From f6cd9015927209be08fe67c84531d59c4828b14f Mon Sep 17 00:00:00 2001 From: Bai Yifan Date: Sat, 8 Feb 2020 01:43:26 +0800 Subject: [PATCH] Fix model_zoo table format (#93) --- docs/zh_cn/model_zoo.md | 560 +++++----------------------------------- 1 file changed, 59 insertions(+), 501 deletions(-) diff --git a/docs/zh_cn/model_zoo.md b/docs/zh_cn/model_zoo.md index 2f28a685..c8126cb7 100644 --- a/docs/zh_cn/model_zoo.md +++ b/docs/zh_cn/model_zoo.md @@ -18,297 +18,39 @@ |ResNet50|quant_post|76.33%/93.02% (-0.17%/+0.02%)| 25.1| 1.19 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_quant_post.tar) | |ResNet50|quant_aware| 76.48%/93.11% (-0.02%/+0.11%)| 25.1 | 1.17 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/ResNet50_quant_awre.tar) | - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
分类模型Lite时延(ms)armv7armv8
设备模型类型压缩策略Thread 1Thread 2Thread 4Thread 1Thread 2Thread 4
高通835MobileNetV1FP32 baseline96.194253.205832.446888.495547.9527.5189
quant_aware60.818632.193116.427556.431129.544615.1053
quant_post60.561532.401616.659656.526629.717815.1459
MobileNetV2FP32 baseline65.71538.134625.15561.359336.203822.849
quant_aware48.365530.202121.930346.148727.314618.3053
quant_post48.349530.306922.150645.871527.410518.2223
ResNet50FP32 baseline526.811319.6486205.8345506.1138335.1584214.8936
quant_aware475.4538256.8672139.699461.7344247.9506145.9847
quant_post476.0507256.5963139.7266461.9176248.3795149.353
高通855MobileNetV1FP32 baseline33.508619.577311.753431.347418.538210.0811
quant_aware36.706721.62811.037214.02388.1994.2588
quant_post37.049821.708111.077914.09478.19264.2934
MobileNetV2FP32 baseline25.039615.28629.660922.90914.17978.8325
quant_aware28.158318.331711.810316.915811.16067.4148
quant_post28.163118.391711.833316.939911.17727.4176
ResNet50FP32 baseline185.3705113.082587.0741177.7367110.043374.4114
quant_aware327.6883202.4536106.243243.5621150.054278.4205
quant_post328.2683201.9937106.744242.6397150.033879.8659
麒麟970MobileNetV1FP32 baseline101.245556.405335.648494.898551.725131.9511
quant_aware62.501232.186316.601857.747729.211615.0703
quant_post62.441232.258516.621557.82529.257315.1206
MobileNetV2FP32 baseline70.417642.079525.193968.959739.214522.6617
quant_aware52.996131.532322.144749.485828.085618.7287
quant_post53.096131.798721.833449.38328.235818.3642
ResNet50FP32 baseline586.8943344.0858228.2293573.3344351.4332225.8006
quant_aware488.361260.1697142.416479.5668249.8485138.1742
quant_post489.6188258.3279142.6063480.0064249.5339138.5284
+分类模型Lite时延(ms) + +| 设备 | 模型类型 | 压缩策略 | armv7 Thread 1 | armv7 Thread 2 | armv7 Thread 4 | armv8 Thread 1 | armv8 Thread 2 | armv8 Thread 4 | +| ------- | ----------- | ------------- | -------------- | -------------- | -------------- | -------------- | -------------- | -------------- | +| 高通835 | MobileNetV1 | FP32 baseline | 96.1942 | 53.2058 | 32.4468 | 88.4955 | 47.95 | 27.5189 | +| 高通835 | MobileNetV1 | quant_aware | 60.8186 | 32.1931 | 16.4275 | 56.4311 | 29.5446 | 15.1053 | +| 高通835 | MobileNetV1 | quant_post | 60.5615 | 32.4016 | 16.6596 | 56.5266 | 29.7178 | 15.1459 | +| 高通835 | MobileNetV2 | FP32 baseline | 65.715 | 38.1346 | 25.155 | 61.3593 | 36.2038 | 22.849 | +| 高通835 | MobileNetV2 | quant_aware | 