Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
PaddleX
提交
99f52dcc
P
PaddleX
项目概览
PaddlePaddle
/
PaddleX
通知
138
Star
4
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
43
列表
看板
标记
里程碑
合并请求
5
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
PaddleX
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
43
Issue
43
列表
看板
标记
里程碑
合并请求
5
合并请求
5
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
99f52dcc
编写于
6月 09, 2020
作者:
J
Jason
提交者:
GitHub
6月 09, 2020
浏览文件
操作
浏览文件
下载
差异文件
Merge pull request #144 from SunAhong1993/syf0609
modify the model zoo benchmark
上级
fb58497f
8e459472
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
43 addition
and
35 deletion
+43
-35
docs/appendix/model_zoo.md
docs/appendix/model_zoo.md
+43
-35
未找到文件。
docs/appendix/model_zoo.md
浏览文件 @
99f52dcc
...
...
@@ -6,48 +6,56 @@
| 模型 | 模型大小 | 预测速度(毫秒) | Top1准确率(%) | Top5准确率(%) |
| :----| :------- | :----------- | :--------- | :--------- |
|
ResNet18| 46.9MB | -
| 71.0 | 89.9 |
|
ResNet34| 87.5MB | -
| 74.6 | 92.1 |
|
ResNet50| 102.7MB | -
| 76.5 | 93.0 |
|
ResNet101 |179.1MB | -
| 77.6 | 93.6 |
|
ResNet50_vd |102.8MB |-
| 79.1 | 94.4 |
|
ResNet101_vd| 179.2MB | -
| 80.2 | 95.0 |
|
ResNet50_vd_ssld |102.8MB | -
| 82.4 | 96.1 |
|
ResNet101_vd_ssld| 179.2MB | -
| 83.7 | 96.7 |
|
DarkNet53|166.9MB | -
| 78.0 | 94.1 |
|
MobileNetV1 | 16.0MB | -
| 71.0 | 89.7 |
|
MobileNetV2 | 14.0MB | -
| 72.2 | 90.7 |
|
MobileNetV3_large| 21.0MB | -
| 75.3 | 93.2 |
|
MobileNetV3_small | 12.0MB | -
| 68.2 | 88.1 |
|
MobileNetV3_large_ssld| 21.0MB | -
| 79.0 | 94.5 |
|
MobileNetV3_small_ssld | 12.0MB | -
| 71.3 | 90.1 |
|
Xception41 |92.4MB | -
| 79.6 | 94.4 |
|
Xception65 | 144.6MB | -
| 80.3 | 94.5 |
|
DenseNet121 | 32.8MB | -
| 75.7 | 92.6 |
|
DenseNet161|116.3MB | -
| 78.6 | 94.1 |
|
DenseNet201| 84.6MB | -
| 77.6 | 93.7 |
|
ShuffleNetV2 | 9.0MB | -
| 68.8 | 88.5 |
|
HRNet_W18 | 21.29MB | -
| 76.9 | 93.4 |
|
[
ResNet18
](
https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_pretrained.tar
)
| 46.2MB | 3.72882
| 71.0 | 89.9 |
|
[
ResNet34
](
https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar
)
| 87.9MB | 5.50876
| 74.6 | 92.1 |
|
[
ResNet50
](
https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar
)
| 103.4MB | 7.76659
| 76.5 | 93.0 |
|
[
ResNet101
](
https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar
)
|180.4MB | 13.80876
| 77.6 | 93.6 |
|
[
ResNet50_vd
](
https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
)
|103.5MB | 8.20476
| 79.1 | 94.4 |
|
[
ResNet101_vd
](
https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar
)
| 180.5MB | 14.24643
| 80.2 | 95.0 |
|
[
ResNet50_vd_ssld
](
https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
)
|103.5MB | 7.79264
| 82.4 | 96.1 |
|
[
ResNet101_vd_ssld
](
https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_ssld_pretrained.tar
)
| 180.5MB | 13.34580
| 83.7 | 96.7 |
|
[
DarkNet53
](
https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_ImageNet1k_pretrained.tar
)
|167.4MB | 8.82047
| 78.0 | 94.1 |
|
[
MobileNetV1
](
https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar
)
| 17.4MB | 3.42838
| 71.0 | 89.7 |
|
[
MobileNetV2
](
https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar
)
| 15.0MB | 5.92667
| 72.2 | 90.7 |
|
[
MobileNetV3_large
](
https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_pretrained.tar
)
| 22.8MB | 8.31428
| 75.3 | 93.2 |
|
[
MobileNetV3_small
](
https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x1_0_pretrained.tar
)
| 12.5MB | 7.30689
| 68.2 | 88.1 |
|
[
MobileNetV3_large_ssld
](
https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_ssld_pretrained.tar
)
| 22.8MB | 8.06651
| 79.0 | 94.5 |
|
[
MobileNetV3_small_ssld
](
https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x1_0_ssld_pretrained.tar
