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# Image Classification using Residual Networks


In this example, we convert Residual Networks trained on [Torch](https://github.com/facebook/fb.resnet.torch) to SINGA for image classification. Tested on [SINGA commit] with the [parameters pretrained by Torch](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz)

## 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/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](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz)|[34](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-34.tar.gz)|[101](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-101.tar.gz)|[152](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-152.tar.gz)
  * addbn (resnet with a batch normalization layer after the addition), [50](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-50.tar.gz)
  * wrn (wide resnet), [50](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/wrn-50-2.tar.gz)
  * preact (resnet with pre-activation) [200](https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-200.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 extracted from the original [torch files](https://github.com/facebook/fb.resnet.torch/tree/master/pretrained) via
the convert.py program.

Usage:

    $ python convert.py -h