Image Classification using DenseNet

In this example, we convert DenseNet on PyTorch to SINGA for image classification.

Instructions

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

Download one parameter checkpoint file

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

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