[Release/2.0 rc1] Fix some description of documentations (#474)

* Fix serial number error * Fix the environment description of running PaddleClas * Add the overview of other models * Modify the pretrained models to dygraph

[Release/2.0 rc1] Fix some description of documentations (#474)
* Fix serial number error * Fix the environment description of running PaddleClas * Add the overview of other models * Modify the pretrained models to dygraph
5287ee8b · Tingquan Gao · GitHub · f6e025d5 · 5287ee8b · 5287ee8b
12 changed file
--- a/README.md
+++ b/README.md
@@ -65,6 +65,7 @@ PaddleClas is a toolset for image classification tasks prepared for the industry
    - [Inception series](#Inception_series)
    - [EfficientNet and ResNeXt101_wsl series](#EfficientNet_and_ResNeXt101_wsl_series)
    - [ResNeSt and RegNet series](#ResNeSt_and_RegNet_series)
+    - [Others](#Others)
    - HS-ResNet: arxiv link: [https://arxiv.org/pdf/2010.07621.pdf](https://arxiv.org/pdf/2010.07621.pdf). Code and models are coming soon!
 - Model training/evaluation
    - [Data preparation](./docs/en/tutorials/data_en.md)
@@ -315,6 +316,25 @@ Accuracy and inference time metrics of ResNeSt and RegNet series models are show
 | RegNetX_4GF            | 0.785     | 0.9416    |    6.46478              |      11.19862           | 8        | 22.1      | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_4GF_pretrained.pdparams)            |


+<a name="Others"></a>
+
+### Others
+
+Accuracy and inference time metrics of AlexNet, SqueezeNet series, VGG series and DarkNet53 models are shown as follows. More detailed information can be refered to [Others](./docs/en/models/Others_en.md).
+
+
+| Model                     | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | Download Address |
+|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|
+| AlexNet       | 0.567 | 0.792 | 1.44993         | 2.46696         | 1.370 | 61.090 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams) |
+| SqueezeNet1_0 | 0.596 | 0.817 | 0.96736 | 2.53221         | 1.550 | 1.240 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams) |
+| SqueezeNet1_1 | 0.601 | 0.819 | 0.76032       | 1.877      | 0.690   | 1.230 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams) |
+| VGG11 | 0.693 | 0.891 | 3.90412 | 9.51147 | 15.090 | 132.850 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG11_pretrained.pdparams) |
+| VGG13 | 0.700 | 0.894 | 4.64684 | 12.61558 | 22.480 | 133.030 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG13_pretrained.pdparams) |
+| VGG16 | 0.720 | 0.907 | 5.61769 | 16.40064 | 30.810 | 138.340 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG16_pretrained.pdparams) |
+| VGG19 | 0.726 | 0.909 | 6.65221 | 20.4334 | 39.130 | 143.650 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG19_pretrained.pdparams) |
+| DarkNet53 | 0.780 | 0.941 | 4.10829 | 12.1714 | 18.580 | 41.600 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams) |
+
+
 <a name="License"></a>
 ## License


--- a/README_cn.md
+++ b/README_cn.md
@@ -67,6 +67,7 @@
    - [Inception系列](#Inception系列)
    - [EfficientNet与ResNeXt101_wsl系列](#EfficientNet与ResNeXt101_wsl系列)
    - [ResNeSt与RegNet系列](#ResNeSt与RegNet系列)
+    - [其他模型](#其他模型)
    - HS-ResNet: arxiv文章链接: [https://arxiv.org/pdf/2010.07621.pdf](https://arxiv.org/pdf/2010.07621.pdf)。 代码和预训练模型即将开源，敬请期待。
 - 模型训练/评估
    - [数据准备](./docs/zh_CN/tutorials/data.md)
@@ -317,8 +318,26 @@ ResNeSt与RegNet系列模型的精度、速度指标如下表所示，更多关
 | ResNeSt50              | 0.8083    | 0.9542    | 6.69042    | 8.01664                | 10.78    | 27.5      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_pretrained.pdparams)              |
 | RegNetX_4GF            | 0.785     | 0.9416    |    6.46478              |      11.19862           | 8        | 22.1      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_4GF_pretrained.pdparams)            |

+<a name="其他模型"></a>
+
+### 其他模型
+
+关于AlexNet、SqueezeNet系列、VGG系列、DarkNet53等模型的精度、速度指标如下表所示，更多介绍可以参考：[其他模型文档](./docs/zh_CN/models/Others.md)。
+
+
+| 模型                     | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | 下载地址 |
+|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|
+| AlexNet       | 0.567 | 0.792 | 1.44993         | 2.46696         | 1.370 | 61.090 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams) |
+| SqueezeNet1_0 | 0.596 | 0.817 | 0.96736 | 2.53221         | 1.550 | 1.240 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams) |
+| SqueezeNet1_1 | 0.601 | 0.819 | 0.76032       | 1.877      | 0.690   | 1.230 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams) |
+| VGG11 | 0.693 | 0.891 | 3.90412 | 9.51147 | 15.090 | 132.850 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG11_pretrained.pdparams) |
+| VGG13 | 0.700 | 0.894 | 4.64684 | 12.61558 | 22.480 | 133.030 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG13_pretrained.pdparams) |
+| VGG16 | 0.720 | 0.907 | 5.61769 | 16.40064 | 30.810 | 138.340 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG16_pretrained.pdparams) |
+| VGG19 | 0.726 | 0.909 | 6.65221 | 20.4334 | 39.130 | 143.650 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/VGG19_pretrained.pdparams) |
+| DarkNet53 | 0.780 | 0.941 | 4.10829 | 12.1714 | 18.580 | 41.600 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams) |

