content/zh/_sources/docs/model_zoo/imagenet/vgg/README.md.txt

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name: VGG on ImageNet SINGA version: 1.1.1 SINGA commit: license: https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py

---

# 用VGG做图像分类


这个例子中，我们将PyTorch训练好的VGG转换为SINGA模型以用作图像分类。

## 操作说明

* 下载参数的checkpoint文件到如下目录

        $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg11.tar.gz
		$ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt
		$ tar xvf vgg11.tar.gz

* 运行程序

        $ python serve.py -h

* 例子

        # use cpu
		$ python serve.py --use_cpu --parameter_file vgg11.pickle --depth 11 &
  		# use gpu
		$ python serve.py --parameter_file vgg11.pickle --depth 11 &

	我们提供了以下模型和深度配置的参数文件:
	* 不使用批量正则, [11](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg11.tar.gz), [13](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg13.tar.gz), [16](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg16.tar.gz), [19](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg19.tar.gz)
	* 使用批量正则, [11](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg11_bn.tar.gz), [13](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg13_bn.tar.gz), [16](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg16_bn.tar.gz), [19](https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg19_bn.tar.gz)


* 提交图片进行分类

        $ curl -i -F image=@image1.jpg http://localhost:9999/api
        $ curl -i -F image=@image2.jpg http://localhost:9999/api
        $ curl -i -F image=@image3.jpg http://localhost:9999/api

image1.jpg, image2.jpg和image3.jpg应该在执行指令前就已被下载。

## 详细信息

用`convert.py`从Pytorch参数文件中提取参数值

* 运行程序

    	$ python convert.py -h