh_CN/models/ResNet_and_vd.md | 52 ++++----- docs/zh_CN/models/SEResNext_and_Res2Net.md | 81 +++++--------- 8 files changed, 135 insertions(+), 288 deletions(-) diff --git a/docs/zh_CN/models/DPN_DenseNet.md b/docs/zh_CN/models/DPN_DenseNet.md index 3c4e6e69..25557506 100644 --- a/docs/zh_CN/models/DPN_DenseNet.md +++ b/docs/zh_CN/models/DPN_DenseNet.md @@ -22,33 +22,19 @@ | DPN131 | 0.807 | 0.951 | 0.801 | 0.949 | 30.510 | 75.360 | -## FP16预测速度 - -| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) | -|:--:|:--:|:--:|:--:|:--:| -| DenseNet121 | 3.653 | 4.560 | 5.574 | 11.517 | -| DenseNet161 | 7.826 | 8.936 | 10.970 | 22.554 | -| DenseNet169 | 5.625 | 6.698 | 7.876 | 14.983 | -| DenseNet201 | 7.243 | 8.537 | 10.111 | 18.928 | -| DenseNet264 | 10.882 | 12.539 | 14.645 | 26.455 | -| DPN68 | 10.310 | 11.060 | 14.299 | 29.618 | -| DPN92 | 16.335 | 17.373 | 23.197 | 45.210 | -| DPN98 | 18.975 | 23.073 | 28.902 | 66.280 | -| DPN107 | 24.932 | 28.607 | 37.513 | 89.112 | -| DPN131 | 25.425 | 29.874 | 37.355 | 88.583 | ## FP32预测速度 -| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) | -|:--:|:--:|:--:|:--:|:--:| -| DenseNet121 | 3.732 | 6.614 | 8.517 | 21.755 | -| DenseNet161 | 8.282 | 14.438 | 19.336 | 51.953 | -| DenseNet169 | 5.705 | 10.074 | 12.432 | 28.756 | -| DenseNet201 | 7.315 | 13.830 | 16.941 | 38.654 | -| DenseNet264 | 10.986 | 21.460 | 25.724 | 56.501 | -| DPN68 | 10.357 | 11.025 | 14.903 | 34.380 | -| DPN92 | 16.067 | 21.315 | 26.176 | 62.126 | -| DPN98 | 18.455 | 26.710 | 36.009 | 104.084 | -| DPN107 | 24.164 | 37.691 | 51.307 | 148.041 | -| DPN131 | 24.761 | 35.806 | 48.401 | 133.233 | +| Models | Crop Size | Resize Short Size | Batch Size=1
(ms) | +|-------------|-----------|-------------------|--------------------------| +| DenseNet121 | 224 | 256 | 4.371 | +| DenseNet161 | 224 | 256 | 8.863 | +| DenseNet169 | 224 | 256 | 6.391 | +| DenseNet201 | 224 | 256 | 8.173 | +| DenseNet264 | 224 | 256 | 11.942 | +| DPN68 | 224 | 256 | 11.805 | +| DPN92 | 224 | 256 | 17.840 | +| DPN98 | 224 | 256 | 21.057 | +| DPN107 | 224 | 256 | 28.685 | +| DPN131 | 224 | 256 | 28.083 | diff --git a/docs/zh_CN/models/EfficientNet_and_ResNeXt101_wsl.md b/docs/zh_CN/models/EfficientNet_and_ResNeXt101_wsl.md index f4f790ab..e32fa0e7 100644 --- a/docs/zh_CN/models/EfficientNet_and_ResNeXt101_wsl.md +++ b/docs/zh_CN/models/EfficientNet_and_ResNeXt101_wsl.md @@ -44,41 +44,21 @@ | EfficientNetB0_
small | 0.758 | 0.926 | | | 0.720 | 4.650 | -## FP16预测速度 - -| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) | -|:--:|:--:|:--:|:--:|:--:| -| ResNeXt101_
32x8d_wsl | 16.063 | 16.342 | 24.914 | 45.035 | -| ResNeXt101_
32x16d_wsl | 16.471 | 25.235 | 30.762 | 67.869 | -| ResNeXt101_
32x32d_wsl | 29.425 | 37.149 | 50.834 | | -| ResNeXt101_
32x48d_wsl | 40.311 | 58.414 | | | -| Fix_ResNeXt101_
