[mlpack] Ready to use Models in mlpack (GSOC '21)

[Aakash kaushik]

HI Marcus,

Thank you so much for reaching back, So just to clarify i would keep the
deliverables to just two which will be:

1. Semantic segmentation dataloader in the format of COCO dataset .
2. One semantic segmentation model

If I understood you correctly, will you be able to help me decide which
kind of model I should add, should i go for a model that is more generally
used such as U-Net or one from the above list that PyTorch has ?

Best,
Aakash

On Wed, Mar 17, 2021 at 7:55 PM Marcus Edel <marcus.edel at fu-berlin.de>
wrote:

> Hello Aakash,
>
> thanks for the interest in the project and all the contributions; what you
> proposed
> looks quite useful to me and as you already pointed out would integrate
> really well
> with some of the existing functionalities.
>
> I guess for loading segmentation datasets we will stick with a common
> format e.g.
> COCO, and add support for the data loader and potentially add support for
> other
> formats later?
>
> One remark about the scope, you might want to remove one model from the
> list, and
> add a note to the proposal something along the lines of, if there is time
> left at the end
> of the summer, I propose to work on z, but the focus is on x and y.
>
> I hope what I said was useful; please don't hesitate to ask if anything
> needs clarification.
>
> Thanks,
> Marcus
>
> On 16. Mar 2021, at 00:16, Aakash kaushik <kaushikaakash7539 at gmail.com>
> wrote:
>
> Hey everyone,
>
> My name is Aakash Kaushik <https://github.com/Aakash-kaushik> and I
> have been contributing for some time specifically on the ANN codebase in
> mlpack.
>
> And the project idea that is ready to use Models in mlpack peaks my
> interest. So initially i would like to propose a data loader and 2 models
> for semantic segmentation because i see that the data loaders for image
> classification and object detection are already there and including a
> couple of models and a data loader in GSOC for semantic segmentation will
> open the gates for further contribution of models in all three fields as
> they would only need to worry about the model and not loading the data and
> also will have some reference models in that field
>
> So the data loader would be capable of taking up image segmentation data
> that is the real image, segmentation map, segmentation map to class
> mapping, and for the models i am a bit confused as if we want some basic
> nets such as U-nets or a combination of both a basic net and state of the
> art model, or two state of the art model. Pytorch supports couple of models
> in the semantic segmentation fields which are:
>
> 1. FCN ResNet50, ResNet101
> 2. DeepLabV3 ResNet50, ResNet101, MobileNetV3-Large
> 3. LR-ASPP MobileNetV3-Large
>
> And so i should be able to convert their weights from pytorch to mlpack by
> modifying the utility created by kartik dutt which is
> mlpack-PyTorch-Weight-Translator
> <https://github.com/kartikdutt18/mlpack-PyTorch-Weight-Translator>
>
> I am trying to keep the deliverables to just three which is a data loader
> and 2 models as the GSOC period is reduced to just 1.5 months and for these
> three things i would have to write tests, documentation and example usage
> in the example repository.
>
> And before this work as we are in the process of removing boost visitors
> from the ANN codebase and had couple of major changes to the mlpack
> codebase the models repo wasn't able to keep up with it so my main goal
> before GSOC starts would be to work on the PR that is to  Swap
> boost::variant with vtable <https://github.com/mlpack/mlpack/pull/2777> and
> then make changes to the code in models repo to adjust the change in boost
> visitors, serialization and porting tests to catch2.
>
> I wanted to hear from you if this is the right path and if the number of
> deliverables are right for this and help in choosing the exact models that
> i should pick that would be the most helpful or beneficial to the library.
>
> Best,
> Aakash
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>
>
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