模型库¶
1. 图象分类¶
数据集：ImageNet1000类
1.1 量化¶
| 模型 | 压缩方法 | Top-1/Top-5 Acc | 模型体积（MB） | TensorRT时延(V100, ms) | 下载 |
|---|---|---|---|---|---|
| MobileNetV1 | - | 70.99%/89.68% | 17 | - | 下载链接 |
| MobileNetV1 | quant_post | 70.18%/89.25% (-0.81%/-0.43%) | 4.4 | - | 下载链接 |
| MobileNetV1 | quant_aware | 70.60%/89.57% (-0.39%/-0.11%) | 4.4 | - | 下载链接 |
| MobileNetV2 | - | 72.15%/90.65% | 15 | - | 下载链接 |
| MobileNetV2 | quant_post | 71.15%/90.11% (-1%/-0.54%) | 4.0 | - | 下载链接 |
| MobileNetV2 | quant_aware | 72.05%/90.63% (-0.1%/-0.02%) | 4.0 | - | 下载链接 |
| ResNet50 | - | 76.50%/93.00% | 99 | 2.71 | 下载链接 |
| ResNet50 | quant_post | 76.33%/93.02% (-0.17%/+0.02%) | 25.1 | 1.19 | 下载链接 |
| ResNet50 | quant_aware | 76.48%/93.11% (-0.02%/+0.11%) | 25.1 | 1.17 | 下载链接 |
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 |
1.2 剪裁¶
| 模型 | 压缩方法 | Top-1/Top-5 Acc | 模型体积（MB） | GFLOPs | 下载 |
|---|---|---|---|---|---|
| MobileNetV1 | Baseline | 70.99%/89.68% | 17 | 1.11 | 下载链接 |
| MobileNetV1 | uniform -50% | 69.4%/88.66% (-1.59%/-1.02%) | 9 | 0.56 | 下载链接 |
| MobileNetV1 | sensitive -30% | 70.4%/89.3% (-0.59%/-0.38%) | 12 | 0.74 | 下载链接 |
| MobileNetV1 | sensitive -50% | 69.8% / 88.9% (-1.19%/-0.78%) | 9 | 0.56 | 下载链接 |
| MobileNetV2 | - | 72.15%/90.65% | 15 | 0.59 | 下载链接 |
| MobileNetV2 | uniform -50% | 65.79%/86.11% (-6.35%/-4.47%) | 11 | 0.296 | 下载链接 |
| ResNet34 | - | 72.15%/90.65% | 84 | 7.36 | 下载链接 |
| ResNet34 | uniform -50% | 70.99%/89.95% (-1.36%/-0.87%) | 41 | 3.67 | 下载链接 |
| ResNet34 | auto -55.05% | 70.24%/89.63% (-2.04%/-1.06%) | 33 | 3.31 | 下载链接 |
1.3 蒸馏¶
| 模型 | 压缩方法 | Top-1/Top-5 Acc | 模型体积（MB） | 下载 |
|---|---|---|---|---|
| MobileNetV1 | student | 70.99%/89.68% | 17 | 下载链接 |
| ResNet50_vd | teacher | 79.12%/94.44% | 99 | 下载链接 |
| MobileNetV1 | ResNet50_vd1 distill | 72.77%/90.68% (+1.78%/+1.00%) | 17 | 下载链接 |
| MobileNetV2 | student | 72.15%/90.65% | 15 | 下载链接 |
| MobileNetV2 | ResNet50_vd distill | 74.28%/91.53% (+2.13%/+0.88%) | 15 | 下载链接 |
| ResNet50 | student | 76.50%/93.00% | 99 | 下载链接 |
| ResNet101 | teacher | 77.56%/93.64% | 173 | 下载链接 |
| ResNet50 | ResNet101 distill | 77.29%/93.65% (+0.79%/+0.65%) | 99 | 下载链接 |
