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未验证 提交 f6cd9015 编写于 作者: B Bai Yifan 提交者： GitHub
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Fix model_zoo table format (#93)

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docs/zh_cn/model_zoo.md
浏览文件 @ f6cd9015
......@@ -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) |
<table border=0 cellpadding=0 cellspacing=0 width=861 style='border-collapse:
collapse;table-layout:fixed;width:644pt'>
<col width=87 style='width:65pt'>
<col width=124 style='mso-width-source:userset;mso-width-alt:3968;width:93pt'>
<col width=128 style='mso-width-source:userset;mso-width-alt:4096;width:96pt'>
<col width=87 span=6 style='width:65pt'>
<tr height=21 style='height:16.0pt'>
<td colspan=3 height=21 class=xl63 width=339 style='height:16.0pt;width:254pt'>分类模型Lite时延(ms)</td>
<td colspan=3 class=xl63 width=261 style='width:195pt'>armv7</td>
<td colspan=3 class=xl63 width=261 style='width:195pt'>armv8</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>设备</td>
<td class=xl63>模型类型</td>
<td class=xl63>压缩策略</td>
<td class=xl63>Thread 1</td>
<td class=xl63>Thread 2</td>
<td class=xl63>Thread 4</td>
<td class=xl63>Thread 1</td>
<td class=xl63>Thread 2</td>
<td class=xl63>Thread 4</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td rowspan=9 height=189 class=xl63 style='height:144.0pt'>高通835</td>
<td rowspan=3 class=xl63>MobileNetV1</td>
<td class=xl63>FP32 baseline</td>
<td class=xl63>96.1942</td>
<td class=xl63>53.2058</td>
<td class=xl63>32.4468</td>
<td class=xl63>88.4955</td>
<td class=xl63>47.95</td>
<td class=xl63>27.5189</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>quant_aware</td>
<td class=xl63>60.8186</td>
<td class=xl63>32.1931</td>
<td class=xl63>16.4275</td>
<td class=xl63>56.4311</td>
<td class=xl63>29.5446</td>
<td class=xl63>15.1053</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>quant_post</td>
<td class=xl63>60.5615</td>
<td class=xl63>32.4016</td>
<td class=xl63>16.6596</td>
<td class=xl63>56.5266</td>
<td class=xl63>29.7178</td>
<td class=xl63>15.1459</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td rowspan=3 height=63 class=xl63 style='height:48.0pt'>MobileNetV2</td>
<td class=xl63>FP32 baseline</td>
<td class=xl63>65.715</td>
<td class=xl63>38.1346</td>
<td class=xl63>25.155</td>
<td class=xl63>61.3593</td>
<td class=xl63>36.2038</td>
<td class=xl63>22.849</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>quant_aware</td>
<td class=xl63>48.3655</td>
<td class=xl63>30.2021</td>
<td class=xl63>21.9303</td>
<td class=xl63>46.1487</td>
<td class=xl63>27.3146</td>
<td class=xl63>18.3053</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>quant_post</td>
<td class=xl63>48.3495</td>
<td class=xl63>30.3069</td>
<td class=xl63>22.1506</td>
<td class=xl63>45.8715</td>
<td class=xl63>27.4105</td>
<td class=xl63>18.2223</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td rowspan=3 height=63 class=xl63 style='height:48.0pt'>ResNet50</td>
<td class=xl63>FP32 baseline</td>
<td class=xl63>526.811</td>
<td class=xl63>319.6486</td>
<td class=xl63>205.8345</td>
<td class=xl63>506.1138</td>
<td class=xl63>335.1584</td>
<td class=xl63>214.8936</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>quant_aware</td>
<td class=xl63>475.4538</td>
<td class=xl63>256.8672</td>
<td class=xl63>139.699</td>
<td class=xl63>461.7344</td>
<td class=xl63>247.9506</td>
<td class=xl63>145.9847</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>quant_post</td>
<td class=xl63>476.0507</td>
<td class=xl63>256.5963</td>
<td class=xl63>139.7266</td>
<td class=xl63>461.9176</td>
<td class=xl63>248.3795</td>
<td class=xl63>149.353</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td rowspan=9 height=189 class=xl63 style='height:144.0pt'>高通855</td>
<td rowspan=3 class=xl63>MobileNetV1</td>
<td class=xl63>FP32 baseline</td>
<td class=xl63>33.5086</td>
<td class=xl63>19.5773</td>
<td class=xl63>11.7534</td>
<td class=xl63>31.3474</td>
<td class=xl63>18.5382</td>
