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

[Aakash kaushik]

Hey Marcus,

I totally got it and i think 1 data loader and 2 models from which 1 will
be a potential model if only time permits.

Thank you for the feedback and help. :D

Best,
Aakash


On Wed, 17 Mar, 2021, 9:33 pm Marcus Edel, <marcus.edel at fu-berlin.de> wrote:

> Yes, that’s what I had in mine, but at the end it’s your decision. About
> the
> model either is fine, you can select whatever you find more interesting.
>
> On 17. Mar 2021, at 10:45, Aakash kaushik <kaushikaakash7539 at gmail.com>
> wrote:
>
> 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
>> _______________________________________________
>> mlpack mailing list
>> mlpack at lists.mlpack.org
>> http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
>>
>>
>>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://knife.lugatgt.org/pipermail/mlpack/attachments/20210318/5afe2eda/attachment.htm>