Merge pull request #286 from cuicheng01/master

Polish README

README.md

@@ -8,9 +8,9 @@ PaddleClas is a toolset for image classification tasks prepared for the industry
 
 
 **Recent update**
-- 2020.09.17 Add `HRNet_W48_C_ssld` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 83.62%. Add `ResNet34_vd_ssld` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 79.72%.
-- 2020.09.07 Add `HRNet_W18_C_ssld` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 81.16%.
-- 2020.07.14 Add `Res2Net200_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 85.13%. Add `Fix_ResNet50_vd_ssld_v2` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 84.00%.
+- 2020.09.17 Add `HRNet_W48_C_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.62%. Add `ResNet34_vd_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 79.72%.
+- 2020.09.07 Add `HRNet_W18_C_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 81.16%.
+- 2020.07.14 Add `Res2Net200_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 85.13%. Add `Fix_ResNet50_vd_ssld_v2` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 84.00%.
 - 2020.06.17 Add English documents.
 - 2020.06.12 Add support for training and evaluation on Windows or CPU.
 - 2020.05.17 Add support for mixed precision training.
@@ -19,9 +19,9 @@ PaddleClas is a toolset for image classification tasks prepared for the industry
 
 ## Features
 
-- Rich model zoo. Based on the ImageNet1k classification dataset, PaddleClas provides 24 series of classification network structures and training configurations, 122 models' pretrained weights and their evaluation metrics.
+- Rich model zoo. Based on the ImageNet-1k classification dataset, PaddleClas provides 24 series of classification network structures and training configurations, 122 models' pretrained weights and their evaluation metrics.
 
-- SSLD Knowledge Distillation. Based on this SSLD distillation strategy, the accuracy of the distilled model is generally increased by more than 3%.
+- SSLD Knowledge Distillation. Based on this SSLD distillation strategy, the top-1 acc of the distilled model is generally increased by more than 3%.
 
 - Data augmentation: PaddleClas provides detailed introduction of 8 data augmentation algorithms such as AutoAugment, Cutout, Cutmix, code reproduction and effect evaluation in a unified experimental environment.
 
@@ -77,7 +77,7 @@ PaddleClas is a toolset for image classification tasks prepared for the industry
 <a name="Model_zoo_overview"></a>
 ### Model zoo overview
 
-Based on the ImageNet1k classification dataset, the 24 classification network structures supported by PaddleClas and the corresponding 122 image classification pretrained models are shown below. Training trick, a brief introduction to each series of network structures, and performance evaluation will be shown in the corresponding chapters. The  evaluation environment is as follows.
+Based on the ImageNet-1k classification dataset, the 24 classification network structures supported by PaddleClas and the corresponding 122 image classification pretrained models are shown below. Training trick, a brief introduction to each series of network structures, and performance evaluation will be shown in the corresponding chapters. The  evaluation environment is as follows.
 
 * CPU evaluation environment is based on Snapdragon 855 (SD855).
 * The GPU evaluation speed is measured by running 500 times under the FP32+TensorRT configuration (excluding the warmup time of the first 10 times).
@@ -127,7 +127,7 @@ Accuracy and inference time metrics of ResNet and Vd series models are shown as
 
 Accuracy and inference time metrics of Mobile series models are shown as follows. More detailed information can be refered to [Mobile series tutorial](./docs/en/models/Mobile_en.md).
 
-| Model                              | Top-1 Acc | Top-5 Acc | SD855 time(ms)<br>bs=1 | Flops(G) | Params(M) | 模型大小(M) | Download Address                                                                                                      |
+| Model                              | Top-1 Acc | Top-5 Acc | SD855 time(ms)<br>bs=1 | Flops(G) | Params(M) | Model storage size(M) | Download Address                                                                                                      |
 |----------------------------------|-----------|-----------|------------------------|----------|-----------|---------|-----------------------------------------------------------------------------------------------------------|
 | MobileNetV1_<br>x0_25                | 0.5143    | 0.7546    | 3.21985                | 0.07     | 0.46      | 1.9     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_x0_25_pretrained.tar)                |
 | MobileNetV1_<br>x0_5                 | 0.6352    | 0.8473    | 9.579599               | 0.28     | 1.31      | 5.2     | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_x0_5_pretrained.tar)                 |

README_cn.md

@@ -7,12 +7,12 @@
 飞桨图像分类套件PaddleClas是飞桨为工业界和学术界所准备的一个图像分类任务的工具集，助力使用者训练出更好的视觉模型和应用落地。
 
 **近期更新**
-- 2020.09.17 添加HRNet_W48_C_ssld模型，在ImageNet上Top-1 Acc可达0.836；添加ResNet34_vd_ssld模型，在ImageNet上Top-1 Acc可达0.797。
-- 2020.09.07 添加HRNet_W18_C_ssld模型，在ImageNet上Top-1 Acc可达0.81162；添加MobileNetV3_small_x0_35_ssld模型，在ImageNet上Top-1 Acc可达0.5555。
-- 2020.07.14 添加Res2Net200_vd_26w_4s_ssld模型，在ImageNet上Top-1 Acc可达85.13%；添加Fix_ResNet50_vd_ssld_v2模型，在ImageNet上Top-1 Acc可达84.0%。
+- 2020.09.17 添加 `HRNet_W48_C_ssld `模型，在ImageNet-1k上Top-1 Acc可达83.62%；添加 `ResNet34_vd_ssld `模型，在ImageNet-1k上Top-1 Acc可达79.72%。
+- 2020.09.07 添加 `HRNet_W18_C_ssld `模型，在ImageNet-1k上Top-1 Acc可达81.16%；添加 `MobileNetV3_small_x0_35_ssld `模型，在ImageNet-1k上Top-1 Acc可达55.55%。
+- 2020.07.14 添加 `Res2Net200_vd_26w_4s_ssld `模型，在ImageNet-1k上Top-1 Acc可达85.13%；添加 `Fix_ResNet50_vd_ssld_v2 `模型，在ImageNet-1k上Top-1 Acc可达84.0%。
 - 2020.06.17 添加英文文档。
 - 2020.06.12 添加对windows和CPU环境的训练与评估支持。
-- 2020.05.17 添加混合精度训练，基于ResNet50模型，精度几乎无损的情况下，训练时间可以减少约40%。
+- 2020.05.17 添加混合精度训练，基于 `ResNet50 `模型，精度几乎无损的情况下，训练时间可以减少约40%。
 - [more](./docs/zh_CN/update_history.md)
 
 
@@ -24,7 +24,7 @@
 
 - 数据增广：支持AutoAugment、Cutout、Cutmix等8种数据增广算法详细介绍、代码复现和在统一实验环境下的效果评估。
 
-- 10万类图像分类预训练模型：百度自研并开源了基于10万类数据集训练的ResNet50_vd模型，在一些实际场景中，使用该预训练模型的识别准确率最多可以提升30%。
+- 10万类图像分类预训练模型：百度自研并开源了基于10万类数据集训练的 `ResNet50_vd `模型，在一些实际场景中，使用该预训练模型的识别准确率最多可以提升30%。
 
 - 多种训练方案，包括多机训练、混合精度训练等。