)
| 12.5MB | 7.08837
| 71.3 | 90.1 |
|
[
Xception41
](
https://paddle-imagenet-models-name.bj.bcebos.com/Xception41_deeplab_pretrained.tar
)
| 109.2MB | 8.15611
| 79.6 | 94.4 |
|
[
Xception65
](
https://paddle-imagenet-models-name.bj.bcebos.com/Xception65_deeplab_pretrained.tar
)
| 161.6MB | 13.87017
| 80.3 | 94.5 |
|
[
DenseNet121
](
https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet121_pretrained.tar
)
| 33.1MB | 17.09874
| 75.7 | 92.6 |
|
[
DenseNet161
](
https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet161_pretrained.tar
)
| 118.0MB | 22.79690
| 78.6 | 94.1 |
|
[
DenseNet201
](
https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet201_pretrained.tar
)
| 84.1MB | 25.26089
| 77.6 | 93.7 |
|
[
ShuffleNetV2
](
https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_pretrained.tar
)
| 10.2MB | 15.40138
| 68.8 | 88.5 |
|
[
HRNet_W18
](
https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W18_C_pretrained.tar
)
| 21.29MB |45.25514
| 76.9 | 93.4 |
## 目标检测模型
> 表中模型相关指标均为在MSCOCO数据集上使用PaddlePaddle Python预测接口测试得到(测试GPU型号为Nvidia Tesla V100测试得到,表中符号`-`表示相关指标暂未测试。
> 表中模型相关指标均为在MSCOCO数据集上使用PaddlePaddle Python预测接口测试得到(测试GPU型号为Nvidia Tesla V100测试得到
)
,表中符号`-`表示相关指标暂未测试。
| 模型 | 模型大小 | 预测时间(毫秒) | BoxAP(%) |
|:-------|:-----------|:-------------|:----------|
|
FasterRCNN-ResNet50|135.6MB| 78.450
| 35.2 |
|
FasterRCNN-ResNet50_vd| 135.7MB | 79.523
| 36.4 |
|
FasterRCNN-ResNet101| 211.7MB | 107.342
| 38.3 |
|
FasterRCNN-ResNet50-FPN| 167.2MB | 44.897
| 37.2 |
|
FasterRCNN-ResNet50_vd-FPN|168.7MB | 45.773
| 38.9 |
|
FasterRCNN-ResNet101-FPN| 251.7MB | 55.782
| 38.7 |
|
FasterRCNN-ResNet101_vd-FPN |252MB | 58.785
| 40.5 |
|
FasterRCNN-HRNet_W18-FPN |115.5MB | 57.11
| 36 |
|
YOLOv3-DarkNet53|252.4MB | 21.944
| 38.9 |
|
YOLOv3-MobileNetv1 |101.2MB | 12.771
| 29.3 |
|
YOLOv3-MobileNetv3|94.6MB | -
| 31.6 |
|
YOLOv3-ResNet34|169.7MB | 15.784
| 36.2 |
|
[
FasterRCNN-ResNet50
](
https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_1x.tar
)
|136.0MB| 197.715
| 35.2 |
|
[
FasterRCNN-ResNet50_vd
](
https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_1x.tar
)
| 136.1MB | 475.700
| 36.4 |
|
[
FasterRCNN-ResNet101
](
https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_1x.tar
)
| 212.5MB | 582.911
| 38.3 |
|
[
FasterRCNN-ResNet50-FPN
](
https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_fpn_1x.tar
)
| 167.7MB | 83.189
| 37.2 |
|
[
FasterRCNN-ResNet50_vd-FPN
](
https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_2x.tar
)
|167.8MB | 128.277
| 38.9 |
|
[
FasterRCNN-ResNet101-FPN
](
https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_1x.tar
)
| 244.2MB | 156.097
| 38.7 |
|
[
FasterRCNN-ResNet101_vd-FPN
](
https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_vd_fpn_2x.tar
)
|244.3MB | 119.788
| 40.5 |
|
[
FasterRCNN-HRNet_W18-FPN
](
https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_hrnetv2p_w18_1x.tar
)
|115.5MB | 81.592
| 36 |
|
[
YOLOv3-DarkNet53
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar
)
|249.2MB | 42.672
| 38.9 |
|
[
YOLOv3-MobileNetV1
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar
)
|99.2MB | 15.442
| 29.3 |
|
[
YOLOv3-MobileNetV3_large
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v3.pdparams
)
|100.7MB | 143.322
| 31.6 |
|
[
YOLOv3-ResNet34
](
https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar
)
|170.3MB | 23.185
| 36.2 |
## 实例分割模型
> 表中模型相关指标均为在MSCOCO数据集上测试得到。
| 模型 | 模型大小 | 预测时间(毫秒) | mIoU(%) |
|:-------|:-----------|:-------------|:----------|
|DeepLabv3+-MobileNetV2_x1.0|-| - | - |
|DeepLabv3+-Xception41|-| - | - |
|DeepLabv3+-Xception65|-| - | - |
|UNet|-| - | - |
|HRNet_w18|-| - | - |
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录