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| |
| # Image Classification using VGG |
| |
| |
| In this example, we convert VGG on [PyTorch](https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py) |
| to SINGA for image classification. |
| |
| ## Instructions |
| |
| * Download one parameter checkpoint file (see below) and the synset word file of ImageNet into this folder, e.g., |
| |
| $ 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 |
| |
| * Usage |
| |
| $ python serve.py -h |
| |
| * Example |
| |
| # use cpu |
| $ python serve.py --use_cpu --parameter_file vgg11.pickle --depth 11 & |
| # use gpu |
| $ python serve.py --parameter_file vgg11.pickle --depth 11 & |
| |
| The parameter files for the following model and depth configuration pairs are provided: |
| * Without batch-normalization, [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) |
| * With batch-normalization, [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) |
| |
| * Submit images for classification |
| |
| $ 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 and image3.jpg should be downloaded before executing the above commands. |
| |
| ## Details |
| |
| The parameter files were converted from the pytorch via the convert.py program. |
| |
| Usage: |
| |
| $ python convert.py -h |