Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

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

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 Drif…

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

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, Cont…

Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement

Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

Image-to-Image Translation in PyTorch

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

《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）

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

🤘 awesome-semantic-segmentation

Awesome Incremental Learning

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.

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

Reproduce simple cv project including attention module, classification, object detection, segmentation, keypoint detection, tracking 😄 etc.

CVPR 2021 论文和开源项目合集

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

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

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

利用pytorch实现图像分类的一个完整的代码，训练，预测，TTA，模型融合，模型部署，cnn提取特征，svm或者随机森林等进行分类，模型蒸馏，一个完整的代码

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。

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》)

PyTorch implementations of Generative Adversarial Networks.

A machine learning package for streaming data in Python. The other ancestor of River.

Hybrid CNN-ELM implementation

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

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

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。

A Unet based modified architecture for crack detection