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avalanche

Avalanche: an End-to-End Library for Continual Learning.

awesome-incremental-learning

Awesome Incremental Learning

awesome-knowledge-distillation

Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2021)。

awesome-pytorch-list

A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

awesome-semantic-segmentation

:metal: awesome-semantic-segmentation

brew

⛔️ DEPRECATED brew: Python Ensemble Learning API

class-incremental-learning

PyTorch implementation of AANets (CVPR 2021) and Mnemonics Training (CVPR 2020)

computer-vision-in-action

《计算机视觉实战演练：算法与应用》中文电子书、源码、读者交流社区（更新中，可以先 star）

concrete_crack_segmentation

A Unet based modified architecture for crack detection

cvpr2021-papers-with-code

CVPR 2021 论文和开源项目合集

deep-forest

An Efficient, Scalable and Optimized Python Framework for Deep Forest (2021.2.1)

deep-learning-for-image-processing

deep learning for image processing including classification and object-detection etc.

dive-into-dl-pytorch

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

dive-into-dl-tensorflow2.0

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的认可

dogsvscats

Kaggle竞赛题猫狗大战 ，用于PyTorch入门

drn

Dilated Residual Networks

efficient-segmentation-networks

Lightweight models for real-time semantic segmentationon PyTorch (include SQNet, LinkNet, SegNet, UNet, ENet, ERFNet, EDANet, ESPNet, ESPNetv2, LEDNet, ESNet, FSSNet, CGNet, DABNet, Fast-SCNN, ContextNet, FPENet, etc.)

fttl-with-keras

Transfer Learning and Fine Tuning for Cross Domain Image Classification with Keras

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

icarl

image-classification-cnn-kelm

ICIP2020论文实验，使用CNN+KELM提升accuracy

keras_image_segmentation

multimodalityfusionforclassification

多模态数据融合：为了完成多模态数据融合，首先利用VGG16网络和cifar10数据集完成多输入网络的分类，在VGG16的基础之上，将前三层特征提取网络作为不同输入的特征提取网络，在中间层进行特征拼接，后面的卷积层用于提取融合特征，最后加上全连接层。该网络稍作修改就能同时提取两张对应的图片作为输入，在特征提取之后进行融合用于分类。

online-continual-learning

A collection of online continual learning paper implementations for computer vision in PyTorch.

online-recurrent-extreme-learning-machine

Online-Recurrent-Extreme-Learning-Machine (OR-ELM) for time-series prediction, implemented in python

predrnn-pp

Code release for "PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning" (ICML 2018)

pwpae-concept-drift-detection-and-adaptation

Data stream analytics: Implement online learning methods to address concept drift in data streams using the River library. Code for the paper entitled "PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams" accepted in IEEE GlobeCom 2021.

pymoo

NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO

pyoselm

A Python implementation of Online Sequential Extreme Machine Learning (OS-ELM) for online machine learning

pytorch-book

PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》)