32x48d_wsl | 43.960 | 86.514 | | | -| EfficientNetB0 | 1.759 | 2.748 | 3.761 | 10.178 | -| EfficientNetB1 | 2.592 | 4.122 | 5.829 | 16.262 | -| EfficientNetB2 | 2.866 | 4.715 | 7.064 | 20.954 | -| EfficientNetB3 | 3.869 | 6.815 | 10.672 | 34.097 | -| EfficientNetB4 | 5.626 | 11.937 | 19.753 | 67.436 | -| EfficientNetB5 | 8.907 | 21.685 | 37.248 | 134.185 | -| EfficientNetB6 | 13.591 | 34.093 | 60.976 | | -| EfficientNetB7 | 20.963 | 56.397 | 103.971 | | -| EfficientNetB0_
small | 1.039 | 1.665 | 2.493 | 7.748 | - - ## FP32预测速度 -| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) | -|:--:|:--:|:--:|:--:|:--:| -| ResNeXt101_
32x8d_wsl | 16.325 | 25.633 | 37.196 | 108.535 | -| ResNeXt101_
32x16d_wsl | 25.224 | 40.929 | 62.898 | | -| ResNeXt101_
32x32d_wsl | 41.047 | 79.575 | | | -| ResNeXt101_
32x48d_wsl | 60.610 | | | | -| Fix_ResNeXt101_
32x48d_wsl | 80.280 | | | | -| EfficientNetB0 | 1.902 | 3.296 | 4.361 | 11.319 | -| EfficientNetB1 | 2.908 | 5.093 | 6.900 | 18.015 | -| EfficientNetB2 | 3.324 | 5.832 | 8.357 | 23.371 | -| EfficientNetB3 | 4.557 | 8.526 | 12.485 | 38.124 | -| EfficientNetB4 | 6.767 | 14.742 | 23.218 | 77.590 | -| EfficientNetB5 | 11.097 | 26.642 | 43.590 | | -| EfficientNetB6 | 17.582 | 42.408 | 74.336 | | -| EfficientNetB7 | 26.529 | 70.337 | 126.839 | | -| EfficientNetB0_
small | 1.171 | 2.026 | 2.906 | 8.506 | +| Models | Crop Size | Resize Short Size | Batch Size=1
(ms) | +|-------------------------------|-----------|-------------------|--------------------------| +| ResNeXt101_
32x8d_wsl | 224 | 256 | 19.127 | +| ResNeXt101_
32x16d_wsl | 224 | 256 | 23.629 | +| ResNeXt101_
32x32d_wsl | 224 | 256 | 40.214 | +| ResNeXt101_
32x48d_wsl | 224 | 256 | 59.714 | +| Fix_ResNeXt101_
32x48d_wsl | 320 | 320 | 82.431 | +| EfficientNetB0 | 224 | 256 | 2.449 | +| EfficientNetB1 | 240 | 272 | 3.547 | +| EfficientNetB2 | 260 | 292 | 3.908 | +| EfficientNetB3 | 300 | 332 | 5.145 | +| EfficientNetB4 | 380 | 412 | 7.609 | +| EfficientNetB5 | 456 | 488 | 12.078 | +| EfficientNetB6 | 528 | 560 | 18.381 | +| EfficientNetB7 | 600 | 632 | 27.817 | +| EfficientNetB0_
small | 224 | 256 | 1.692 | diff --git a/docs/zh_CN/models/HRNet.md b/docs/zh_CN/models/HRNet.md index ac42a669..0ecbb364 100644 --- a/docs/zh_CN/models/HRNet.md +++ b/docs/zh_CN/models/HRNet.md @@ -19,27 +19,14 @@ | HRNet_W64_C | 0.793 | 0.946 | 0.795 | 0.946 | 57.830 | 128.060 | -## FP16预测速度 - -| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) | -|:--:|:--:|:--:|:--:|:--:| -| HRNet_W18_C | 6.188 | 7.207 | 9.149 | 18.221 | -| HRNet_W30_C | 7.941 | 8.851 | 10.540 | 21.129 | -| HRNet_W32_C | 7.904 | 8.890 | 10.752 | 21.159 | -| HRNet_W40_C | 9.233 | 11.600 | 13.927 | 29.868 | -| HRNet_W44_C | 9.917 | 12.119 | 15.555 | 31.948 | -| HRNet_W48_C | 10.198 | 12.399 | 15.572 | 32.210 | -| HRNet_W64_C | 12.264 | 14.552 | 18.251 | 41.106 | - - ## FP32预测速度 -| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) | -|:--:|:--:|:--:|:--:|:--:| -| HRNet_W18_C | 6.828 | 8.552 | 11.154 | 30.665 | -| HRNet_W30_C | 8.901 | 11.067 | 14.421 | 43.459 | -| HRNet_W32_C | 8.983 | 11.334 | 14.688 | 44.564 | -| HRNet_W40_C | 10.300 | 14.720 | 20.257 | 64.346 | -| HRNet_W44_C | 11.183 | 15.830 | 25.292 | 73.136 | -| HRNet_W48_C | 11.619 | 16.791 | 26.569 | 77.536 | -| HRNet_W64_C | 14.434 | 20.988 | 35.114 | 117.434 | +| Models | Crop Size | Resize Short Size | Batch Size=1
(ms) | +|-------------|-----------|-------------------|--------------------------| +| HRNet_W18_C | 224 | 256 | 7.368 | +| HRNet_W30_C | 224 | 256 | 9.495 | +| HRNet_W32_C | 224 | 256 | 9.292 | +| HRNet_W40_C | 224 | 256 | 10.739 | +| HRNet_W44_C | 224 | 256 | 11.497 | +| HRNet_W48_C | 224 | 256 | 12.165 | +| HRNet_W64_C | 224 | 256 | 15.003 | diff --git a/docs/zh_CN/models/Inception.md b/docs/zh_CN/models/Inception.md index f23defe2..c5104c48 100644 --- a/docs/zh_CN/models/Inception.md +++ b/docs/zh_CN/models/Inception.md @@ -19,27 +19,15 @@ GoogLeNet在预测时，图像的crop_size设置为224，resize_short_size设置 | InceptionV4 | 0.808 | 0.953 | 0.800 | 0.950 | 24.570 | 42.680 | -## FP16预测速度 - -| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) | -|:--:|:--:|:--:|:--:|:--:| -| GoogLeNet | 1.428 | 1.833 | 2.138 | 4.143 | -| Xception41 | 1.545 | 2.772 | 4.961 | 18.447 | -| Xception41
_deeplab | 1.630 | 2.647 | 4.462 | 16.354 | -| Xception65 | 5.398 | 4.215 | 8.611 | 28.702 | -| Xception65
_deeplab | 5.317 | 3.688 | 6.168 | 23.108 | -| Xception71 | 2.732 | 5.033 | 8.948 | 33.857 | -| InceptionV4 | 6.172 | 7.558 | 9.527 | 24.021 | - ## FP32预测速度 -| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) | -|:--:|:--:|:--:|:--:|:--:| -| GoogLeNet | 1.436 | 2.904 | 3.800 | 9.049 | -| Xception41 | 3.402 | 7.889 | 14.953 | 56.142 | -| Xception41
_deeplab | 3.778 | 8.396 | 15.449 | 58.735 | -| Xception65 | 6.802 | 13.935 | 34.301 | 87.256 | -| Xception65
_deeplab | 8.583 | 12.132 | 22.917 | 87.983 | -| Xception71 | 6.156 | 14.359 | 27.360 | 107.282 | -| InceptionV4 | 10.384 | 17.438 | 23.312 | 68.777 | +| Models | Crop Size | Resize Short Size | Batch Size=1
(ms) | +|------------------------|-----------|-------------------|--------------------------| +| GoogLeNet | 224 | 256 | 1.807 | +| Xception41 | 299 | 320 | 3.972 | +| Xception41
_deeplab | 299 | 320 | 4.408 | +| Xception65 | 299 | 320 | 6.174 | +| Xception65
_deeplab | 299 | 320 | 6.464 | +| Xception71 | 299 | 320 | 6.782 | +| InceptionV4 | 299 | 320 | 11.141 | diff --git a/docs/zh_CN/models/Mobile.md b/docs/zh_CN/models/Mobile.md index 3331e02d..8471b5b3 100644 --- a/docs/zh_CN/models/Mobile.md +++ b/docs/zh_CN/models/Mobile.md @@ -54,78 +54,42 @@ MobileNetV3是Google于2019年提出的一种基于NAS的新的轻量级网络 | ShuffleNetV2_swish | 0.700 | 0.892 | | | 0.290 | 2.260 | -## FP16预测速度 - -| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) | -|:--:|:--:|:--:|:--:|:--:| -| MobileNetV1_x0_25 | 