deeplearning模型库
作者：互联网
deeplearning模型库
1. 图像分类
数据集：ImageNet1000类
1.1 量化
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
PaddleLite推理耗时说明：
环境：Qualcomm SnapDragon 845 + armv8
速度指标：Thread1/Thread2/Thread4耗时
PaddleLite版本： v2.3
|
模型 |
压缩方法 |
Top-1/Top-5 Acc |
模型体积（MB） |
GFLOPs |
PaddleLite推理耗时 |
TensorRT推理速度(FPS) |
|
MobileNetV1 |
Baseline |
70.99%/89.68% |
17 |
1.11 |
66.052\35.8014\19.5762 |
- |
|
MobileNetV1 |
uniform -50% |
69.4%/88.66% (-1.59%/-1.02%) |
9 |
0.56 |
33.5636\18.6834\10.5076 |
- |
|
MobileNetV1 |
sensitive -30% |
70.4%/89.3% (-0.59%/-0.38%) |
12 |
0.74 |
46.5958\25.3098\13.6982 |
- |
|
MobileNetV1 |
sensitive -50% |
69.8% / 88.9% (-1.19%/-0.78%) |
9 |
0.56 |
37.9892\20.7882\11.3144 |
- |
|
MobileNetV2 |
- |
72.15%/90.65% |
15 |
0.59 |
41.7874\23.375\13.3998 |
- |
|
MobileNetV2 |
uniform -50% |
65.79%/86.11% (-6.35%/-4.47%) |
11 |
0.296 |
23.8842\13.8698\8.5572 |
- |
|
ResNet34 |
- |
72.15%/90.65% |
84 |
7.36 |
217.808\139.943\96.7504 |
342.32 |
|
ResNet34 |
uniform -50% |
70.99%/89.95% (-1.36%/-0.87%) |
41 |
3.67 |
114.787\75.0332\51.8438 |
452.41 |
|
ResNet34 |
auto -55.05% |
70.24%/89.63% (-2.04%/-1.06%) |
33 |
3.31 |
105.924\69.3222\48.0246 |
457.25 |
1.3 蒸馏
|
模型 |
压缩方法 |
Top-1/Top-5 Acc |
模型体积（MB） |
|
MobileNetV1 |
student |
70.99%/89.68% |
17 |
|
ResNet50_vd |
teacher |
79.12%/94.44% |
99 |
|
MobileNetV1 |
ResNet50_vd1 distill |
72.77%/90.68% (+1.78%/+1.00%) |
17 |
|
MobileNetV2 |
student |
72.15%/90.65% |
15 |
|
MobileNetV2 |
ResNet50_vd distill |
74.28%/91.53% (+2.13%/+0.88%) |
15 |
|
ResNet50 |
student |
76.50%/93.00% |
99 |
|
ResNet101 |
teacher |
77.56%/93.64% |
173 |
|
ResNet50 |
ResNet101 distill |
77.29%/93.65% (+0.79%/+0.65%) |
99 |
注意：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
1.4 搜索
数据集: ImageNet1000
|
模型 |
压缩方法 |
Top-1/Top-5 Acc |
模型体积（MB） |
GFLOPs |
|
MobileNetV2 |
- |
72.15%/90.65% |
15 |
0.59 |
|
MobileNetV2 |
SANAS |
71.518%/90.208% (-0.632%/-0.442%) |
14 |
0.295 |
数据集: Cifar10
|
模型 |
压缩方法 |
Acc |
模型参数（MB） |
下载 |
|
Darts |
- |
97.135% |
3.767 |
- |
|
Darts_SA(基于Darts搜索空间) |
SANAS |
97.276%(+0.141%) |
3.344(-11.2%) |
- |
Note: MobileNetV2_NAS 的token是：[4, 4, 5, 1, 1, 2, 1, 1, 0, 2, 6, 2, 0, 3, 4, 5, 0, 4, 5, 5, 1, 4, 8, 0, 0]. Darts_SA的token是：[5, 5, 0, 5, 5, 10, 7, 7, 5, 7, 7, 11, 10, 12, 10, 0, 5, 3, 10, 8].
