加入极市专业CV交流群,与6000+来自腾讯,华为,百度,北大,清华,中科院等名企名校视觉开发者互动交流!更有机会与李开复老师等大牛群内互动!
同时提供每月大咖直播分享、真实项目需求对接、干货资讯汇总,行业技术交流。点击文末“阅读原文”立刻申请入群~
作者:ycszen
原文:https://zhuanlan.zhihu.com/p/55563637
项目地址:https://github.com/ycszen/TorchSeg#why-this-name-furnace
Hi, All
我们开源了TorchSeg,其中包括我们复现的算法:DFN, BiSeNet, PSPNet.
This project aims at providing a fast, modular reference implementation for semantic segmentation models using PyTorch.
Modular Design: easily construct a customized semantic segmentation models by combining different components.
Distributed Training: >60% faster than the multi-thread parallel method(nn.DataParallel), we use the multi-processing parallel method.
Multi-GPU training and inference: support different manners of inference.
Provides pre-trained models and implement different semantic segmentation models.
PyTorch 1.0
pip3 install torch torchvision
Easydict
pip3 install easydict
Apex
Ninja
sudo apt-get install ninja-build
tqdm
pip3 install tqdm
Supported Model
FCN
DFN
BiSeNet
PSPNet
Performance and Benchmarks
SS:Single Scale MSF:Multi-scale + Flip
PASCAL VOC 2012
80.61: this result reported in paper is further finetuned on train dataset.
Cityscapes
Non-real-time Methods
BiSeNet(ours)1: because we didn't pre-train the Xception39 model on ImageNet in PyTorch, we train this experiment from scratch. We will release the pre-trained Xception39 model in PyTorch and the corresponding experiment.
Real-time Methods
ADE
后续我们会开源更多复现的语义分割算法,欢迎大家 Star 和 Contribute~
*延伸阅读