0.236 | 0.258 | 0.281 | 0.556 | -| MobileNetV1_x0_5 | 0.246 | 0.318 | 0.364 | 0.845 | -| MobileNetV1_x0_75 | 0.303 | 0.380 | 0.512 | 1.164 | -| MobileNetV1 | 0.340 | 0.426 | 0.601 | 1.578 | -| MobileNetV1_ssld | 0.340 | 0.426 | 0.601 | 1.578 | -| MobileNetV2_x0_25 | 0.432 | 0.488 | 0.532 | 0.967 | -| MobileNetV2_x0_5 | 0.475 | 0.564 | 0.654 | 1.296 | -| MobileNetV2_x0_75 | 0.553 | 0.653 | 0.821 | 1.761 | -| MobileNetV2 | 0.610 | 0.738 | 0.931 | 2.115 | -| MobileNetV2_x1_5 | 0.731 | 0.966 | 1.252 | 3.152 | -| MobileNetV2_x2_0 | 0.870 | 1.123 | 1.494 | 3.910 | -| MobileNetV2_ssld | 0.610 | 0.738 | 0.931 | 2.115 | -| MobileNetV3_large_
x1_25 | 2.004 | 2.223 | 2.433 | 5.954 | -| MobileNetV3_large_
x1_0 | 1.943 | 2.203 | 2.113 | 4.823 | -| MobileNetV3_large_
x0_75 | 2.107 | 2.266 | 2.120 | 3.968 | -| MobileNetV3_large_
x0_5 | 1.942 | 2.178 | 2.179 | 2.936 | -| MobileNetV3_large_
x0_35 | 1.994 | 2.407 | 2.285 | 2.420 | -| MobileNetV3_small_
x1_25 | 1.876 | 2.141 | 2.118 | 3.423 | -| MobileNetV3_small_
x1_0 | 1.751 | 2.160 | 2.203 | 2.830 | -| MobileNetV3_small_
x0_75 | 1.856 | 2.235 | 2.166 | 2.464 | -| MobileNetV3_small_
x0_5 | 1.773 | 2.304 | 2.242 | 2.133 | -| MobileNetV3_small_
x0_35 | 1.870 | 2.392 | 2.323 | 2.101 | -| MobileNetV3_large_
x1_0_ssld | 1.943 | 2.203 | 2.113 | 4.823 | | -| MobileNetV3_small_
x1_0_ssld | 1.751 | 2.160 | 2.203 | 2.830 | -| ShuffleNetV2 | 1.134 | 1.068 | 1.199 | 2.558 | -| ShuffleNetV2_x0_25 | 0.911 | 0.953 | 0.948 | 1.327 | -| ShuffleNetV2_x0_33 | 0.853 | 1.072 | 0.958 | 1.398 | -| ShuffleNetV2_x0_5 | 0.858 | 1.059 | 1.084 | 1.620 | -| ShuffleNetV2_x1_5 | 1.040 | 1.153 | 1.394 | 3.452 | -| ShuffleNetV2_x2_0 | 1.061 | 1.316 | 1.694 | 4.485 | -| ShuffleNetV2_swish | 1.688 | 1.958 | 1.707 | 3.711 | - ## FP32预测速度 -| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) | -|:--:|:--:|:--:|:--:|:--:| -| MobileNetV1_x0_25 | 0.233 | 0.372 | 0.424 | 0.930 | -| MobileNetV1_x0_5 | 0.281 | 0.532 | 0.677 | 1.808 | -| MobileNetV1_x0_75 | 0.344 | 0.733 | 0.960 | 2.920 | -| MobileNetV1 | 0.420 | 0.963 | 1.462 | 4.769 | -| MobileNetV1_ssld | 0.420 | 0.963 | 1.462 | 4.769 | -| MobileNetV2_x0_25 | 0.718 | 0.738 | 0.775 | 1.482 | -| MobileNetV2_x0_5 | 0.818 | 0.975 | 1.107 | 2.481 | -| MobileNetV2_x0_75 | 0.830 | 1.104 | 1.514 | 3.629 | -| MobileNetV2 | 0.889 | 1.346 | 1.875 | 4.711 | -| MobileNetV2_x1_5 | 1.221 | 1.982 | 2.951 | 7.645 | -| MobileNetV2_x2_0 | 1.546 | 2.625 | 3.734 | 10.429 | -| MobileNetV2_ssld | 0.889 | 1.346 | 1.875 | 4.711 | -| MobileNetV3_large_
x1_25 | 2.113 | 2.377 | 3.114 | 7.332 | -| MobileNetV3_large_
x1_0 | 1.991 | 2.380 | 2.517 | 5.826 | -| MobileNetV3_large_
x0_75 | 2.105 | 2.454 | 2.336 | 4.611 | -| MobileNetV3_large_
x0_5 | 1.978 | 2.603 | 2.291 | 3.306 | -| MobileNetV3_large_
x0_35 | 2.017 | 2.469 | 2.316 | 2.558 | -| MobileNetV3_small_
x1_25 | 1.915 | 2.411 | 2.295 | 3.742 | -| MobileNetV3_small_
