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a4f0a1dd
编写于
2月 05, 2020
作者:
L
Liufang Sang
提交者:
GitHub
2月 05, 2020
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add quantization result in model zoo (#77)
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docs/docs/model_zoo.md
docs/docs/model_zoo.md
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docs/docs/model_zoo.md
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a4f0a1dd
...
@@ -4,17 +4,309 @@
...
@@ -4,17 +4,309 @@
### 1.1 量化
### 1.1 量化
| 模型 | 压缩方法 | Top-1/Top-5 Acc | 模型体积(MB) | 下载 |
| 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%| xx |
[
下载链接
](
)
|
|MobileNetV1|-|70.99%/89.68%| 17 | -|
[
下载链接
](
http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar
)
|
|MobileNetV1|quant_post|xx%/xx%| xx |
[
下载链接
](
)
|
|MobileNetV1|quant_post|70.18%/89.25% (-0.81%/-0.43%)| 4.4 | - |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_quant_post.tar
)
|
|MobileNetV1|quant_aware|xx%/xx%| xx |
[
下载链接
](
)
|
|MobileNetV1|quant_aware|70.60%/89.57% (-0.39%/-0.11%)| 4.4 | -|
[
下载链接
](
https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV1_quant_aware.tar
)
|
| MobileNetV2 | - |72.15%/90.65%| xx |
[
下载链接
](
)
|
| MobileNetV2 | - |72.15%/90.65%| 15 | - |
[
下载链接
](
https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar
)
|
| MobileNetV2 | quant_post |xx%/xx%| xx |
[
下载链接
](
)
|
| MobileNetV2 | quant_post | 71.15%/90.11% (-1%/-0.54%)| 4.0 | - |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_quant_post.tar
)
|
| MobileNetV2 | quant_aware |xx%/xx%| xx |
[
下载链接
](
)
|
| MobileNetV2 | quant_aware |72.05%/90.63% (-0.1%/-0.02%)| 4.0 | - |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/PaddleSlim/MobileNetV2_quant_aware.tar
)
|
|ResNet50|-|76.50%/93.00%| xx |
[
下载链接
](
)
|
|ResNet50|-|76.50%/93.00%| 99 | 2.71 |
[
下载链接
](
http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar
)
|
|ResNet50|quant_post|xx%/xx%| xx |
[
下载链接
](
)
|
|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|xx%/xx%| xx |
[
下载链接
](
)
|
|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>
...
@@ -60,14 +352,16 @@
...
@@ -60,14 +352,16 @@
数据集: COCO 2017
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) | 下载 |
| 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 | xx |
[
下载链接
](
)
|
| MobileNet-V1-YOLOv3 | - | COCO | 8 | 29.3 | 29.3 | 27.1 | 95 | - |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar
)
|
| MobileNet-V1-YOLOv3 | quant_post | COCO | 8 | xx | xx | xx | xx |
[
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
](
https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_post.tar
)
|
| MobileNet-V1-YOLOv3 | quant_aware | COCO | 8 | xx | xx | xx | xx |
[
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
](
https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenet_coco_quant_aware.tar
)
|
| R50-dcn-YOLOv3 obj365_pretrain | - | COCO | 8 | 41.4 | xx | xx | xx |
[
下载链接
](
)
|
| R34-YOLOv3 | - | COCO | 8 | 36.2 | 34.3 | 31.4 | 162 | - |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar
)
|
| R50-dcn-YOLOv3 obj365_pretrain | quant_post | COCO | 8 | xx | xx | xx | xx |
[
下载链接
](
)
|
| R34-YOLOv3 | quant_post | COCO | 8 | 35.7 (-0.5) | - | - | 42.7 | - |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_post.tar
)
|
| R50-dcn-YOLOv3 obj365_pretrain | quant_aware | COCO | 8 | xx | xx | xx | xx |
[
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
](
https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_aware.tar
)
|
| R50-dcn-YOLOv3 obj365_pretrain | - | COCO | 8 | 41.4 | - | - | 177 | 18.56 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_pretrained_coco.tar
)
|
| R50-dcn-YOLOv3 obj365_pretrain | quant_aware | COCO | 8 | 40.6 (-0.8) | 37.5 | 34.1 | 66 | 14.64 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r50vd_dcn_obj365_pretrained_coco_quant_aware.tar
)
|
...
