Giter Site home page Giter Site logo

kamalengine's People

Contributors

1anjibaiyun1 avatar bingowo avatar canyizl avatar cuzny avatar gaotianhong avatar horseee avatar lingyaoluu avatar marsha1147 avatar maxwzju avatar ssssseason avatar vainf avatar yangxinyu-april avatar ying-yuchen avatar zcluu avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

kamalengine's Issues

Can I update some new features?

Hello! I read the paper and want to implement the code, and I want to add some KA features on vit for image classification. My ideas are as below:
Provide a comprehensive and robust pipeline and a data preprocessing method that allows training with own datasets.
Implement KA on vision transformers to accomplish image classification tasks.(finetuned on several datasets such as Stanford Dogs)
Provide a multimodal transfer method to apply KA for audio classification.

mismatches of definitions

Is this model not finished or what? I was trying the examples and there's been many mismatches of definitions.
For example
KamalEngine/kamal/slim/prunning/strategy.py", line 23, in
_PRUNABLE_MODULES= tp.DependencyGraph.PRUNABLE_MODULES
AttributeError: type object 'DependencyGraph' has no attribute 'PRUNABLE_MODULES'

Question about details of Task Branch

Hi, thank you for sharing such an awesome project. I have some questions about the details in your code, and I sincerely hope you can help me solve them.

1 In your paper, it is said that "We conduct knowledge amalgamation for each block of TargetNet" (Section 5.1.1), however, in the code, it seems that you only apply the knowledge amalgamation on the decoder blocks. This makes me feel confused that whether we need to apply it on the encoder blocks, as in the "layerwise amal".

2 In the task branch code, should the object of student be JointSegNet, instead of BranchySegNet? As in BranchySegNet, the network does not use teacher's blocks.
Since after running the code on NYUv2, I only got a target net with mIOU 0.2830 by merging the depth model (RMSE: 0.6772) and the seg model (MIOU: 0.5227) with BranchySegNet. When I replace it with JointSegNet, I can get a target net with mIOU 0.5233.

I would really appreciate it if you could help me. Look forward to your reply, thanks!

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.