48.3655 | 30.2021 | 21.9303 | 46.1487 | 27.3146 | 18.3053 | +| 高通835 | MobileNetV2 | quant_post | 48.3495 | 30.3069 | 22.1506 | 45.8715 | 27.4105 | 18.2223 | +| 高通835 | ResNet50 | FP32 baseline | 526.811 | 319.6486 | 205.8345 | 506.1138 | 335.1584 | 214.8936 | +| 高通835 | ResNet50 | quant_aware | 475.4538 | 256.8672 | 139.699 | 461.7344 | 247.9506 | 145.9847 | +| 高通835 | ResNet50 | quant_post | 476.0507 | 256.5963 | 139.7266 | 461.9176 | 248.3795 | 149.353 | +| 高通855 | MobileNetV1 | FP32 baseline | 33.5086 | 19.5773 | 11.7534 | 31.3474 | 18.5382 | 10.0811 | +| 高通855 | MobileNetV1 | quant_aware | 36.7067 | 21.628 | 11.0372 | 14.0238 | 8.199 | 4.2588 | +| 高通855 | MobileNetV1 | quant_post | 37.0498 | 21.7081 | 11.0779 | 14.0947 | 8.1926 | 4.2934 | +| 高通855 | MobileNetV2 | FP32 baseline | 25.0396 | 15.2862 | 9.6609 | 22.909 | 14.1797 | 8.8325 | +| 高通855 | MobileNetV2 | quant_aware | 28.1583 | 18.3317 | 11.8103 | 16.9158 | 11.1606 | 7.4148 | +| 高通855 | MobileNetV2 | quant_post | 28.1631 | 18.3917 | 11.8333 | 16.9399 | 11.1772 | 7.4176 | +| 高通855 | ResNet50 | FP32 baseline | 185.3705 | 113.0825 | 87.0741 | 177.7367 | 110.0433 | 74.4114 | +| 高通855 | ResNet50 | quant_aware | 327.6883 | 202.4536 | 106.243 | 243.5621 | 150.0542 | 78.4205 | +| 高通855 | ResNet50 | quant_post | 328.2683 | 201.9937 | 106.744 | 242.6397 | 150.0338 | 79.8659 | +| 麒麟970 | MobileNetV1 | FP32 baseline | 101.2455 | 56.4053 | 35.6484 | 94.8985 | 51.7251 | 31.9511 | +| 麒麟970 | MobileNetV1 | quant_aware | 62.5012 | 32.1863 | 16.6018 | 57.7477 | 29.2116 | 15.0703 | +| 麒麟970 | MobileNetV1 | quant_post | 62.4412 | 32.2585 | 16.6215 | 57.825 | 29.2573 | 15.1206 | +| 麒麟970 | MobileNetV2 | FP32 baseline | 70.4176 | 42.0795 | 25.1939 | 68.9597 | 39.2145 | 22.6617 | +| 麒麟970 | MobileNetV2 | quant_aware | 52.9961 | 31.5323 | 22.1447 | 49.4858 | 28.0856 | 18.7287 | +| 麒麟970 | MobileNetV2 | quant_post | 53.0961 | 31.7987 | 21.8334 | 49.383 | 28.2358 | 18.3642 | +| 麒麟970 | ResNet50 | FP32 baseline | 586.8943 | 344.0858 | 228.2293 | 573.3344 | 351.4332 | 225.8006 | +| 麒麟970 | ResNet50 | quant_aware | 488.361 | 260.1697 | 142.416 | 479.5668 | 249.8485 | 138.1742 | +| 麒麟970 | ResNet50 | quant_post | 489.6188 | 258.3279 | 142.6063 | 480.0064 | 249.5339 | 138.5284 | + + @@ -430,216 +172,32 @@ | DeepLabv3+/MobileNetv2 | quant_post | 67.59 (-2.22) | 2.1 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/deeplabv3_mobilenetv2_2049x1025_quant_post.tar) | | DeepLabv3+/MobileNetv2 | quant_aware | 68.33 (-1.48) | 2.1 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/deeplabv3_mobilenetv2_2049x1025_quant_aware.tar) | -
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图像分割模型Lite时延(ms), - 输入尺寸769x769armv7armv8
设备模型类型压缩策略Thread 1Thread 2Thread 4Thread 1Thread 2Thread 4
高通835Deeplabv3- MobileNetV1FP32 baseline1227.9894734.1922527.95921109.96699.3818479.0818
quant_aware848.6544512.785382.9915752.3573455.0901307.8808
quant_post840.2323510.103371.9315748.9401452.1745309.2084
Deeplabv3-MobileNetV2FP32 baseline1282.8126793.2064653.65381193.9908737.1827593.4522
quant_aware976.0495659.0541513.4279892.1468582.9847484.7512
quant_post981.44658.4969538.6166885.3273586.1284484.0018
高通855Deeplabv3-MobileNetV1FP32 baseline568.8748339.8578278.6316420.6031281.3197217.5222