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) |
|
MobileNet-V1-YOLOv3 |
- |
COCO |
8 |
29.3 |
29.3 |
27.1 |
95 |
- |
|
MobileNet-V1-YOLOv3 |
quant_post |
COCO |
8 |
27.9 (-1.4) |
28.0 (-1.3) |
26.0 (-1.0) |
25 |
- |
|
MobileNet-V1-YOLOv3 |
quant_aware |
COCO |
8 |
28.1 (-1.2) |
28.2 (-1.1) |
25.8 (-1.2) |
26.3 |
- |
|
R34-YOLOv3 |
- |
COCO |
8 |
36.2 |
34.3 |
31.4 |
162 |
- |
|
R34-YOLOv3 |
quant_post |
COCO |
8 |
35.7 (-0.5) |
- |
- |
42.7 |
- |
|
R34-YOLOv3 |
quant_aware |
COCO |
8 |
35.2 (-1.0) |
33.3 (-1.0) |
30.3 (-1.1) |
44 |
- |
|
R50-dcn-YOLOv3 obj365_pretrain |
- |
COCO |
8 |
41.4 |
- |
- |
177 |
18.56 |
|
R50-dcn-YOLOv3 obj365_pretrain |
quant_aware |
COCO |
8 |
40.6 (-0.8) |
37.5 |
34.1 |
66 |
14.64 |
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） |
|
BlazeFace |
- |
8 |
640 |
91.5/89.2/79.7 |
815 |
|
BlazeFace |
quant_post |
8 |
640 |
87.8/85.1/74.9 (-3.7/-4.1/-4.8) |
228 |
|
BlazeFace |
quant_aware |
8 |
640 |
90.5/87.9/77.6 (-1.0/-1.3/-2.1) |
228 |
|
BlazeFace-Lite |
- |
8 |
640 |
90.9/88.5/78.1 |
711 |
|
BlazeFace-Lite |
quant_post |
8 |
640 |
89.4/86.7/75.7 (-1.5/-1.8/-2.4) |
211 |
|
BlazeFace-Lite |
quant_aware |
8 |
640 |
89.7/87.3/77.0 (-1.2/-1.2/-1.1) |
211 |
|
BlazeFace-NAS |
- |
8 |
640 |
83.7/80.7/65.8 |
244 |
|
BlazeFace-NAS |
quant_post |
8 |
640 |
81.6/78.3/63.6 (-2.1/-2.4/-2.2) |
71 |
|
BlazeFace-NAS |
quant_aware |
8 |
640 |
83.1/79.7/64.2 (-0.6/-1.0/-1.6) |
71 |
2.2 剪裁
数据集：Pasacl VOC & COCO 2017
PaddleLite推理耗时说明：
环境：Qualcomm SnapDragon 845 + armv8
速度指标：Thread1/Thread2/Thread4耗时
PaddleLite版本： v2.3
|
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) |
PaddleLite推理耗时(ms)(608*608) |
TensorRT推理速度(FPS)(608*608) |
|
MobileNet-V1-YOLOv3 |
Baseline |
Pascal VOC |
8 |
76.2 |
76.7 |
75.3 |
94 |
40.49 |
1238\796.943\520.101 |
60.04 |
|
MobileNet-V1-YOLOv3 |
sensitive -52.88% |
Pascal VOC |
8 |
77.6 (+1.4) |
77.7 (1.0) |
75.5 (+0.2) |
31 |
19.08 |
602.497\353.759\222.427 |
99.36 |
|
MobileNet-V1-YOLOv3 |
- |
COCO |
8 |
29.3 |
29.3 |
27.0 |
95 |
41.35 |
- |
- |
|
MobileNet-V1-YOLOv3 |
sensitive -51.77% |
COCO |
8 |
26.0 (-3.3) |
25.1 (-4.2) |
22.6 (-4.4) |
32 |
19.94 |
- |
73.93 |
|
R50-dcn-YOLOv3 |
- |
COCO |
8 |
39.1 |
- |
- |
177 |
89.60 |
- |
27.68 |
|
R50-dcn-YOLOv3 |
sensitive -9.37% |
COCO |
8 |
39.3 (+0.2) |
- |
- |
150 |
81.20 |
- |
30.08 |
|
R50-dcn-YOLOv3 |