<td class=xl63>10.0811</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>quant_aware</td>
<td class=xl63>36.7067</td>
<td class=xl63>21.628</td>
<td class=xl63>11.0372</td>
<td class=xl63>14.0238</td>
<td class=xl63>8.199</td>
<td class=xl63>4.2588</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>quant_post</td>
<td class=xl63>37.0498</td>
<td class=xl63>21.7081</td>
<td class=xl63>11.0779</td>
<td class=xl63>14.0947</td>
<td class=xl63>8.1926</td>
<td class=xl63>4.2934</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td rowspan=3 height=63 class=xl63 style='height:48.0pt'>MobileNetV2</td>
<td class=xl63>FP32 baseline</td>
<td class=xl63>25.0396</td>
<td class=xl63>15.2862</td>
<td class=xl63>9.6609</td>
<td class=xl63>22.909</td>
<td class=xl63>14.1797</td>
<td class=xl63>8.8325</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>quant_aware</td>
<td class=xl63>28.1583</td>
<td class=xl63>18.3317</td>
<td class=xl63>11.8103</td>
<td class=xl63>16.9158</td>
<td class=xl63>11.1606</td>
<td class=xl63>7.4148</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>quant_post</td>
<td class=xl63>28.1631</td>
<td class=xl63>18.3917</td>
<td class=xl63>11.8333</td>
<td class=xl63>16.9399</td>
<td class=xl63>11.1772</td>
<td class=xl63>7.4176</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td rowspan=3 height=63 class=xl63 style='height:48.0pt'>ResNet50</td>
<td class=xl63>FP32 baseline</td>
<td class=xl63>185.3705</td>
<td class=xl63>113.0825</td>
<td class=xl63>87.0741</td>
<td class=xl63>177.7367</td>
<td class=xl63>110.0433</td>
<td class=xl63>74.4114</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>quant_aware</td>
<td class=xl63>327.6883</td>
<td class=xl63>202.4536</td>
<td class=xl63>106.243</td>
<td class=xl63>243.5621</td>
<td class=xl63>150.0542</td>
<td class=xl63>78.4205</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>quant_post</td>
<td class=xl63>328.2683</td>
<td class=xl63>201.9937</td>
<td class=xl63>106.744</td>
<td class=xl63>242.6397</td>
<td class=xl63>150.0338</td>
<td class=xl63>79.8659</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td rowspan=9 height=189 class=xl63 style='height:144.0pt'>麒麟970</td>
<td rowspan=3 class=xl63>MobileNetV1</td>
<td class=xl63>FP32 baseline</td>
<td class=xl63>101.2455</td>
<td class=xl63>56.4053</td>
<td class=xl63>35.6484</td>
<td class=xl63>94.8985</td>
<td class=xl63>51.7251</td>
<td class=xl63>31.9511</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>quant_aware</td>
<td class=xl63>62.5012</td>
<td class=xl63>32.1863</td>
<td class=xl63>16.6018</td>
<td class=xl63>57.7477</td>
<td class=xl63>29.2116</td>
<td class=xl63>15.0703</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>quant_post</td>
<td class=xl63>62.4412</td>
<td class=xl63>32.2585</td>
<td class=xl63>16.6215</td>
<td class=xl63>57.825</td>
<td class=xl63>29.2573</td>
<td class=xl63>15.1206</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td rowspan=3 height=63 class=xl63 style='height:48.0pt'>MobileNetV2</td>
<td class=xl63>FP32 baseline</td>
<td class=xl63>70.4176</td>
<td class=xl63>42.0795</td>
<td class=xl63>25.1939</td>
<td class=xl63>68.9597</td>
<td class=xl63>39.2145</td>
<td class=xl63>22.6617</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>quant_aware</td>
<td class=xl63>52.9961</td>
<td class=xl63>31.5323</td>
<td class=xl63>22.1447</td>
<td class=xl63>49.4858</td>
<td class=xl63>28.0856</td>
<td class=xl63>18.7287</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>quant_post</td>
<td class=xl63>53.0961</td>
<td class=xl63>31.7987</td>
<td class=xl63>21.8334</td>
<td class=xl63>49.383</td>
<td class=xl63>28.2358</td>
<td class=xl63>18.3642</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td rowspan=3 height=63 class=xl63 style='height:48.0pt'>ResNet50</td>
<td class=xl63>FP32 baseline</td>
<td class=xl63>586.8943</td>
<td class=xl63>344.0858</td>
<td class=xl63>228.2293</td>
<td class=xl63>573.3344</td>
<td class=xl63>351.4332</td>