 <a name="许可证书"></a>
+
 ## 许可证书
 本项目的发布受<a href="https://github.com/PaddlePaddle/PaddleCLS/blob/master/LICENSE">Apache 2.0 license</a>许可认证。


--- a/configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml
+++ b/configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml
@@ -2,7 +2,6 @@ mode: 'train'
 ARCHITECTURE:
    name: 'MobileNetV3_large_x1_0'
 pretrained_model: "./pretrained/MobileNetV3_large_x1_0_pretrained"
-load_static_weights: True
 model_save_dir: "./output/"
 classes_num: 102
 total_images: 1020

--- a/configs/quick_start/R50_vd_distill_MV3_large_x1_0.yaml
+++ b/configs/quick_start/R50_vd_distill_MV3_large_x1_0.yaml
@@ -4,10 +4,7 @@ ARCHITECTURE:

 pretrained_model:
    - "./pretrained/flowers102_R50_vd_final/ppcls"
-    - "./pretrained/MobileNetV3_large_x1_0_pretrained/"
-load_static_weights:
-    - False
-    - True
+    - "./pretrained/MobileNetV3_large_x1_0_pretrained"
 model_save_dir: "./output/"
 classes_num: 102
 total_images: 7169

--- a/configs/quick_start/ResNet50_vd.yaml
+++ b/configs/quick_start/ResNet50_vd.yaml
@@ -4,7 +4,6 @@ ARCHITECTURE:

 checkpoints: ""
 pretrained_model: ""
-load_static_weights: True
 model_save_dir: "./output/"
 classes_num: 102
 total_images: 1020

--- a/configs/quick_start/ResNet50_vd_finetune.yaml
+++ b/configs/quick_start/ResNet50_vd_finetune.yaml
@@ -2,7 +2,6 @@ mode: 'train'
 ARCHITECTURE:
    name: 'ResNet50_vd'
 pretrained_model: "./pretrained/ResNet50_vd_pretrained"
-load_static_weights: true
 model_save_dir: "./output/"
 classes_num: 102
 total_images: 1020

--- a/configs/quick_start/ResNet50_vd_ssld_finetune.yaml
+++ b/configs/quick_start/ResNet50_vd_ssld_finetune.yaml
@@ -4,7 +4,6 @@ ARCHITECTURE:
    params:
        lr_mult_list: [0.1, 0.1, 0.2, 0.2, 0.3]
 pretrained_model: "./pretrained/ResNet50_vd_ssld_pretrained"
-load_static_weights: True
 model_save_dir: "./output/"
 classes_num: 102
 total_images: 1020

--- a/configs/quick_start/ResNet50_vd_ssld_random_erasing_finetune.yaml
+++ b/configs/quick_start/ResNet50_vd_ssld_random_erasing_finetune.yaml
@@ -4,7 +4,6 @@ ARCHITECTURE:
    params:
        lr_mult_list: [0.1, 0.1, 0.2, 0.2, 0.3]
 pretrained_model: "./pretrained/ResNet50_vd_ssld_pretrained"
-load_static_weights: True
 model_save_dir: "./output/"
 classes_num: 102
 total_images: 1020

--- a/docs/en/tutorials/install_en.md
+++ b/docs/en/tutorials/install_en.md
@@ -8,7 +8,7 @@ This document introduces how to install PaddleClas and its requirements.

 ## Install PaddlePaddle

-Python 3.6, CUDA 9.0, CUDNN7.0 nccl2.1.2 and later version are required at first, For now, PaddleClas only support training on the GPU device. Please follow the instructions in the [Installation](http://www.paddlepaddle.org.cn/install/quick) if the PaddlePaddle on the device is lower than v1.7
+Python 3.6, CUDA 9.0, CUDNN7.6.4 nccl2.1.2 and later version are required at first, For now, PaddleClas only support training on the GPU device. Please follow the instructions in the [Installation](http://www.paddlepaddle.org.cn/install/quick) if the PaddlePaddle on the device is lower than v1.7

 Install PaddlePaddle

@@ -34,6 +34,7 @@ python -c "import paddle; print(paddle.__version__)"
 Note:
 - Make sure the compiled version is later than PaddlePaddle2.0rc.
 - Indicate **WITH_DISTRIBUTE=ON** when compiling, Please refer to [Instruction](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/install/Tables.html#id3) for more details.
+- When running in docker, in order to ensure that the container has enough shared memory for data read acceleration of Paddle, please set the parameter `--shm_size=8g` at creating a docker container, if conditions permit, you can set it to a larger value.


