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