<td class=xl63>225.8006</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>quant_aware</td>
<td class=xl63>488.361</td>
<td class=xl63>260.1697</td>
<td class=xl63>142.416</td>
<td class=xl63>479.5668</td>
<td class=xl63>249.8485</td>
<td class=xl63>138.1742</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl63 style='height:16.0pt'>quant_post</td>
<td class=xl63>489.6188</td>
<td class=xl63>258.3279</td>
<td class=xl63>142.6063</td>
<td class=xl63>480.0064</td>
<td class=xl63>249.5339</td>
<td class=xl63>138.5284</td>
</tr>
<![if supportMisalignedColumns]>
<tr height=0 style='display:none'>
<td width=87 style='width:65pt'></td>
<td width=124 style='width:93pt'></td>
<td width=128 style='width:96pt'></td>
<td width=87 style='width:65pt'></td>
<td width=87 style='width:65pt'></td>
<td width=87 style='width:65pt'></td>
<td width=87 style='width:65pt'></td>
<td width=87 style='width:65pt'></td>
<td width=87 style='width:65pt'></td>
</tr>
<![endif]>
</table>
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) |
<br/>
<table border=0 cellpadding=0 cellspacing=0 width=841 style='border-collapse:
collapse;table-layout:fixed;width:629pt'>
<col width=87 style='width:65pt'>
<col width=105 style='mso-width-source:userset;mso-width-alt:3370;width:79pt'>
<col width=127 style='mso-width-source:userset;mso-width-alt:4053;width:95pt'>
<col width=87 span=6 style='width:65pt'>
<tr height=21 style='height:16.0pt'>
<td colspan=3 height=21 class=xl65 width=319 style='height:16.0pt;width:239pt'>图像分割模型Lite时延(ms),
输入尺寸769x769</td>
<td colspan=3 class=xl65 width=261 style='width:195pt'>armv7</td>
<td colspan=3 class=xl65 width=261 style='width:195pt'>armv8</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl65 style='height:16.0pt'>设备</td>
<td class=xl65>模型类型</td>
<td class=xl65>压缩策略</td>
<td class=xl65>Thread 1</td>
<td class=xl65>Thread 2</td>
<td class=xl65>Thread 4</td>
<td class=xl65>Thread 1</td>
<td class=xl65>Thread 2</td>
<td class=xl65>Thread 4</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td rowspan=6 height=126 class=xl65 style='height:96.0pt'>高通835</td>
<td rowspan=3 class=xl66 width=105 style='width:79pt'>Deeplabv3- MobileNetV1</td>
<td class=xl65>FP32 baseline</td>
<td class=xl65>1227.9894</td>
<td class=xl65>734.1922</td>
<td class=xl65>527.9592</td>
<td class=xl65>1109.96</td>
<td class=xl65>699.3818</td>
<td class=xl65>479.0818</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl65 style='height:16.0pt'>quant_aware</td>
<td class=xl65>848.6544</td>
<td class=xl65>512.785</td>
<td class=xl65>382.9915</td>
<td class=xl65>752.3573</td>
<td class=xl65>455.0901</td>
<td class=xl65>307.8808</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl65 style='height:16.0pt'>quant_post</td>
<td class=xl65>840.2323</td>
<td class=xl65>510.103</td>
<td class=xl65>371.9315</td>
<td class=xl65>748.9401</td>
<td class=xl65>452.1745</td>
<td class=xl65>309.2084</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td rowspan=3 height=63 class=xl66 width=105 style='height:48.0pt;width:79pt'>Deeplabv3-MobileNetV2</td>
<td class=xl65>FP32 baseline</td>
<td class=xl65>1282.8126</td>
<td class=xl65>793.2064</td>
<td class=xl65>653.6538</td>
<td class=xl65>1193.9908</td>
<td class=xl65>737.1827</td>
<td class=xl65>593.4522</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl65 style='height:16.0pt'>quant_aware</td>
<td class=xl65>976.0495</td>
<td class=xl65>659.0541</td>
<td class=xl65>513.4279</td>
<td class=xl65>892.1468</td>
<td class=xl65>582.9847</td>
<td class=xl65>484.7512</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl65 style='height:16.0pt'>quant_post</td>
<td class=xl65>981.44</td>
<td class=xl65>658.4969</td>
<td class=xl65>538.6166</td>
<td class=xl65>885.3273</td>
<td class=xl65>586.1284</td>
<td class=xl65>484.0018</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td rowspan=6 height=126 class=xl65 style='height:96.0pt'>高通855</td>
<td rowspan=3 class=xl66 width=105 style='width:79pt'>Deeplabv3-MobileNetV1</td>
<td class=xl65>FP32 baseline</td>
