Junjun2016/APCNet 开源项目中文解读：技术栈、适用场景与上手建议

Junjun2016

项目介绍

Junjun2016/APCNet 是一个托管在 GitHub 上的开源项目，主要使用 Python 编写，采用 Apache License 2.0 许可证，目前获得 22 个 Star、3 次 Fork。

仓库全名: Junjun2016/APCNet

README 摘要

Adaptive Pyramid Context Network for Semantic Segmentation (APCNet CVPR'2019) Introduction Official implementation of Adaptive Pyramid Context Network for Semantic Segmentation (Paper). 🔥🔥 APCNet is on MMsegmentation. 🔥🔥 Overview Framework Results and models Cityscapes Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | : | -------- | -------------- |…

完整内容见 GitHub 原始 README 或本页。

常见问题

APCNet 用什么语言开发？

主要使用 Python 编写，采用 Apache License 2.0 许可证，目前有 22 个 Star。

APCNet 可以免费商用吗？

如何获取 APCNet 的源码？

可以直接克隆仓库：git clone https://github.com/Junjun2016/APCNet.git；安装与运行步骤见仓库 README。

APCNet 最近还在维护吗？

仓库最近一次更新为 2021年8月29日，当前没有未关闭的 Issue；Star 数为 22，可作为社区活跃度的参考。

关于

Adaptive Pyramid Context Network for Semantic Segmentation (APCNet CVPR'2019)

acm adaptive-context-module ade20k apcnet cityscapes mmsegmentation non-local self-attention semantic-segmentation transformer

Apache License 2.0
22 Stars
3 Forks
22 Watching

描述取自 GitHub 官方 API，中文内容由 GitRead 中文社区编辑团队整理。

简要分析

热度分

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活跃状态

活跃

GitHub

Adaptive Pyramid Context Network for Semantic Segmentation (APCNet CVPR’2019)

Introduction

Official implementation of Adaptive Pyramid Context Network for Semantic Segmentation (Paper).
🔥🔥 APCNet is on MMsegmentation. 🔥🔥

@InProceedings{He_2019_CVPR,
author = {He, Junjun and Deng, Zhongying and Zhou, Lei and Wang, Yali and Qiao, Yu},
title = {Adaptive Pyramid Context Network for Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}

Overview

Framework

Results and models

Cityscapes

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
APCNet	R-50-D8	512x1024	40000	7.7	3.57	78.02	79.26	config	model \| log
APCNet	R-101-D8	512x1024	40000	11.2	2.15	79.08	80.34	config	model \| log
APCNet	R-50-D8	769x769	40000	8.7	1.52	77.89	79.75	config	model \| log
APCNet	R-101-D8	769x769	40000	12.7	1.03	77.96	79.24	config	model \| log
APCNet	R-50-D8	512x1024	80000	-	-	78.96	79.94	config	model \| log
APCNet	R-101-D8	512x1024	80000	-	-	79.64	80.61	config	model \| log
APCNet	R-50-D8	769x769	80000	-	-	78.79	80.35	config	model \| log
APCNet	R-101-D8	769x769	80000	-	-	78.45	79.91	config	model \| log

ADE20K

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
APCNet	R-50-D8	512x512	80000	10.1	19.61	42.20	43.30	config	model \| log
APCNet	R-101-D8	512x512	80000	13.6	13.10	45.54	46.65	config	model \| log
APCNet	R-50-D8	512x512	160000	-	-	43.40	43.94	config	model \| log
APCNet	R-101-D8	512x512	160000	-	-	45.41	46.63	config	model \| log

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