sensitive -24.68% |
COCO |
8 |
37.3 (-1.8) |
- |
- |
113 |
67.48 |
- |
34.32 |
|
R50-dcn-YOLOv3 obj365_pretrain |
- |
COCO |
8 |
41.4 |
- |
- |
177 |
89.60 |
- |
- |
|
R50-dcn-YOLOv3 obj365_pretrain |
sensitive -9.37% |
COCO |
8 |
40.5 (-0.9) |
- |
- |
150 |
81.20 |
- |
- |
|
R50-dcn-YOLOv3 obj365_pretrain |
sensitive -24.68% |
COCO |
8 |
37.8 (-3.3) |
- |
- |
113 |
67.48 |
- |
- |
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） |
|
MobileNet-V1-YOLOv3 |
- |
Pascal VOC |
8 |
76.2 |
76.7 |
75.3 |
94 |
|
ResNet34-YOLOv3 |
- |
Pascal VOC |
8 |
82.6 |
81.9 |
80.1 |
162 |
|
MobileNet-V1-YOLOv3 |
ResNet34-YOLOv3 distill |
Pascal VOC |
8 |
79.0 (+2.8) |
78.2 (+1.5) |
75.5 (+0.2) |
94 |
|
MobileNet-V1-YOLOv3 |
- |
COCO |
8 |
29.3 |
29.3 |
27.0 |
95 |
|
ResNet34-YOLOv3 |
- |
COCO |
8 |
36.2 |
34.3 |
31.4 |
163 |
|
MobileNet-V1-YOLOv3 |
ResNet34-YOLOv3 distill |
COCO |
8 |
31.4 (+2.1) |
30.0 (+0.7) |
27.1 (+0.1) |
95 |
2.4 搜索
数据集：WIDER-FACE
|
模型 |
压缩方法 |
Image/GPU |
输入尺寸 |
Easy/Medium/Hard |
模型体积（KB） |
硬件延时（ms） |
|
BlazeFace |
- |
8 |
640 |
91.5/89.2/79.7 |
815 |
71.862 |
|
BlazeFace-NAS |
- |
8 |
640 |
83.7/80.7/65.8 |
244 |
21.117 |
|
BlazeFace-NASV2 |
SANAS |
8 |
640 |
87.0/83.7/68.5 |
389 |
22.558 |
Note: 硬件延时时间是利用提供的硬件延时表得到的，硬件延时表是在855芯片上基于PaddleLite测试的结果。BlazeFace-NASV2的详细配置在这里.
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） |
|
DeepLabv3+/MobileNetv1 |
- |
63.26 |
6.6 |
|
DeepLabv3+/MobileNetv1 |
quant_post |
58.63 (-4.63) |
1.8 |
|
DeepLabv3+/MobileNetv1 |
quant_aware |
62.03 (-1.23) |
1.8 |
|
DeepLabv3+/MobileNetv2 |
- |
69.81 |
7.4 |
|
DeepLabv3+/MobileNetv2 |
quant_post |
67.59 (-2.22) |
2.1 |
|
DeepLabv3+/MobileNetv2 |
quant_aware |
68.33 (-1.48) |
2.1 |
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
PaddleLite推理耗时说明：
环境：Qualcomm SnapDragon 845 + armv8
速度指标：Thread1/Thread2/Thread4耗时
PaddleLite版本： v2.3
|
模型 |
压缩方法 |
mIoU |
模型体积（MB） |
GFLOPs |
PaddleLite推理耗时 |
TensorRT推理速度(FPS) |
|
fast-scnn |
baseline |
69.64 |
11 |
14.41 |
1226.36\682.96\415.664 |
39.53 |
|
fast-scnn |
uniform -17.07% |
69.58 (-0.06) |
8.5 |
11.95 |
1140.37\656.612\415.888 |
42.01 |
|
fast-scnn |
sensitive -47.60% |
66.68 (-2.96) |
5.7 |
7.55 |
866.693\494.467\291.748 |
51.48 |
标签：YOLOv3,模型库,MobileNetV1,aware,高通,MobileNetV2,quant,deeplearning 来源： https://www.cnblogs.com/wujianming-110117/p/14424097.html