x1_0 | 1.915 | 2.889 | 2.862 | 3.022 | -| MobileNetV3_small_
x0_75 | 1.941 | 2.358 | 2.232 | 2.602 | -| MobileNetV3_small_
x0_5 | 1.872 | 2.364 | 2.238 | 2.061 | -| MobileNetV3_small_
x0_35 | 1.889 | 2.407 | 2.328 | 2.127 | -| MobileNetV3_large_
x1_0_ssld | 1.991 | 2.380 | 2.517 | 5.826 | -| MobileNetV3_small_
x1_0_ssld | 1.915 | 2.889 | 2.862 | 3.022 | -| ShuffleNetV2 | 1.328 | 1.211 | 1.440 | 3.210 | -| ShuffleNetV2_x0_25 | 0.905 | 0.908 | 0.924 | 1.284 | -| ShuffleNetV2_x0_33 | 0.871 | 1.073 | 0.891 | 1.416 | -| ShuffleNetV2_x0_5 | 0.852 | 1.150 | 1.093 | 1.702 | -| ShuffleNetV2_x1_5 | 0.874 | 1.470 | 1.889 | 4.490 | -| ShuffleNetV2_x2_0 | 1.443 | 1.908 | 2.556 | 6.864 | -| ShuffleNetV2_swish | 1.694 | 1.856 | 2.101 | 3.942 | +| Models | Crop Size | Resize Short Size | Batch Size=1
(ms) | +|--------------------------------------|-----------|-------------------|--------------------------| +| MobileNetV1_x0_25 | 224 | 256 | 0.492 | +| MobileNetV1_x0_5 | 224 | 256 | 0.599 | +| MobileNetV1_x0_75 | 224 | 256 | 0.695 | +| MobileNetV1 | 224 | 256 | 0.739 | +| MobileNetV1_ssld | 224 | 256 | 0.739 | +| MobileNetV2_x0_25 | 224 | 256 | 1.014 | +| MobileNetV2_x0_5 | 224 | 256 | 1.216 | +| MobileNetV2_x0_75 | 224 | 256 | 1.392 | +| MobileNetV2 | 224 | 256 | 1.153 | +| MobileNetV2_x1_5 | 224 | 256 | 1.516 | +| MobileNetV2_x2_0 | 224 | 256 | 1.819 | +| MobileNetV2_ssld | 224 | 256 | 1.153 | +| MobileNetV3_large_
x1_25 | 224 | 256 | 3.070 | +| MobileNetV3_large_
x1_0 | 224 | 256 | 3.173 | +| MobileNetV3_large_
x0_75 | 224 | 256 | 2.928 | +| MobileNetV3_large_
x0_5 | 224 | 256 | 2.979 | +| MobileNetV3_large_
x0_35 | 224 | 256 | 2.987 | +| MobileNetV3_small_
x1_25 | 224 | 256 | 3.003 | +| MobileNetV3_small_
x1_0 | 224 | 256 | 3.168 | +| MobileNetV3_small_
x0_75 | 224 | 256 | 2.974 | +| MobileNetV3_small_
x0_5 | 224 | 256 | 2.199 | +| MobileNetV3_small_
x0_35 | 224 | 256 | 2.240 | +| MobileNetV3_large_
x1_0_ssld | 224 | 256 | 3.173 | +| MobileNetV3_small_
x1_0_ssld | 224 | 256 | 3.168 | +| ShuffleNetV2 | 224 | 256 | 1.861 | +| ShuffleNetV2_x0_25 | 224 | 256 | 1.410 | +| ShuffleNetV2_x0_33 | 224 | 256 | 1.271 | +| ShuffleNetV2_x0_5 | 224 | 256 | 1.389 | +| ShuffleNetV2_x1_5 | 224 | 256 | 1.239 | +| ShuffleNetV2_x2_0 | 224 | 256 | 2.152 | +| ShuffleNetV2_swish | 224 | 256 | 2.150 | ## CPU预测速度和存储大小 diff --git a/docs/zh_CN/models/Others.md b/docs/zh_CN/models/Others.md index e57a6c97..35fd3732 100644 --- a/docs/zh_CN/models/Others.md +++ b/docs/zh_CN/models/Others.md @@ -22,31 +22,18 @@ DarkNet53在预测时，图像的crop_size设置为256，resize_short_size设置 | ResNet50_ACNet
_deploy | 0.767 | 0.932 | | | 8.190 | 25.550 | -## FP16预测速度 - -| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) | -|:--:|:--:|:--:|:--:|:--:| -| AlexNet | 0.684 | 0.740 | 0.810 | 1.481 | -| SqueezeNet1_0 | 0.545 | 0.841 | 1.146 | 3.501 | -| SqueezeNet1_1 | 0.473 | 0.575 | 0.805 | 1.862 | -| VGG11 | 1.096 | 1.655 | 2.396 | 6.728 | -| VGG13 | 1.216 | 2.059 | 3.056 | 9.468 | -| VGG16 | 1.518 | 2.594 | 4.019 | 12.145 | -| VGG19 | 1.817 | 3.124 | 4.886 | 14.958 | -| DarkNet53 | 2.150 | 2.627 | 3.422 | 10.092 | | -| ResNet50_ACNet
