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).
@@ -8,17 +8,23 @@ PaddleClas在Windows 平台下基于`Visual Studio 2019 Community` 进行了测
* CUDA 9.0 / CUDA 10.0，cudnn 7.6+ （仅在使用GPU版本的预测库时需要）
* CMake 3.0+
请确保系统已经安装好上述基本软件，以下测试基于`Visual Studio 2019 Community`版本。
请确保系统已经正确安装并配置好上述基本软件，其中：
* 在安装`Visual Studio 2019`时，`工作负载`需要勾选`使用C++的桌面开发`；
* CUDA需要正确安装并设置系统环境变量；
* CMake需要正确安装并将路径添加到系统环境变量中。
以下测试基于`Visual Studio 2019 Community`版本。
**下面所有示例以工作目录为 `D:\projects`演示**。
### Step1: 下载PaddlePaddle C++ 预测库 fluid_inference
### Step1: 下载PaddlePaddle C++ 预测库 paddle_inference_install_dir
PaddlePaddle C++ 预测库针对不同的`CPU`和`CUDA`版本提供了不同的预编译版本，请根据实际情况下载: [C++预测库下载列表](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/advanced_guide/inference_deployment/inference/windows_cpp_inference.html)。
@@ -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
- 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.
**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.
* 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
