Hey Marcus, I totally got it and i think 1 data loader and 2 models from which 1 will be a potential model if only time permits.
Thank you for the feedback and help. :D Best, Aakash On Wed, 17 Mar, 2021, 9:33 pm Marcus Edel, <[email protected]> wrote: > 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]> > 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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