!!! note “Note“
<a name="trans1">[1]</a>：带_vd后缀代表该预训练模型使用了Mixup，Mixup相关介绍参考[mixup: Beyond Empirical Risk Minimization](https://arxiv.org/abs/1710.09412)
2. 目标检测¶
2.1 量化¶
数据集： COCO 2017
| 模型 | 压缩方法 | 数据集 | Image/GPU | 输入608 Box AP | 输入416 Box AP | 输入320 Box AP | 模型体积（MB） | TensorRT时延(V100, ms) | 下载 |
|---|---|---|---|---|---|---|---|---|---|
| MobileNet-V1-YOLOv3 | - | COCO | 8 | 29.3 | 29.3 | 27.1 | 95 | - | 下载链接 |
| MobileNet-V1-YOLOv3 | quant_post | COCO | 8 | 27.9 (-1.4) | 28.0 (-1.3) | 26.0 (-1.0) | 25 | - | 下载链接 |
| MobileNet-V1-YOLOv3 | quant_aware | COCO | 8 | 28.1 (-1.2) | 28.2 (-1.1) | 25.8 (-1.2) | 26.3 | - | 下载链接 |
| R34-YOLOv3 | - | COCO | 8 | 36.2 | 34.3 | 31.4 | 162 | - | 下载链接 |
| R34-YOLOv3 | quant_post | COCO | 8 | 35.7 (-0.5) | - | - | 42.7 | - | 下载链接 |
| R34-YOLOv3 | quant_aware | COCO | 8 | 35.2 (-1.0) | 33.3 (-1.0) | 30.3 (-1.1) | 44 | - | 下载链接 |
| R50-dcn-YOLOv3 obj365_pretrain | - | COCO | 8 | 41.4 | - | - | 177 | 18.56 | 下载链接 |
| R50-dcn-YOLOv3 obj365_pretrain | quant_aware | COCO | 8 | 40.6 (-0.8) | 37.5 | 34.1 | 66 | 14.64 | 下载链接 |
数据集：WIDER-FACE
| 模型 | 压缩方法 | Image/GPU | 输入尺寸 | Easy/Medium/Hard | 模型体积（MB） | 下载 |
|---|---|---|---|---|---|---|
| BlazeFace | - | 8 | 640 | 91.5/89.2/79.7 | 815 | 下载链接 |
| BlazeFace | quant_post | 8 | 640 | 87.8/85.1/74.9 (-3.7/-4.1/-4.8) | 228 | 下载链接 |
| BlazeFace | quant_aware | 8 | 640 | 90.5/87.9/77.6 (-1.0/-1.3/-2.1) | 228 | 下载链接 |
| BlazeFace-Lite | - | 8 | 640 | 90.9/88.5/78.1 | 711 | 下载链接 |
| BlazeFace-Lite | quant_post | 8 | 640 | 89.4/86.7/75.7 (-1.5/-1.8/-2.4) | 211 | 下载链接 |
| BlazeFace-Lite | quant_aware | 8 | 640 | 89.7/87.3/77.0 (-1.2/-1.2/-1.1) | 211 | 下载链接 |
| BlazeFace-NAS | - | 8 | 640 | 83.7/80.7/65.8 | 244 | 下载链接 |
| BlazeFace-NAS | quant_post | 8 | 640 | 81.6/78.3/63.6 (-2.1/-2.4/-2.2) | 71 | 下载链接 |
| BlazeFace-NAS | quant_aware | 8 | 640 | 83.1/79.7/64.2 (-0.6/-1.0/-1.6) | 71 | 下载链接 |
2.2 剪裁¶
数据集：Pasacl VOC & COCO 2017
| 模型 | 压缩方法 | 数据集 | Image/GPU | 输入608 Box AP | 输入416 Box AP | 输入320 Box AP | 模型体积(MB) | GFLOPs (608*608) | 下载 |
|---|---|---|---|---|---|---|---|---|---|
| MobileNet-V1-YOLOv3 | Baseline | Pascal VOC | 8 | 76.2 | 76.7 | 75.3 | 94 | 40.49 | 下载链接 |