<td class=xl65>568.8748</td>
<td class=xl65>339.8578</td>
<td class=xl65>278.6316</td>
<td class=xl65>420.6031</td>
<td class=xl65>281.3197</td>
<td class=xl65>217.5222</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl65 style='height:16.0pt'>quant_aware</td>
<td class=xl65>608.7578</td>
<td class=xl65>347.2087</td>
<td class=xl65>260.653</td>
<td class=xl65>241.2394</td>
<td class=xl65>177.3456</td>
<td class=xl65>143.9178</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl65 style='height:16.0pt'>quant_post</td>
<td class=xl65>609.0142</td>
<td class=xl65>347.3784</td>
<td class=xl65>259.9825</td>
<td class=xl65>239.4103</td>
<td class=xl65>180.1894</td>
<td class=xl65>139.9178</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td rowspan=3 height=63 class=xl66 width=105 style='height:48.0pt;width:79pt'>Deeplabv3-MobileNetV2</td>
<td class=xl65>FP32 baseline</td>
<td class=xl65>639.4425</td>
<td class=xl65>390.1851</td>
<td class=xl65>322.7014</td>
<td class=xl65>477.7667</td>
<td class=xl65>339.7411</td>
<td class=xl65>262.2847</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl65 style='height:16.0pt'>quant_aware</td>
<td class=xl65>703.7275</td>
<td class=xl65>497.689</td>
<td class=xl65>417.1296</td>
<td class=xl65>394.3586</td>
<td class=xl65>300.2503</td>
<td class=xl65>239.9204</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl65 style='height:16.0pt'>quant_post</td>
<td class=xl65>705.7589</td>
<td class=xl65>474.4076</td>
<td class=xl65>427.2951</td>
<td class=xl65>394.8352</td>
<td class=xl65>297.4035</td>
<td class=xl65>264.6724</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td rowspan=6 height=126 class=xl65 style='height:96.0pt'>麒麟970</td>
<td rowspan=3 class=xl66 width=105 style='width:79pt'>Deeplabv3-MobileNetV1</td>
<td class=xl65>FP32 baseline</td>
<td class=xl65>1682.1792</td>
<td class=xl65>1437.9774</td>
<td class=xl65>1181.0246</td>
<td class=xl65>1261.6739</td>
<td class=xl65>1068.6537</td>
<td class=xl65>690.8225</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl65 style='height:16.0pt'>quant_aware</td>
<td class=xl65>1062.3394</td>
<td class=xl65>1248.1014</td>
<td class=xl65>878.3157</td>
<td class=xl65>774.6356</td>
<td class=xl65>710.6277</td>
<td class=xl65>528.5376</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl65 style='height:16.0pt'>quant_post</td>
<td class=xl65>1109.1917</td>
<td class=xl65>1339.6218</td>
<td class=xl65>866.3587</td>
<td class=xl65>771.5164</td>
<td class=xl65>716.5255</td>
<td class=xl65>500.6497</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td rowspan=3 height=63 class=xl66 width=105 style='height:48.0pt;width:79pt'>Deeplabv3-MobileNetV2</td>
<td class=xl65>FP32 baseline</td>
<td class=xl65>1771.1301</td>
<td class=xl65>1746.0569</td>
<td class=xl65>1222.4805</td>
<td class=xl65>1448.9739</td>
<td class=xl65>1192.4491</td>
<td class=xl65>760.606</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl65 style='height:16.0pt'>quant_aware</td>
<td class=xl65>1320.2905</td>
<td class=xl65>921.4522</td>
<td class=xl65>676.0732</td>
<td class=xl65>1145.8801</td>
<td class=xl65>821.5685</td>
<td class=xl65>590.1713</td>
</tr>
<tr height=21 style='height:16.0pt'>
<td height=21 class=xl65 style='height:16.0pt'>quant_post</td>
<td class=xl65>1320.386</td>
<td class=xl65>918.5328</td>
<td class=xl65>672.2481</td>
<td class=xl65>1020.753</td>
<td class=xl65>820.094</td>
<td class=xl65>591.4114</td>
</tr>
<![if supportMisalignedColumns]>
<tr height=0 style='display:none'>
<td width=87 style='width:65pt'></td>
<td width=105 style='width:79pt'></td>
<td width=127 style='width:95pt'></td>
<td width=87 style='width:65pt'></td>
<td width=87 style='width:65pt'></td>
<td width=87 style='width:65pt'></td>
<td width=87 style='width:65pt'></td>
<td width=87 style='width:65pt'></td>
<td width=87 style='width:65pt'></td>
</tr>
<![endif]>
</table>
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
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