Add se_hrnet_w64_c_ssld_pretrained (#452)

add se hrnet64_c

Add se_hrnet_w64_c_ssld_pretrained (#452)
add se hrnet64_c
a73573a3 · cuicheng01 · GitHub · 1ecf8334 · a73573a3 · a73573a3
10 changed file
--- a/README.md
+++ b/README.md
@@ -7,6 +7,7 @@
 PaddleClas is a toolset for image classification tasks prepared for the industry and academia. It helps users train better computer vision models and apply them in real scenarios.
 **Recent update**
+- 2020.12.06 Add `SE_HRNet_W64_C_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 84.75%.
 - 2020.11.23 Add `GhostNet_x1_3_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 79.38%.
 - 2020.11.09 Add `InceptionV3` architecture and pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 79.1%.
 - 2020.09.17 Add `Res2Net50_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.1%. Add `Res2Net101_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.9%.
@@ -256,6 +257,7 @@ Accuracy and inference time metrics of HRNet series models are shown as follows.
 | HRNet_W48_C | 0.7895    | 0.9442    | 13.70761         | 34.43572         | 34.58    | 77.47     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams) |
 | HRNet_W48_C_ssld | 0.8363    | 0.9682    | 13.70761         | 34.43572         | 34.58    | 77.47     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams) |
 | HRNet_W64_C | 0.7930    | 0.9461    | 17.57527         | 47.9533          | 57.83    | 128.06    | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams) |
+| SE_HRNet_W64_C_ssld | 0.8475    |  0.9726    |    31.69770      |      94.99546     | 57.83    | 128.97    | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_HRNet_W64_C_ssld_pretrained.pdparams) |
 <a name="Inception_series"></a>

--- a/README_cn.md
+++ b/README_cn.md
@@ -8,6 +8,7 @@
 **近期更新**
+- 2020.12.06 添加`SE_HRNet_W64_C_ssld`模型，在ImageNet-1k上Top-1 Acc可达84.75%。
 - 2020.11.23 添加`GhostNet_x1_3_ssld `模型，在ImageNet-1k上Top-1 Acc可达79.38%。
 - 2020.11.18 添加图像分类[常见问题2020第一季第三期](./docs/zh_CN/faq_series/faq_2020_s1.md) 5个新问题，并且计划以后每周会更新，欢迎大家持续关注。
 - 2020.11.09 添加`InceptionV3 `结构和模型，在ImageNet-1k上Top-1 Acc可达79.14%。
@@ -260,6 +261,7 @@ HRNet系列模型的精度、速度指标如下表所示，更多关于该系列
 | HRNet_W48_C | 0.7895    | 0.9442    | 13.70761         | 34.43572         | 34.58    | 77.47     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams) |
 | HRNet_W48_C_ssld | 0.8363    | 0.9682    | 13.70761         | 34.43572         | 34.58    | 77.47     | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams) |
 | HRNet_W64_C | 0.7930    | 0.9461    | 17.57527         | 47.9533          | 57.83    | 128.06    | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams) |
+| SE_HRNet_W64_C_ssld | 0.8475    |  0.9726    |    31.69770      |     94.99546      | 57.83    | 128.97    | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_HRNet_W64_C_ssld_pretrained.pdparams) |
 <a name="Inception系列"></a>

--- a/docs/en/models/HRNet_en.md
+++ b/docs/en/models/HRNet_en.md
@@ -30,6 +30,7 @@ At present, there are 7 pretrained models of such models open-sourced by PaddleC
 | HRNet_W48_C | 0.790  | 0.944  | 0.793             | 0.945             | 34.580       | 77.470            |
 | HRNet_W48_C_ssld | 0.836  | 0.968  | 0.793             | 0.945             | 34.580       | 77.470            |
 | HRNet_W64_C | 0.793  | 0.946  | 0.795             | 0.946             | 57.830       | 128.060           |
+| SE_HRNet_W64_C_ssld | 0.847  | 0.973  |                |                   | 57.830       | 128.970           |
 ## Inference speed based on V100 GPU
@@ -62,3 +63,5 @@ At present, there are 7 pretrained models of such models open-sourced by PaddleC
 | HRNet_W48_C | 224       | 256               | 12.65015                     | 23.12886                     | 33.37859                     | 13.70761                     | 34.43572                     | 63.01219                     |
 | HRNet_W48_C_ssld | 224       | 256               | 12.65015                     | 23.12886                     | 33.37859                     | 13.70761                     | 34.43572                     | 63.01219                     |
 | HRNet_W64_C | 224       | 256               | 15.10428                     | 27.68901                     | 40.4198                      | 17.57527                     | 47.9533                      | 97.11228                     |
+| SE_HRNet_W64_C_ssld | 224       | 256               |           32.33651           |          69.31189            |           116.07245            |                   31.69770   |           94.99546            |             174.45766        |
--- a/docs/en/models/Mobile_en.md
+++ b/docs/en/models/Mobile_en.md
@@ -144,3 +144,6 @@ Currently there are 32 pretrained models of the mobile series open source by Pad
 | ShuffleNetV2_x1_5           | 1.51054               | 2.4565                | 3.41738               | 1.45389               | 2.5203                | 3.99872               |
 | ShuffleNetV2_x2_0           | 1.95616               | 2.44751               | 4.19173               | 2.15654               | 3.18247               | 5.46893               |
 | ShuffleNetV2_swish          | 2.50213               | 2.92881               | 3.474                 | 2.5129                | 2.97422               | 3.69357               |
+| GhostNet_x0_5          | 2.64492              | 3.48473               | 4.48844                 | 2.36115                | 3.52802               | 3.89444               |
+| GhostNet_x1_0          | 2.63120               | 3.92065               | 4.48296                 | 2.57042                | 3.56296               | 4.85524               |
+| GhostNet_x1_3          | 2.89715               | 3.80329               | 4.81661                 | 2.81810                | 3.72071              | 5.92269               |
--- a/docs/en/models/models_intro_en.md
+++ b/docs/en/models/models_intro_en.md
@@ -163,6 +163,7 @@ python tools/infer/predict.py \
    - [HRNet_W48_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams)
    - [HRNet_W48_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_ssld_pretrained.pdparams)
    - [HRNet_W64_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams)
+    - [SE_HRNet_W64_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_HRNet_W64_C_ssld_pretrained.pdparams)
 - DPN and DenseNet series

