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albumentations_examples

Augmentations usage examples for albumentations library

algorithmmarkdown

annotated_deep_learning_paper_implementations

🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

automl

Google Brain AutoML

copy-paste-for-semantic-segmentation

Unofficial implementation of Copy-Paste method：Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation

ctpn

Detecting Text in Natural Image with Connectionist Text Proposal Network (ECCV'16)

cvpr-2021-papers

dask-sql

Distributed SQL Engine in Python using Dask

dask-tutorial

Dask tutorial

data-visualization3

该项目主要用来存放纯数据挖掘的项目内容

datart

Datart is a next generation Data Visualization Open Platform

datawhale--

A cv practice project

deep-learning-for-image-processing

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

deep-learning-models

Keras code and weights files for popular deep learning models.

deeplabv3plus-pytorch

Here is a pytorch implementation of deeplabv3+ supporting ResNet(79.155%) and Xception(79.945%). Multi-scale & flip test and COCO dataset interface has been finished.

deeplearning-utils

Commonly ulits in deep learning.

deepweeds

A Multiclass Weed Species Image Dataset for Deep Learning

efficientdet-pytorch

这是一个efficientdet-pytorch的源码，可以用于训练自己的模型。

hello-world

hello_world

第一个git

koalas

Koalas: pandas API on Apache Spark

latex

从零开始 LaTeX 快速入门

machine-learning-learning-notes

周志华《机器学习》又称西瓜书是一本较为全面的书籍，书中详细介绍了机器学习领域不同类型的算法(例如：监督学习、无监督学习、半监督学习、强化学习、集成降维、特征选择等)，记录了本人在学习过程中的理解思路与扩展知识点，希望对新人阅读西瓜书有所帮助！

mcq

Official code for "Bridging Video-text Retrieval with Multiple Choice Questions", CVPR 2022 (Oral).

metrics

Machine learning metrics for distributed, scalable PyTorch applications.

minigemini

Official implementation for Mini-Gemini

model-compression

model compression based on pytorch (1、quantization: 8/4/2bits(dorefa)、ternary/binary value(twn/bnn/xnor-net)；2、 pruning: normal、regular and group convolutional channel pruning；3、 group convolution structure；4、batch-normalization folding for binary value of feature(A))

prepare_detection_dataset

convert dataset to coco/voc format

python-tutorial

pytorch-camp