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