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https://issues.apache.org/jira/browse/TIKA-2298?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15964786#comment-15964786
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ASF GitHub Bot commented on TIKA-2298:
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thammegowda commented on issue #159: Creation of TIKA-2298 contributed by
asmehra95- Import of vgg16 via Deeplearning4j
URL: https://github.com/apache/tika/pull/159#issuecomment-293358840
@asmehra95 appreciate your effort. Thanks for updating the code based on our
review.
1. I feel this PR should be raised to `tika-dl` module that is being
proposed in #165 so that we can isolate DL4J dependencies to that module
instead of `tika-parsers`. we have to wait till #165 PR gets merged and then
move your classes inside tika-dl module.
2. I am not sure whats happening with online/offline issue. It seems to me
that one or other necessary file is missing (either the Keras JSON model, or
the weights or the labels) so it tries to download from S3. I will have a
closer look again and report my findings.
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> 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.
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