@@ -75,17 +369,17 @@
...
@@ -75,17 +369,17 @@
| 模型 | 压缩方法 | Image/GPU | 输入尺寸 |
Easy/Medium/Hard | 模型体积(MB) | 下载
|
| 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 |
0.915/0.892/0.797 | xx |
[
下载链接
](
)
|
| BlazeFace | - | 8 | 640 |
91.5/89.2/79.7 | 815 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/blazeface_original.tar
)
|
| BlazeFace | quant_post | 8 | 640 |
xx/xx/xx | xx |
[
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
](
https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_origin_quant_post.tar
)
|
| BlazeFace | quant_aware | 8 | 640 |
xx/xx/xx | xx |
[
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
](
https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_origin_quant_aware.tar
)
|
| BlazeFace-Lite | - | 8 | 640 |
0.909/0.885/0.781 | xx |
[
下载链接
](
)
|
| BlazeFace-Lite | - | 8 | 640 |
90.9/88.5/78.1 | 711 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/blazeface_lite.tar
)
|
| BlazeFace-Lite | quant_post | 8 | 640 |
xx/xx/xx | xx |
[
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
](
(https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_lite_quant_post.tar
)
) |
| BlazeFace-Lite | quant_aware | 8 | 640 |
xx/xx/xx | xx |
[
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
](
https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_lite_quant_aware.tar
)
|
| BlazeFace-NAS | - | 8 | 640 |
0.837/0.807/0.658 | xx |
[
下载链接
](
)
|
| BlazeFace-NAS | - | 8 | 640 |
83.7/80.7/65.8 | 244 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar
)
|
| BlazeFace-NAS | quant_post | 8 | 640 |
xx/xx/xx | xx |
[
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
](
https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_nas_quant_post.tar
)
|
| BlazeFace-NAS | quant_aware | 8 | 640 |
xx/xx/xx | xx |
[
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
](
https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_nas_quant_aware.tar
)
|
### 2.2 剪裁
### 2.2 剪裁
...
@@ -125,14 +419,225 @@
...
@@ -125,14 +419,225 @@
### 3.1 量化
### 3.1 量化
| 模型 | 压缩方法 | mIoU | 模型体积(MB) | 下载 |
| 模型 | 压缩方法 | mIoU | 模型体积(MB) | 下载 |
| :--------------------: | :---------: | :---: | :------------: | :----------: |
| :--------------------: | :---------: | :-----------: | :------------: | :----------------------------------------------------------: |
| DeepLabv3+/MobileNetv1 | - | 63.26 | xx |
[
下载链接
](
)
|
| DeepLabv3+/MobileNetv1 | - | 63.26 | 6.6 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/PaddleSlim/deeplabv3_mobilenetv1.tar
)
|
| DeepLabv3+/MobileNetv1 | quant_post | xx | xx |
[
下载链接
](
)
|
| DeepLabv3+/MobileNetv1 | quant_post | 58.63 (-4.63) | 1.8 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/PaddleSlim/deeplabv3_mobilenetv1_2049x1025_quant_post.tar
)
|
| DeepLabv3+/MobileNetv1 | quant_aware | xx | xx |
[
下载链接
](
)
|
| DeepLabv3+/MobileNetv1 | quant_aware | 62.03 (-1.23) | 1.8 |
[
下载链接
](
https://paddlemodels.bj.bcebos.com/PaddleSlim/deeplabv3_mobilenetv1_2049x1025_quant_aware.tar
)
|
| DeepLabv3+/MobileNetv2 | - | 69.81 | xx |
[
下载链接
](
)
|
| DeepLabv3+/MobileNetv2 | - | 69.81 | 7.4 |
[
下载链接
](
https://paddleseg.bj.bcebos.com/models/mobilenet_cityscapes.tgz
)
|
| DeepLabv3+/MobileNetv2 | quant_post | xx | xx |
[
下载链接
](
)
|
| 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 | xx | xx |
[
下载链接
](
)
|
| 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>
### 3.2 剪裁
### 3.2 剪裁
...
...
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