 ## Install PaddleClas

--- a/docs/en/tutorials/quick_start_en.md
+++ b/docs/en/tutorials/quick_start_en.md
@@ -47,16 +47,14 @@ You can use the following commands to downdload the pretrained models.
 ```bash
 mkdir pretrained
 cd pretrained
-wget https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
-wget https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
-wget https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_pretrained.tar
-tar -xf ResNet50_vd_pretrained.tar
-tar -xf ResNet50_vd_ssld_pretrained.tar
-tar -xf MobileNetV3_large_x1_0_pretrained.tar
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_0_pretrained.pdparams
+
 cd ../
 ```

-**Note**: If you want to download the pretrained models on Windows environment, you can copy the links to the browser and download, then use the the thirdparty tools such as `7Zip` to uncompress the tar files.
+**Note**: If you want to download the pretrained models on Windows environment, you can copy the links to the browser and download.


 ## Training
@@ -165,7 +163,7 @@ cp -r output/ResNet50_vd/19/  ./pretrained/flowers102_R50_vd_final/

 ### Distillation

-* Use extra_list.txt as unlabeled data, Note:
+* Use `extra_list.txt` as unlabeled data, Note:
    * Samples in the `extra_list.txt` and `val_list.txt` don't have intersection
    * Because of in the source code, label information is unused, This is still unlabeled distillation
    * Teacher model use the pretrained_model trained on the flowers102 dataset, and student model use the MobileNetV3_large_x1_0 pretrained model(Acc 75.32\%) trained on the ImageNet1K dataset

--- a/docs/zh_CN/tutorials/install.md
+++ b/docs/zh_CN/tutorials/install.md
@@ -34,13 +34,14 @@ python -c "import paddle; print(paddle.__version__)"

 注意：
 - 从源码编译的PaddlePaddle版本号为0.0.0，请确保使用了PaddlePaddle 2.0rc及之后的源码编译。
- PaddleClas基于PaddlePaddle高性能的分布式训练能力，若您从源码编译，请确保打开编译选项，**WITH_DISTRIBUTE=ON**。具体编译选项参考[编译选项表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/install/Tables.html#id3)
+- PaddleClas基于PaddlePaddle高性能的分布式训练能力，若您从源码编译，请确保打开编译选项，**WITH_DISTRIBUTE=ON**。具体编译选项参考[编译选项表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/install/Tables.html#id3)。
+- 在docker中运行时，为保证docker容器有足够的共享内存用于Paddle的数据读取加速，在创建docker容器时，请设置参数`--shm_size=8g`，条件允许的话可以设置为更大的值。

 **运行环境需求:**

 - Python3 (当前只支持Linux系统)
 - CUDA >= 9.0
- cuDNN >= 5.0
+- cuDNN >= 7.6.4
 - nccl >= 2.1.2



--- a/docs/zh_CN/tutorials/quick_start.md
+++ b/docs/zh_CN/tutorials/quick_start.md
@@ -47,16 +47,14 @@ cd ../../
 ```bash
 mkdir pretrained
 cd pretrained
-wget https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
-wget https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
-wget https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_pretrained.tar
-tar -xf ResNet50_vd_pretrained.tar
-tar -xf ResNet50_vd_ssld_pretrained.tar
-tar -xf MobileNetV3_large_x1_0_pretrained.tar
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_0_pretrained.pdparams
+
 cd ../
 ```

-**注意**：如果是在windows中下载预训练模型的话，需要将地址拷贝到浏览器中并下载，然后使用`7Zip`等工具进行解压。
+**注意**：如果是在windows中下载预训练模型的话，需要将地址拷贝到浏览器中下载。

 ### 2.2 环境说明

@@ -164,7 +162,7 @@ cp -r output/ResNet50_vd/best_model/  ./pretrained/flowers102_R50_vd_final/

 ### 3.6 知识蒸馏小试牛刀

-* 使用flowers102数据集进行模型蒸馏，为了进一步提提升模型的精度，使用extra_list.txt充当无标签数据，在这里有几点需要注意：
+* 使用flowers102数据集进行模型蒸馏，为了进一步提提升模型的精度，使用`extra_list.txt`充当无标签数据，在这里有几点需要注意：
    * `extra_list.txt`与`val_list.txt`的样本没有重复，因此可以用于扩充知识蒸馏任务的训练数据。
    * 即使引入了有标签的extra_list.txt中的图像，但是代码中没有使用标签信息，因此仍然可以视为无标签的模型蒸馏。
    * 蒸馏过程中，教师模型使用的预训练模型为flowers102数据集上的训练结果，学生模型使用的是ImageNet1k数据集上精度为75.32\%的MobileNetV3_large_x1_0预训练模型。
@@ -196,7 +194,7 @@ python -m paddle.distributed.launch \
 最终flowers102验证集上的精度为0.9647，结合更多的无标签数据，使用教师模型进行知识蒸馏，MobileNetV3的精度涨幅高达6.47\%。


-### 3.6 精度一览
+### 3.7 精度一览

 * 下表给出了不同训练yaml文件对应的精度。