| MobileNet-V1-YOLOv3 | sensitive -52.88% | Pascal VOC | 8 | 77.6 (+1.4) | 77.7 (1.0) | 75.5 (+0.2) | 31 | 19.08 | 下载链接 |
| MobileNet-V1-YOLOv3 | - | COCO | 8 | 29.3 | 29.3 | 27.0 | 95 | 41.35 | 下载链接 |
| MobileNet-V1-YOLOv3 | sensitive -51.77% | COCO | 8 | 26.0 (-3.3) | 25.1 (-4.2) | 22.6 (-4.4) | 32 | 19.94 | 下载链接 |
| R50-dcn-YOLOv3 | - | COCO | 8 | 39.1 | - | - | 177 | 89.60 | 下载链接 |
| R50-dcn-YOLOv3 | sensitive -9.37% | COCO | 8 | 39.3 (+0.2) | - | - | 150 | 81.20 | 下载链接 |
| R50-dcn-YOLOv3 | sensitive -24.68% | COCO | 8 | 37.3 (-1.8) | - | - | 113 | 67.48 | 下载链接 |
| R50-dcn-YOLOv3 obj365_pretrain | - | COCO | 8 | 41.4 | - | - | 177 | 89.60 | 下载链接 |
| R50-dcn-YOLOv3 obj365_pretrain | sensitive -9.37% | COCO | 8 | 40.5 (-0.9) | - | - | 150 | 81.20 | 下载链接 |
| R50-dcn-YOLOv3 obj365_pretrain | sensitive -24.68% | COCO | 8 | 37.8 (-3.3) | - | - | 113 | 67.48 | 下载链接 |
2.3 蒸馏¶
数据集：Pasacl VOC & COCO 2017
| 模型 | 压缩方法 | 数据集 | Image/GPU | 输入608 Box AP | 输入416 Box AP | 输入320 Box AP | 模型体积（MB） | 下载 |
|---|---|---|---|---|---|---|---|---|
| MobileNet-V1-YOLOv3 | - | Pascal VOC | 8 | 76.2 | 76.7 | 75.3 | 94 | 下载链接 |
| ResNet34-YOLOv3 | - | Pascal VOC | 8 | 82.6 | 81.9 | 80.1 | 162 | 下载链接 |
| MobileNet-V1-YOLOv3 | ResNet34-YOLOv3 distill | Pascal VOC | 8 | 79.0 (+2.8) | 78.2 (+1.5) | 75.5 (+0.2) | 94 | 下载链接 |
| MobileNet-V1-YOLOv3 | - | COCO | 8 | 29.3 | 29.3 | 27.0 | 95 | 下载链接 |
| ResNet34-YOLOv3 | - | COCO | 8 | 36.2 | 34.3 | 31.4 | 163 | 下载链接 |
| MobileNet-V1-YOLOv3 | ResNet34-YOLOv3 distill | COCO | 8 | 31.4 (+2.1) | 30.0 (+0.7) | 27.1 (+0.1) | 95 | 下载链接 |
3. 图像分割¶
数据集：Cityscapes
3.1 量化¶
| 模型 | 压缩方法 | mIoU | 模型体积（MB） | 下载 |
|---|---|---|---|---|
| DeepLabv3+/MobileNetv1 | - | 63.26 | 6.6 | 下载链接 |
| DeepLabv3+/MobileNetv1 | quant_post | 58.63 (-4.63) | 1.8 | 下载链接 |
| DeepLabv3+/MobileNetv1 | quant_aware | 62.03 (-1.23) | 1.8 | 下载链接 |
| DeepLabv3+/MobileNetv2 | - | 69.81 | 7.4 | 下载链接 |
| DeepLabv3+/MobileNetv2 | quant_post | 67.59 (-2.22) | 2.1 | 下载链接 |
| DeepLabv3+/MobileNetv2 | quant_aware | 68.33 (-1.48) | 2.1 | 下载链接 |
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 |