update ghostnet docs

2020-07-01 07:46:58 +00:00 · 2020-07-01 07:46:58 +00:00 · feac3ca44f
parent d9a3588615
commit feac3ca44f
5 changed files with 25 additions and 1 deletions
--- a/docs/en/models/Mobile_en.md
+++ b/docs/en/models/Mobile_en.md
@ -10,6 +10,7 @@ The ShuffleNet series network is the lightweight network structure proposed by M

 MobileNetV3 is a new and lightweight network based on NAS proposed by Google in 2019. In order to further improve the effect, the activation functions of relu and sigmoid were replaced with hard_swish and hard_sigmoid activation functions, and some improved strategies were introduced to reduce the amount of network computing.

+GhosttNet is a brand-new lightweight network structure proposed by Huawei in 2020. By introducing the ghost module, the problem of redundant calculation of features in traditional deep networks is greatly alleviated, which greatly reduces the amount of network parameters and calculations.

 ![](../../images/models/mobile_arm_top1.png)

@ -57,6 +58,9 @@ Currently there are 32 pretrained models of the mobile series open source by Pad
 | ShuffleNetV2_x1_5                    | 0.716   | 0.902   | 0.726             |                   | 0.580        | 3.470             |
 | ShuffleNetV2_x2_0                    | 0.732   | 0.912   | 0.749             |                   | 1.120        | 7.320             |
 | ShuffleNetV2_swish                   | 0.700   | 0.892   |                   |                   | 0.290        | 2.260             |
+| GhostNet_x0_5                        | 0.668   | 0.869   | 0.662             | 0.866             | 0.041        | 2.600             |
+| GhostNet_x1_0                        | 0.740   | 0.916   | 0.739             | 0.914             | 0.147        | 5.200             |
+| GhostNet_x1_3                        | 0.757   | 0.925   | 0.757             | 0.927             | 0.220        | 7.300             |


 ## Inference speed and storage size based on SD855
--- a/docs/en/models/models_intro_en.md
+++ b/docs/en/models/models_intro_en.md
@ -93,6 +93,10 @@ python tools/infer/predict.py \
    - [ShuffleNetV2_x1_5](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_x1_5_pretrained.tar)
    - [ShuffleNetV2_x2_0](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_x2_0_pretrained.tar)
    - [ShuffleNetV2_swish](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_swish_pretrained.tar)
+  - GhostNet series<sup>[[23](#ref23)]</sup>([paper link](https://arxiv.org/pdf/1911.11907.pdf))
+    - [GhostNet_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x0_5_pretrained.pdparams)
+    - [GhostNet_x1_0](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_0_pretrained.pdparams)
+    - [GhostNet_x1_3](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_3_pretrained.pdparams)


 - SEResNeXt and Res2Net series
@ -254,3 +258,5 @@ python tools/infer/predict.py \
 <a name="ref21">[21]</a> Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779-788.

 <a name="ref22">[22]</a> Ding X, Guo Y, Ding G, et al. Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1911-1920.
+
+<a name="ref23">[23]</a> Han K, Wang Y, Tian Q, et al. GhostNet: More features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 1580-1589.
--- a/docs/zh_CN/models/Mobile.md
+++ b/docs/zh_CN/models/Mobile.md
@ -9,6 +9,8 @@ ShuffleNet系列网络是旷视提出的轻量化网络结构，到目前为止

 MobileNetV3是Google于2019年提出的一种基于NAS的新的轻量级网络，为了进一步提升效果，将relu和sigmoid激活函数分别替换为hard_swish与hard_sigmoid激活函数，同时引入了一些专门减小网络计算量的改进策略。

+GhosttNet是华为于2020年提出的一种全新的轻量化网络结构，通过引入ghost module，大大减缓了传统深度网络中特征的冗余计算问题，使得网络的参数量和计算量大大降低。
+
 ![](../../images/models/mobile_arm_top1.png)

 ![](../../images/models/mobile_arm_storage.png)
@ -18,7 +20,7 @@ MobileNetV3是Google于2019年提出的一种基于NAS的新的轻量级网络
 ![](../../images/models/T4_benchmark/t4.fp32.bs4.mobile_trt.params.png)


