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网约车

awesome-domain-adaptation

A collection of AWESOME things about domian adaptation

coding-interview-university

A complete computer science study plan to become a software engineer.

cvpr-2021-papers

d2det

D2Det: Towards High Quality Object Detection and Instance Segmentation (CVPR2020)

dapn

A pytorch implementation of "Domain-Adaptive Few-Shot Learning"

dcnet

Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection, CVPR 2021

deep-learning

deep-learning-for-tracking-and-detection

Collection of papers, datasets, code and other resources for object tracking and detection using deep learning

defrcn

edrnet

⚡EDRNet:Encoder-Decoder Residual Network for Salient Object Detection of Strip Steel Surface Defects

few-shot-object-detection-via-muti-

Implementations of few-shot object detection benchmarks

free-programming-books-zh_cn

:books: 免费的计算机编程类中文书籍，欢迎投稿

fsce

java-eight-part

『Java八股文』Java面试套路，Java进阶学习，打破内卷拿大厂Offer，升职加薪！

kejuzhang.github.io

个人博客

keyan

ldms

高校实验室设备管理系统。

lm-bff

ACL'2021: LM-BFF: Better Few-shot Fine-tuning of Language Models https://arxiv.org/abs/2012.15723

mall-admin-web

mall-admin-web是一个电商后台管理系统的前端项目，基于Vue+Element实现。 主要包括商品管理、订单管理、会员管理、促销管理、运营管理、内容管理、统计报表、财务管理、权限管理、设置等功能。

paddlepaddle

PaddlePaddle

sa-autoaug

Scale-aware Automatic Augmentation for Object Detection (CVPR 2021)

supermix

Pytorch implementation of CVPR2021 paper: SuperMix: Supervising the Mixing Data Augmentation

surface-inspection-defect-detection-dataset

This project include several different surfaces, each surface contains one or several defects. For segmentation,object detection, saliency detection，etc

test

test1

test_one

thundernet-review

Real-time generic object detection on mobile platforms is a crucial but challenging computer vision task. However, previous CNN-based detectors suffer from enormous computational cost, which hinders them from real-time inference in computation-constrained scenarios. In this paper, we investigate the effectiveness of two-stage detectors in real-time generic detection and propose a lightweight twostage detector named ThunderNet. In the backbone part, we analyze the drawbacks in previous lightweight backbones and present a lightweight backbone designed for object detection. In the detection part, we exploit an extremely efficient RPN and detection head design. To generate more discriminative feature representation, we design two efficient architecture blocks, Context Enhancement Module and Spatial Attention Module. At last, we investigate the balance between the input resolution, the backbone, and the detection head. Compared with lightweight one-stage detectors, ThunderNet achieves superior performance with only 40% of the computational cost on PASCAL VOC and COCO benchmarks. Without bells and whistles, our model runs at 24.1 fps on an ARM-based device. To the best of our knowledge, this is the first real-time detector reported on ARM platforms. Code will be released for paper reproduction.

transferlearning

Everything about Transfer Learning and Domain Adaptation--迁移学习

vuepress

test