[
https://issues.apache.org/jira/browse/TIKA-2298?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15934847#comment-15934847
]
Thamme Gowda commented on TIKA-2298:
------------------------------------
[~asmehra95]
Please share a link to your code, I will have a look on this!
Could you also refer to my example code at
https://github.com/USCDataScience/dl4j-kerasimport-examples/tree/master/dl4j-import-example
and see what flags to pass to the importer (especially flags to disable
further training)?
PR to that repo with your VGG16 example would be greatly appreciated!
> To improve object recognition parser so that it may work without external
> RESTful service setup
> -----------------------------------------------------------------------------------------------
>
> Key: TIKA-2298
> URL: https://issues.apache.org/jira/browse/TIKA-2298
> Project: Tika
> Issue Type: Improvement
> Components: parser
> Affects Versions: 1.14
> Reporter: Avtar Singh
> Labels: ObjectRecognitionParser
> Fix For: 1.15
>
> Original Estimate: 672h
> Remaining Estimate: 672h
>
> When ObjectRecognitionParser was built to do image recognition, there wasn't
> good support for Java frameworks. All the popular neural networks were in
> C++ or python. Since there was nothing that runs within JVM, we tried
> several ways to glue them to Tika (like CLI, JNI, gRPC, REST).
> However, this game is changing slowly now. Deeplearning4j, the most famous
> neural network library for JVM, now supports importing models that are
> pre-trained in python/C++ based kits [5].
> *Improvement:*
> It will be nice to have an implementation of ObjectRecogniser that
> doesn't require any external setup(like installation of native libraries or
> starting REST services). Reasons: easy to distribute and also to cut the IO
> time.
--
This message was sent by Atlassian JIRA
(v6.3.15#6346)
