TMoonLi's starred repositories
InterviewGuide
🔥🔥「InterviewGuide」是阿秀从校园->职场多年计算机自学过程的记录以及学弟学妹们计算机校招&秋招经验总结文章的汇总,包括但不限于C/C++ 、Golang、JavaScript、Vue、操作系统、数据结构、计算机网络、MySQL、Redis等学习总结,坚持学习,持续成长!
interview
📚 C/C++ 技术面试基础知识总结,包括语言、程序库、数据结构、算法、系统、网络、链接装载库等知识及面试经验、招聘、内推等信息。This repository is a summary of the basic knowledge of recruiting job seekers and beginners in the direction of C/C++ technology, including language, program library, data structure, algorithm, system, network, link loading library, interview experience, recruitment, recommendation, etc.
Contrastive-COVIDNet
[IEEE JBHI'20] Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classification
COVID19_imaging_AI_paper_list
COVID-19 imaging-based AI paper collection
Context_Aware_SSL
Official PyTorch implementation for paper Context Matters: Graph-based Self-supervised Representation Learning for Medical Images
programmer-job-blacklist
:see_no_evil:程序员找工作黑名单,换工作和当技术合伙人需谨慎啊 更新有赞
UNet-family
Paper and implementation of UNet-related model.
YOLOv4-pytorch
This is a pytorch repository of YOLOv4, attentive YOLOv4 and mobilenet YOLOv4 with PASCAL VOC and COCO
awesome-semantic-segmentation-pytorch
Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)
DeepLabV3Plus-Pytorch
Pretrained DeepLabv3 and DeepLabv3+ for Pascal VOC & Cityscapes
HRNet-Semantic-Segmentation
The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919
awesome-quant
**的Quant相关资源索引
QtaTraining2019
北京大学量化交易协会2019级培训课件及代码
OIDv4_ToolKit
Download and visualize single or multiple classes from the huge Open Images v4 dataset
go-gin-example
An example of gin
react-chrome-extension-boilerplate
Boilerplate for Chrome Extension React.js project
Yet-Another-EfficientDet-Pytorch
The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.