quant_aware608.7578347.2087260.653241.2394177.3456143.9178
quant_post609.0142347.3784259.9825239.4103180.1894139.9178
Deeplabv3-MobileNetV2FP32 baseline639.4425390.1851322.7014477.7667339.7411262.2847
quant_aware703.7275497.689417.1296394.3586300.2503239.9204
quant_post705.7589474.4076427.2951394.8352297.4035264.6724
麒麟970Deeplabv3-MobileNetV1FP32 baseline1682.17921437.97741181.02461261.67391068.6537690.8225
quant_aware1062.33941248.1014878.3157774.6356710.6277528.5376
quant_post1109.19171339.6218866.3587771.5164716.5255500.6497
Deeplabv3-MobileNetV2FP32 baseline1771.13011746.05691222.48051448.97391192.4491760.606
quant_aware1320.2905921.4522676.07321145.8801821.5685590.1713
quant_post1320.386918.5328672.24811020.753820.094591.4114
+图像分割模型Lite时延(ms), 输入尺寸769x769 + +| 设备 | 模型类型 | 压缩策略 | armv7 Thread 1 | armv7 Thread 2 | armv7 Thread 4 | armv8 Thread 1 | armv8 Thread 2 | armv8 Thread 4 | +| ------- | ---------------------- | ------------- | -------------- | -------------- | -------------- | -------------- | -------------- | -------------- | +| 高通835 | Deeplabv3- MobileNetV1 | FP32 baseline | 1227.9894 | 734.1922 | 527.9592 | 1109.96 | 699.3818 | 479.0818 | +| 高通835 | Deeplabv3- MobileNetV1 | quant_aware | 848.6544 | 512.785 | 382.9915 | 752.3573 | 455.0901 | 307.8808 | +| 高通835 | Deeplabv3- MobileNetV1 | quant_post | 840.2323 | 510.103 | 371.9315 | 748.9401 | 452.1745 | 309.2084 | +| 高通835 | Deeplabv3-MobileNetV2 | FP32 baseline | 1282.8126 | 793.2064 | 653.6538 | 1193.9908 | 737.1827 | 593.4522 | +| 高通835 | Deeplabv3-MobileNetV2 | quant_aware | 976.0495 | 659.0541 | 513.4279 | 892.1468 | 582.9847 | 484.7512 | +| 高通835 | Deeplabv3-MobileNetV2 | quant_post | 981.44 | 658.4969 | 538.6166 | 885.3273 | 586.1284 | 484.0018 | +| 高通855 | Deeplabv3- MobileNetV1 | FP32 baseline | 568.8748 | 339.8578 | 278.6316 | 420.6031 | 281.3197 | 217.5222 | +| 高通855 | Deeplabv3- MobileNetV1 | quant_aware | 608.7578 | 347.2087 | 260.653 | 241.2394 | 177.3456 | 143.9178 | +| 高通855 | Deeplabv3- MobileNetV1 | quant_post | 609.0142 | 347.3784 | 259.9825 | 239.4103 | 180.1894 | 139.9178 | +| 高通855 | Deeplabv3-MobileNetV2 | FP32 baseline | 639.4425 | 390.1851 | 322.7014 | 477.7667 | 339.7411 | 262.2847 | +| 高通855 | Deeplabv3-MobileNetV2 | quant_aware | 703.7275 | 497.689 | 417.1296 | 394.3586 | 300.2503 | 239.9204 | +| 高通855 | Deeplabv3-MobileNetV2 | quant_post | 705.7589 | 474.4076 | 427.2951 | 394.8352 | 297.4035 | 264.6724 | +| 麒麟970 | Deeplabv3- MobileNetV1 | FP32 baseline | 1682.1792 | 1437.9774 | 1181.0246 | 1261.6739 | 1068.6537 | 690.8225 | +| 麒麟970 | Deeplabv3- MobileNetV1 | quant_aware | 1062.3394 | 1248.1014 | 878.3157 | 774.6356 | 710.6277 | 528.5376 | +| 麒麟970 | Deeplabv3- MobileNetV1 | quant_post | 1109.1917 | 1339.6218 | 866.3587 | 771.5164 | 716.5255 | 500.6497 | +| 麒麟970 | Deeplabv3-MobileNetV2 | FP32 baseline | 1771.1301 | 1746.0569 | 1222.4805 | 1448.9739 | 1192.4491 | 760.606 | +| 麒麟970 | Deeplabv3-MobileNetV2 | quant_aware | 1320.2905 | 921.4522 | 676.0732 | 1145.8801 | 821.5685 | 590.1713 | +| 麒麟970 | Deeplabv3-MobileNetV2 | quant_post | 1320.386 | 918.5328 | 672.2481 | 1020.753 | 820.094 | 591.4114 | + + + + ### 3.2 剪裁 -- GitLab