zhangrj91 Goto Github PK

android_lab

autodeeplab

Pytorch Implementation the paper Auto-DeepLab Hierarchical Neural Architecture Search for Semantic Image Segmentation

autodeeplab-1

AutoDeeplab / auto-deeplab / AutoML for semantic segmentation, implemented in Pytorch

contactstest

A project for learning Contentprovider

darkpose

Distribution-Aware Coordinate Representation for Human Pose Estimation

darts

Differentiable architecture search for convolutional and recurrent networks

efficientnet-pytorch

A PyTorch implementation of EfficientNet

faster-rcnn.pytorch

A faster pytorch implementation of faster r-cnn

finalproj-v1

fna

Fast Neural Network Adaptation via Parameter Remapping and Architecture Search (ICLR2020)

fpn-semantic-segmentation

use FPN to FPN-Semantic-segmentation。pytorch version

fpn.pytorch

Pytorch implementation of Feature Pyramid Network (FPN) for Object Detection

group1

guidemap

A custom component that mimics map distribution and browsing.

higherhrnet-human-pose-estimation

This is an official implementation of our CVPR 2020 paper "HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation" (https://arxiv.org/abs/1908.10357)

hrformer

This is an official implementation of our NeurIPS 2021 paper "HRFormer: High-Resolution Transformer for Dense Prediction".

hrnet-human-pose-estimation

This repo is copied from https://github.com/leoxiaobin/deep-high-resolution-net.pytorch

imdroid-mapping-client

java

java-websocket

A barebones WebSocket client and server implementation written in 100% Java.

javalearning

javaswing

k_rob

lab3

learning

学习过程中的全部

loginpaneldemo

nasnet

reimplementation of "Learning Transferable Architectures for Scalable Image Recognition" using mnist dataset, include controller

netty-websocket

Netty WebSocket聊天室(只包含接口).推送消息/单聊/群聊/房间聊天,轻松支持万人聊天.使用技术Spring Boot+Netty+Mybatis+ Ehcache +Srping Security+ Jwt 命令拆分,扩展方便.可作为基础框架.

nn-cuda-learning

pose-ae-train

Training code for "Associative Embedding: End-to-End Learning for Joint Detection and Grouping"