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 <[email protected]> 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 <[email protected]> > 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 > _______________________________________________ > mlpack mailing list > [email protected] > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack > > >
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