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

Image Classification using DenseNet

In this example, we convert DenseNet on PyTorch 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/densenet/densenet-121.tar.gz
    $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt
    $ tar xvf densenet-121.tar.gz
  

• Usage

    $ python serve.py -h
  

• Example

    # use cpu
    $ python serve.py --use_cpu --parameter_file densenet-121.pickle --depth 121 &
    # use gpu
    $ python serve.py --parameter_file densenet-121.pickle --depth 121 &
  

  The parameter files for the following model and depth configuration pairs are provided: 121, 169, 201, 161

• 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