_deploy | 2.748 | 3.178 | 3.823 | 8.369 | - ## FP32预测速度 -| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) | -|:--:|:--:|:--:|:--:|:--:| -| AlexNet | 0.682 | 0.875 | 1.196 | 3.196 | -| SqueezeNet1_0 | 0.530 | 1.072 | 1.652 | 5.338 | -| SqueezeNet1_1 | 0.439 | 0.787 | 1.164 | 2.973 | -| VGG11 | 1.575 | 3.638 | 6.427 | 23.227 | -| VGG13 | 1.859 | 4.832 | 8.832 | 32.946 | -| VGG16 | 2.316 | 6.420 | 11.936 | 44.719 | -| VGG19 | 2.775 | 8.013 | 14.925 | 57.272 | -| DarkNet53 | 2.648 | 5.727 | 9.616 | 33.664 | | -| ResNet50_ACNet
_deploy | 4.544 | 6.873 | 9.627 | 28.283 | + +| Models | Crop Size | Resize Short Size | Batch Size=1
(ms) | +|---------------------------|-----------|-------------------|----------------------| +| AlexNet | 224 | 256 | 1.176 | +| SqueezeNet1_0 | 224 | 256 | 0.860 | +| SqueezeNet1_1 | 224 | 256 | 0.763 | +| VGG11 | 224 | 256 | 1.867 | +| VGG13 | 224 | 256 | 2.148 | +| VGG16 | 224 | 256 | 2.616 | +| VGG19 | 224 | 256 | 3.076 | +| DarkNet53 | 256 | 256 | 3.139 | +| ResNet50_ACNet
_deploy | 224 | 256 | 5.626 | diff --git a/docs/zh_CN/models/ResNet_and_vd.md b/docs/zh_CN/models/ResNet_and_vd.md index 7fc5e816..dc8d2896 100644 --- a/docs/zh_CN/models/ResNet_and_vd.md +++ b/docs/zh_CN/models/ResNet_and_vd.md @@ -32,43 +32,27 @@ ResNet系列模型是在2015年提出的，一举在ILSVRC2015比赛中取得冠 | ResNet152_vd | 0.806 | 0.953 | | | 23.530 | 60.210 | | ResNet200_vd | 0.809 | 0.953 | | | 30.530 | 74.740 | | ResNet50_vd_ssld | 0.824 | 0.961 | | | 8.670 | 25.580 | +| ResNet101_vd_ssld | 0.835 | 0.968 | | | 16.100 | 44.570 | -## FP16预测速度 - -| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) | -|:--:|:--:|:--:|:--:|:--:| -| ResNet18 | 0.966 | 1.076 | 1.263 | 2.656 | -| ResNet18_vd | 1.002 | 1.163 | 1.392 | 3.045 | -| ResNet34 | 1.798 | 1.959 | 2.269 | 4.716 | -| ResNet34_vd | 1.839 | 2.011 | 2.482 | 4.767 | -| ResNet50 | 1.892 | 2.146 | 2.692 | 6.411 | -| ResNet50_vc | 1.903 | 2.094 | 2.677 | 6.096 | -| ResNet50_vd | 1.918 | 2.273 | 2.833 | 6.978 | -| ResNet50_vd_v2 | 1.918 | 2.273 | 2.833 | 6.978 | -| ResNet101 | 3.790 | 4.128 | 4.789 | 10.913 | -| ResNet101_vd | 3.853 | 4.229 | 5.001 | 11.437 | -| ResNet152 | 5.523 | 5.871 | 6.710 | 15.258 | -| ResNet152_vd | 5.503 | 6.003 | 7.001 | 15.716 | -| ResNet200_vd | 7.270 | 7.595 | 8.802 | 19.516 | -| ResNet50_vd_ssld | 1.918 | 2.273 | 2.833 | 6.978 | ## FP32预测速度 -| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) | -|:--:|:--:|:--:|:--:|:--:| -| ResNet18 | 1.127 | 1.428 | 2.352 | 7.780 | -| ResNet18_vd | 1.142 | 1.532 | 2.584 | 8.441 | -| ResNet34 | 1.936 | 2.409 | 4.197 | 14.442 | -| ResNet34_vd | 1.948 | 2.526 | 4.403 | 15.133 | -| ResNet50 | 2.630 | 4.393 | 6.491 | 20.449 | -| ResNet50_vc | 2.728 | 4.413 | 6.618 | 21.183 | -| ResNet50_vd | 2.649 | 4.522 | 6.771 | 21.552 | -| ResNet50_vd_v2 | 2.649 | 4.522 | 6.771 | 21.552 | -| ResNet101 | 4.747 | 8.015 | 11.555 | 36.739 | -| ResNet101_vd | 4.735 | 8.111 | 11.820 | 37.155 | -| ResNet152 | 6.618 | 11.471 | 16.580 | 51.792 | -| ResNet152_vd | 6.626 | 11.613 | 16.843 | 53.645 | -| ResNet200_vd | 8.540 | 14.770 | 21.554 | 69.053 | -| ResNet50_vd_ssld | 2.649 | 4.522 | 6.771 | 21.552 | +| Models | Crop Size | Resize Short Size | Batch Size=1
(ms) | +|------------------|-----------|-------------------|--------------------------| +| ResNet18 | 224 | 256 | 1.499 | +| ResNet18_vd | 224 | 256 | 1.603 | +| ResNet34 | 224 | 256 | 2.272 | +| ResNet34_vd | 224 | 256 | 2.343 | +| ResNet50 | 224 | 256 | 2.939 | +| ResNet50_vc | 224 | 256 | 3.041 | +| ResNet50_vd | 224 | 256 | 3.165 | +| ResNet50_vd_v2 | 224 | 256 | 3.165 | +| ResNet101 | 224 | 256 | 5.314 | +| ResNet101_vd | 224 | 256 | 5.252 | +| ResNet152 | 224 | 256 | 7.205 | +| ResNet152_vd | 224 | 256 | 7.200 | +| ResNet200_vd | 224 | 256 | 8.885 | +| ResNet50_vd_ssld | 224 | 256 | 3.165 | +| ResNet101_vd_ssld | 224 | 256 | 5.252 | diff --git a/docs/zh_CN/models/SEResNext_and_Res2Net.md b/docs/zh_CN/models/SEResNext_and_Res2Net.md index 577b1b34..cf28c8e3 100644 --- a/docs/zh_CN/models/SEResNext_and_Res2Net.md +++ b/docs/zh_CN/models/SEResNext_and_Res2Net.md @@ -42,61 +42,32 @@ Res2Net是2019年提出的一种全新的对ResNet的改进方案，该方案可 | SENet154_vd | 0.814 | 0.955 | | | 45.830 | 114.290 | -## FP16预测速度 - -| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) | -|:--:|:--:|:--:|:--:|:--:| -| Res2Net50_26w_4s | 2.625 | 3.338 | 4.670 | 11.939 | -| Res2Net50_vd_26w_4s | 2.642 | 3.480 | 4.862 | 13.089 | -| Res2Net50_14w_8s | 3.393 | 4.237 | 5.473 | 13.979 | -| Res2Net101_vd_26w_4s | 5.128 | 6.190 | 7.995 | 20.534 | -| Res2Net200_vd_26w_4s | 9.594 | 11.131 | 14.278 | 36.258 | -| ResNeXt50_32x4d | 6.795 | 7.102 | 8.444 | 18.938 | -| ResNeXt50_vd_32x4d | 7.455 | 7.231 | 8.891 | 19.849 | -| ResNeXt50_64x4d | 20.279 | 12.343 | 13.633 | 32.772 | -| ResNeXt50_vd_64x4d | 16.325 | 21.773 | 25.007 | 55.329 | -| ResNeXt101_32x4d | 14.847 | 15.092 | 15.847 | 42.681 | -| ResNeXt101_vd_32x4d | 15.227 | 15.139 | 16.603 | 39.371 | -| ResNeXt101_64x4d | 28.221 | 29.455 | 29.873 | 59.415 | -| ResNeXt101_vd_64x4d | 31.051 | 28.160 | 28.915 | 60.525 | -| ResNeXt152_32x4d | 22.961 | 23.167 | 24.173 | 51.621 | -| ResNeXt152_vd_32x4d | 23.259 | 23.469 | 23.886 | 52.085 | -| ResNeXt152_64x4d | 41.930 | 42.441 | 45.985 | 79.405 | -| ResNeXt152_vd_64x4d | 42.778 | 43.281 | 45.017 | 79.728 | -| SE_ResNet18_vd | 1.256 | 1.463 | 1.917 | 4.316 | -| SE_ResNet34_vd | 2.314 | 2.691 | 3.432 | 7.411 | -| SE_ResNet50_vd | 2.884 | 4.051 | 5.421 | 15.013 | -| SE_ResNeXt50_32x4d | 7.973 | 10.613 | 12.788 | 29.091 | -| SE_ResNeXt50_vd_32x4d | 8.340 | 12.245 | 15.253 | 30.399 | -| SE_ResNeXt101_32x4d | 17.324 | 21.004 | 28.541 | 52.888 | -| SENet154_vd | 47.234 | 48.018 | 52.967 | 109.787 | - ## FP32预测速度 -| Models | batch_size=1
