NetGraft | AAAI 2021 Paper : Progressive Network

by zju-vipa Python Version: v1.0 License: No License

X-Ray Key Features Code Snippets(3)Community Discussions Vulnerabilities Install Support

kandi X-RAY | NetGraft Summary

NetGraft is a Python library. NetGraft has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

The implementation of AAAI 2021 Paper: "Progressive Network Grafting for Few-Shot Knowledge Distillation".

Support

Quality

Security

License

Reuse

Support

NetGraft has a low active ecosystem.

It has 13 star(s) with 2 fork(s). There are 4 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 3 have been closed. On average issues are closed in 48 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of NetGraft is v1.0

Quality

NetGraft has 0 bugs and 0 code smells.

Security

NetGraft has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

NetGraft code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

NetGraft does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

NetGraft releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed NetGraft and discovered the below as its top functions. This is intended to give you an instant insight into NetGraft implemented functionality, and help decide if they suit your requirements.

Calculate the accuracy for a given dataset
Train an epoch
Run graft block
Runs graft_net
Graft block of data
Builds the dataset
Download and extract the dataset
Extract num_per_class from cifar100
Extract a dataset from cifar10
Test the whole network
Get a dataset from the given args
Get a transformer for a dataset
Gets normalizer for given data set
Extract num_per_class dataset from cifar10 dataset
Splits data into num_classes
Loads cifar10 - 10 training data
Parse the given arguments
Runs the graft net
Run a graft block

Get all kandi verified functions for this library.

NetGraft Key Features

No Key Features are available at this moment for NetGraft.

NetGraft Examples and Code Snippets

NetGraft,Evaluation

Python

Lines of Code : 9

License : No License

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# ----------- Run on CIFAR10 -----------
python evaluate.py --dataset='CIFAR10' --nshot=1 # 1-Shot Distillation
python evaluate.py --dataset='CIFAR10' --nshot=5 # 5-Shot Distillation
python evaluate.py --dataset='CIFAR10' --nshot=10 # 10-Shot Distill

NetGraft,Abstract:

Python

Lines of Code : 6

License : No License

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@inproceedings{shen2021progressive,
  author    = {Shen, Chengchao and Wang, Xinchao and Yin, Youtan and Song, Jie and Luo, Sihui and Song, Mingli},
  title     = {Progressive Network Grafting for Few-Shot Knowledge Distillation},
  booktitle = {AAAI

NetGraft,Training

Python

Lines of Code : 5

License : No License

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# ----------- Run on CIFAR10 -----------
python train.py --dataset CIFAR10 # Training [1~10, 20, 50]-Shot Distillation 

# ----------- Run on CIFAR100 -----------
python train.py --dataset CIFAR100 # Training [1~10, 20, 50]-Shot Distillation

Community Discussions

No Community Discussions are available at this moment for NetGraft.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install NetGraft

You can download it from GitHub.
You can use NetGraft like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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