[mlpack] GSOC 21

[Vishwas Chepuri]

Hello Marcus,
Thanks for reaching back. As implementing VGG16 is almost done, I would
like to propose to implement following models

1) VGG19

2) InceptionV3

3) ResNet50

As suggested, along with functionalities mentioned in the previous mail I
would also like to include a tutorial for each model. If there is time left
after completely implementing every part of my proposal I would like to add
ResNet101. Thanks for reading.
Regards,
Vishwas Chepuri

On Sat, 27 Mar 2021 at 22:18, Marcus Edel <marcus.edel at fu-berlin.de> wrote:

> Hello Vishwas,
>
> thanks for getting in touch and interest in the project. The outline looks
> good to me, especially VGG16 and VGG19 since they are so widely
> used. That said my recommendation is to focus on two or three of the
> models you listed, e.g. VGG16, VG19 and InceptionV3, you can always
> propose to look into another model at the end if there is time left, but
> since testing and documentation, I guess in this case a tutorial would
> be useful, take time I would focus on a subset.
>
> I hope that was helpful, also the PR is on my review list.
>
> Thanks,
> Marcus
>
> On 26. Mar 2021, at 13:01, Vishwas Chepuri <chepurivishwas360 at gmail.com>
> wrote:
>
> Hello everyone!
>
> I am Vishwas Chepuri, a sophomore at IIT(BHU), Varanasi, India. I have
> been getting myself familiar with mlpack for the last couple of months. I
> wanted to get some opinions regarding my project proposal and kindly help
> me improve it.
>
> Idea is to implement the following ready to use models,
>
> 1) VGG16
>
> 2) VGG19
>
> 3) InceptionV3
>
> 4) ResNet50
>
> 5) ResNet101
>
> For each of the above models, I would like to implement a class following
> the class design in the models repo which includes
>
> the sketch of model with and without FC layers on top of base
> architecture, include ImageNet weights for both the models (with and
> without FC layers), implement preprocessing function which includes
> preprocessing steps with which the above-included weights are trained,
> write tests and documentation.
>
> I have opened a PR #49 (https://github.com/mlpack/models/pull/49) for
> VGG16 and VGG19 models, and I am able to get weights using PyTorch-mlpack
> Weight Converter (
> https://github.com/kartikdutt18/mlpack-PyTorch-Weight-Translator).
> Hopefully, I will complete implementing and including all the
> above-mentioned functionalities for these two models before GSOC begins.
>
> I am excited about this project. Kindly let me know your thoughts on this
> idea. Thanks for reading.
>
> Regards,
>
> Vishwas Chepuri
>
> GitHub ID: vstark21
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