(ms) | batch_size=4
(ms) | batch_size=8
(ms) | batch_size=32
(ms) | -|:--:|:--:|:--:|:--:|:--:| -| Res2Net50_26w_4s | 3.711 | 5.855 | 8.450 | 26.084 | -| Res2Net50_vd_26w_4s | 3.651 | 5.986 | 8.747 | 26.772 | -| Res2Net50_14w_8s | 4.549 | 6.863 | 9.492 | 27.049 | -| Res2Net101_vd_26w_4s | 6.658 | 10.870 | 15.364 | 47.054 | -| Res2Net200_vd_26w_4s | 12.017 | 19.871 | 28.330 | 88.645 | -| ResNeXt50_32x4d | 6.747 | 8.862 | 11.961 | 32.782 | -| ResNeXt50_vd_32x4d | 6.746 | 9.037 | 12.279 | 33.496 | -| ResNeXt50_64x4d | 11.577 | 14.570 | 20.425 | 57.979 | -| ResNeXt50_vd_64x4d | 19.219 | 21.454 | 30.943 | 90.950 | -| ResNeXt101_32x4d | 14.652 | 18.082 | 24.148 | 70.200 | -| ResNeXt101_vd_32x4d | 14.927 | 18.454 | 23.894 | 67.334 | -| ResNeXt101_64x4d | 28.726 | 30.999 | 43.169 | 116.282 | -| ResNeXt101_vd_64x4d | 28.350 | 31.186 | 41.315 | 113.655 | -| ResNeXt152_32x4d | 23.578 | 27.323 | 35.588 | 99.121 | -| ResNeXt152_vd_32x4d | 23.548 | 26.879 | 35.091 | 104.832 | -| ResNeXt152_64x4d | 43.214 | 43.339 | 60.990 | 159.381 | -| ResNeXt152_vd_64x4d | 43.998 | 44.510 | 61.094 | 160.601 | -| SE_ResNet18_vd | 1.353 | 1.867 | 3.021 | 9.331 | -| SE_ResNet34_vd | 2.421 | 3.201 | 5.294 | 16.849 | -| SE_ResNet50_vd | 3.403 | 6.023 | 8.721 | 26.978 | -| SE_ResNeXt50_32x4d | 8.339 | 12.689 | 15.471 | 41.562 | -| SE_ResNeXt50_vd_32x4d | 7.849 | 13.530 | 16.810 | 44.020 | -| SE_ResNeXt101_32x4d | 16.853 | 24.409 | 32.666 | 81.806 | -| SENet154_vd | 46.002 | 53.666 | 70.589 | 180.334 | +| Models | Crop Size | Resize Short Size | Batch Size=1
(ms) | +|-----------------------|-----------|-------------------|--------------------------| +| Res2Net50_26w_4s | 224 | 256 | 4.148 | +| Res2Net50_vd_26w_4s | 224 | 256 | 4.172 | +| Res2Net50_14w_8s | 224 | 256 | 5.113 | +| Res2Net101_vd_26w_4s | 224 | 256 | 7.327 | +| Res2Net200_vd_26w_4s | 224 | 256 | 12.806 | +| ResNeXt50_32x4d | 224 | 256 | 10.964 | +| ResNeXt50_vd_32x4d | 224 | 256 | 7.566 | +| ResNeXt50_64x4d | 224 | 256 | 13.905 | +| ResNeXt50_vd_64x4d | 224 | 256 | 14.321 | +| ResNeXt101_32x4d | 224 | 256 | 14.915 | +| ResNeXt101_vd_32x4d | 224 | 256 | 14.885 | +| ResNeXt101_64x4d | 224 | 256 | 28.716 | +| ResNeXt101_vd_64x4d | 224 | 256 | 28.398 | +| ResNeXt152_32x4d | 224 | 256 | 22.996 | +| ResNeXt152_vd_32x4d | 224 | 256 | 22.729 | +| ResNeXt152_64x4d | 224 | 256 | 46.705 | +| ResNeXt152_vd_64x4d | 224 | 256 | 46.395 | +| SE_ResNet18_vd | 224 | 256 | 1.694 | +| SE_ResNet34_vd | 224 | 256 | 2.786 | +| SE_ResNet50_vd | 224 | 256 | 3.749 | +| SE_ResNeXt50_32x4d | 224 | 256 | 8.924 | +| SE_ResNeXt50_vd_32x4d | 224 | 256 | 9.011 | +| SE_ResNeXt101_32x4d | 224 | 256 | 19.204 | +| SENet154_vd | 224 | 256 | 50.406 | -- GitLab