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Posted to dev@singa.apache.org by "wentong (JIRA)" <ji...@apache.org> on 2018/05/14 06:50:00 UTC
[jira] [Updated] (SINGA-363) Add DenseNet for ImageNet
classification
[ https://issues.apache.org/jira/browse/SINGA-363?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
wentong updated SINGA-363:
--------------------------
Description:
The DenseNet[ |http://arxiv.org/abs/1602.07261]models are a series of deep CNN models with high performance.
We convert the parameters pre-trained from Pytorch into pickle dictionary and load them into the net created using SINGA FeedForwardNet.
Coding style and format will follow previous examples.
In the meantime, some other minor bugs in "examples" folder would be fixed in order to run in Python 3 environment.
was:
The DenseNet[ |http://arxiv.org/abs/1602.07261]models are a series of deep CNN models with high performance.
We convert the parameters pre-trained from Pytorch into pickle dictionary and load them into the net created using SINGA FeedForwardNet.
In the meantime, some other minor bugs in "examples" folder would be fixed in order to run in Python 3 environment.
> Add DenseNet for ImageNet classification
> ----------------------------------------
>
> Key: SINGA-363
> URL: https://issues.apache.org/jira/browse/SINGA-363
> Project: Singa
> Issue Type: New Feature
> Reporter: wentong
> Priority: Minor
>
> The DenseNet[ |http://arxiv.org/abs/1602.07261]models are a series of deep CNN models with high performance.
> We convert the parameters pre-trained from Pytorch into pickle dictionary and load them into the net created using SINGA FeedForwardNet.
> Coding style and format will follow previous examples.
> In the meantime, some other minor bugs in "examples" folder would be fixed in order to run in Python 3 environment.
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