[mlpack] GSOC 21

CED18I048 SHAH ANWAAR KHALID ced18i048 at iiitdm.ac.in
Sun Mar 28 07:04:25 EDT 2021


Hi Vishwas!

I just wanted to say if you decide to go ahead with implementing Inception
V3 for the mlpack/models repository, you may find the following PR useful:

Inception V3 Layer <https://github.com/mlpack/mlpack/pull/2848>

In this PR, I've implemented various blocks ( except Auxiliary Classifier)
in Inception V3 network. All the tests have also been written.

Regards
Shah Anwaar Khalid


On Fri, Mar 26, 2021 at 10:32 PM 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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