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]> 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]> > 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] > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack > > >
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