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From "Amol Lele (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (MXNET-1083) Write examples to demonstrate the inference workflow using C++ API.
Date Fri, 12 Oct 2018 18:31:00 GMT

     [ https://issues.apache.org/jira/browse/MXNET-1083?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Amol Lele updated MXNET-1083:
-----------------------------
    Description: 
We are planning to provide examples that demonstrate the inference workflow using C++ API.

These examples will demonstrate:
 #   How to load pre-trained network and its parameters.
 #   How to pre-process the data for validation/inference.
 #  Measure the throughput and validation accuracy.

 

These examples will be available under separate folder. We will primarily focus on CiFar10
dataset. We will use models that are available at http://data.mxnet.io/models/

In order to be consistent with python examples, the cpp-package examples will demonstrate
usage of
 # 'imagenet1k-inception-bn'
 # 'imagenet1k-resnet-18'
 # 'imagenet1k-resnet-34'
 # 'imagenet1k-resnet-50'
 # 'imagenet1k-resnet-101'
 # 'imagenet1k-resnet-152'
 # 'imagenet1k-resnext-50'
 # 'imagenet1k-resnext-101'
 # 'imagenet1k-resnext-101-64x4d'
 # 'imagenet11k-resnet-152'
 # 'imagenet11k-place365ch-resnet-152'
 # 'imagenet11k-place365ch-resnet-50'

 

 

 

> Write examples to demonstrate the inference workflow using C++ API.
> -------------------------------------------------------------------
>
>                 Key: MXNET-1083
>                 URL: https://issues.apache.org/jira/browse/MXNET-1083
>             Project: Apache MXNet
>          Issue Type: Story
>          Components: Apache MXNet C/C++ API
>            Reporter: Amol Lele
>            Assignee: Amol Lele
>            Priority: Major
>
> We are planning to provide examples that demonstrate the inference workflow using C++
API.
> These examples will demonstrate:
>  #   How to load pre-trained network and its parameters.
>  #   How to pre-process the data for validation/inference.
>  #  Measure the throughput and validation accuracy.
>  
> These examples will be available under separate folder. We will primarily focus on CiFar10
dataset. We will use models that are available at http://data.mxnet.io/models/
> In order to be consistent with python examples, the cpp-package examples will demonstrate
usage of
>  # 'imagenet1k-inception-bn'
>  # 'imagenet1k-resnet-18'
>  # 'imagenet1k-resnet-34'
>  # 'imagenet1k-resnet-50'
>  # 'imagenet1k-resnet-101'
>  # 'imagenet1k-resnet-152'
>  # 'imagenet1k-resnext-50'
>  # 'imagenet1k-resnext-101'
>  # 'imagenet1k-resnext-101-64x4d'
>  # 'imagenet11k-resnet-152'
>  # 'imagenet11k-place365ch-resnet-152'
>  # 'imagenet11k-place365ch-resnet-50'
>  
>  
>  



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