--- a/docs/en/update_history_en.md
+++ b/docs/en/update_history_en.md
 # Release Notes
+- 2020.12.06
+    * Add `SE_HRNet_W64_C_ssld` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 84.75%.
 - 2020.11.23
    * Add `GhostNet_x1_3_ssld` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 79.38%.

--- a/docs/zh_CN/models/HRNet.md
+++ b/docs/zh_CN/models/HRNet.md
@@ -29,6 +29,7 @@ HRNet是2019年由微软亚洲研究院提出的一种全新的神经网络，
 | HRNet_W48_C | 0.790  | 0.944  | 0.793             | 0.945             | 34.580       | 77.470            |
 | HRNet_W48_C_ssld | 0.836  | 0.968  | 0.793             | 0.945             | 34.580       | 77.470            |
 | HRNet_W64_C | 0.793  | 0.946  | 0.795             | 0.946             | 57.830       | 128.060           |
+| SE_HRNet_W64_C_ssld | 0.847  | 0.973  |                |                   | 57.830       | 128.970           |
 ## 基于V100 GPU的预测速度
@@ -47,7 +48,6 @@ HRNet是2019年由微软亚洲研究院提出的一种全新的神经网络，
 ## 基于T4 GPU的预测速度
 | Models      | Crop Size | Resize Short Size | FP16<br>Batch Size=1<br>(ms) | FP16<br>Batch Size=4<br>(ms) | FP16<br>Batch Size=8<br>(ms) | FP32<br>Batch Size=1<br>(ms) | FP32<br>Batch Size=4<br>(ms) | FP32<br>Batch Size=8<br>(ms) |
@@ -61,3 +61,4 @@ HRNet是2019年由微软亚洲研究院提出的一种全新的神经网络，
 | HRNet_W48_C | 224       | 256               | 12.65015                     | 23.12886                     | 33.37859                     | 13.70761                     | 34.43572                     | 63.01219                     |
 | HRNet_W48_C_ssld | 224       | 256               | 12.65015                     | 23.12886                     | 33.37859                     | 13.70761                     | 34.43572                     | 63.01219                     |
 | HRNet_W64_C | 224       | 256               | 15.10428                     | 27.68901                     | 40.4198                      | 17.57527                     | 47.9533                      | 97.11228                     |
+| SE_HRNet_W64_C_ssld | 224       | 256               |           32.33651           |          69.31189            |           116.07245            |                   31.69770   |           94.99546            |             174.45766        |
--- a/docs/zh_CN/models/Mobile.md
+++ b/docs/zh_CN/models/Mobile.md
@@ -145,3 +145,6 @@ GhostNet是华为于2020年提出的一种全新的轻量化网络结构，通
 | ShuffleNetV2_x1_5           | 1.51054               | 2.4565                | 3.41738               | 1.45389               | 2.5203                | 3.99872               |
 | ShuffleNetV2_x2_0           | 1.95616               | 2.44751               | 4.19173               | 2.15654               | 3.18247               | 5.46893               |
 | ShuffleNetV2_swish          | 2.50213               | 2.92881               | 3.474                 | 2.5129                | 2.97422               | 3.69357               |
+| GhostNet_x0_5          | 2.64492              | 3.48473               | 4.48844                 | 2.36115                | 3.52802               | 3.89444               |
+| GhostNet_x1_0          | 2.63120               | 3.92065               | 4.48296                 | 2.57042                | 3.56296               | 4.85524               |
+| GhostNet_x1_3          | 2.89715               | 3.80329               | 4.81661                 | 2.81810                | 3.72071              | 5.92269               |
--- a/docs/zh_CN/models/models_intro.md
+++ b/docs/zh_CN/models/models_intro.md
@@ -163,7 +163,7 @@ python tools/infer/predict.py \
    - [HRNet_W48_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_pretrained.pdparams)
    - [HRNet_W48_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W48_C_ssld_pretrained.pdparams)
    - [HRNet_W64_C](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HRNet_W64_C_pretrained.pdparams)
+    - [SE_HRNet_W64_C_ssld](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_HRNet_W64_C_ssld_pretrained.pdparams)
 - DPN与DenseNet系列
  - DPN系列<sup>[[14](#ref14)]</sup>([论文地址](https://arxiv.org/abs/1707.01629))

--- a/docs/zh_CN/update_history.md
+++ b/docs/zh_CN/update_history.md
 # 更新日志
+- 2020.12.06
+    * 添加SE_HRNet_W64_C_ssld模型，在ImageNet上Top-1 Acc可达0.8475。
 - 2020.11.23
    * 添加GhostNet_x1_3_ssld模型，在ImageNet上Top-1 Acc可达0.7938。