-目前PaddleClas开源的的移动端系列的预训练模型一共有32个，其指标如图所示。从图片可以看出，越新的轻量级模型往往有更优的表现，MobileNetV3代表了目前最新的轻量级神经网络结构。在MobileNetV3中，作者为了获得更高的精度，在global-avg-pooling后使用了1x1的卷积。该操作大幅提升了参数量但对计算量影响不大，所以如果从存储角度评价模型的优异程度，MobileNetV3优势不是很大，但由于其更小的计算量，使得其有更快的推理速度。此外，我们模型库中的ssld蒸馏模型表现优异，从各个考量角度下，都刷新了当前轻量级模型的精度。由于MobileNetV3模型结构复杂，分支较多，对GPU并不友好，GPU预测速度不如MobileNetV1。
+目前PaddleClas开源的的移动端系列的预训练模型一共有35个，其指标如图所示。从图片可以看出，越新的轻量级模型往往有更优的表现，MobileNetV3代表了目前主流的轻量级神经网络结构。在MobileNetV3中，作者为了获得更高的精度，在global-avg-pooling后使用了1x1的卷积。该操作大幅提升了参数量但对计算量影响不大，所以如果从存储角度评价模型的优异程度，MobileNetV3优势不是很大，但由于其更小的计算量，使得其有更快的推理速度。此外，我们模型库中的ssld蒸馏模型表现优异，从各个考量角度下，都刷新了当前轻量级模型的精度。由于MobileNetV3模型结构复杂，分支较多，对GPU并不友好，GPU预测速度不如MobileNetV1。GhostNet于2020年提出，通过引入ghost的网络设计理念，大大降低了计算量和参数量，同时在精度上也超过前期最高的MobileNetV3网络结构。


 ## 精度、FLOPS和参数量
@ -57,6 +59,9 @@ MobileNetV3是Google于2019年提出的一种基于NAS的新的轻量级网络
 | ShuffleNetV2_x1_5                    | 0.716   | 0.902   | 0.726             |                   | 0.580        | 3.470             |
 | ShuffleNetV2_x2_0                    | 0.732   | 0.912   | 0.749             |                   | 1.120        | 7.320             |
 | ShuffleNetV2_swish                   | 0.700   | 0.892   |                   |                   | 0.290        | 2.260             |
+| GhostNet_x0_5                        | 0.668   | 0.869   | 0.662             | 0.866             | 0.041        | 2.600             |
+| GhostNet_x1_0                        | 0.740   | 0.916   | 0.739             | 0.914             | 0.147        | 5.200             |
+| GhostNet_x1_3                        | 0.757   | 0.925   | 0.757             | 0.927             | 0.220        | 7.300             |


 ## 基于SD855的预测速度和存储大小
--- a/docs/zh_CN/models/models_intro.md
+++ b/docs/zh_CN/models/models_intro.md
@ -93,6 +93,10 @@ python tools/infer/predict.py \
    - [ShuffleNetV2_x1_5](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_x1_5_pretrained.tar)
    - [ShuffleNetV2_x2_0](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_x2_0_pretrained.tar)
    - [ShuffleNetV2_swish](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_swish_pretrained.tar)
+  - GhostNet系列<sup>[[23](#ref23)]</sup>([论文地址](https://arxiv.org/pdf/1911.11907.pdf))
+    - [GhostNet_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x0_5_pretrained.pdparams)
+    - [GhostNet_x1_0](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_0_pretrained.pdparams)
+    - [GhostNet_x1_3](https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_3_pretrained.pdparams)


 - SEResNeXt与Res2Net系列
@ -254,3 +258,5 @@ python tools/infer/predict.py \
 <a name="ref21">[21]</a> Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779-788.

 <a name="ref22">[22]</a> Ding X, Guo Y, Ding G, et al. Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 1911-1920.
+
+<a name="ref23">[23]</a> Han K, Wang Y, Tian Q, et al. GhostNet: More features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 1580-1589.
--- a/ppcls/utils/pretrained.list
+++ b/ppcls/utils/pretrained.list
@ -119,3 +119,6 @@ VGG19
 DarkNet53_ImageNet1k
 ResNet50_ACNet_deploy
 CSPResNet50_leaky
+GhostNet_x0_5
+GhostNet_x1_0
+GhostNet_x1_3