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id: version-2.0.0-model-zoo-imagenet-resnet title: Image Classification using Residual Networks original_id: model-zoo-imagenet-resnet

In this example, we convert Residual Networks trained on Torch to SINGA for image classification. Tested with the parameters pretrained by Torch

Instructions

Please cd to singa/examples/imagenet/resnet/ for the following commands

Download

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/resnet/resnet-18.tar.gz
$ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt
$ tar xvf resnet-18.tar.gz

Usage

$ python serve.py -h

Example

# use cpu
$ python serve.py --use_cpu --parameter_file resnet-18.pickle --model resnet --depth 18 &
# use gpu
$ python serve.py --parameter_file resnet-18.pickle --model resnet --depth 18 &

The parameter files for the following model and depth configuration pairs are provided:

• resnet (original resnet), 18|34|101|152
• addbn (resnet with a batch normalization layer after the addition), 50
• wrn (wide resnet), 50
• preact (resnet with pre-activation) 200

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 extracted from the original torch files via the convert.py program.

Usage:

$ python convert.py -h