带有大写 T 的第一个选项是 torch.Tensor 类的构造函数,而第二个选项是我们所说的工厂函数,用于构造torch.Tensor 对象并将其返回给调用者。你可以把torch.tensor()函数看作是一个工厂,它在给定一些参数输入后构建张量。工厂函数是一种创建对象的软件设计模式。好的。这就是大写的T和小写的t之间的区别,但是这两...
本案例使用Cityscape子集,数据位于fast-scnn/datasets中 importos# 数据代码下载!wget https://obs-aigallery-zc.obs.cn-north-4.myhuaweicloud.com/algorithm/fast-scnn.zip# 解压缩os.system('unzip fast-scnn.zip -d ./') --2021-06-1615:28:21--https://obs-aigallery-zc.obs.cn-north-4.myhuawei...
A PyTorch implementation ofFast-SCNN: Fast Semantic Segmentation Networkfrom the paper by Rudra PK Poudel, Stephan Liwicki. Table of Contents Installation Datasets Train Evaluate Demo Results TO DO Reference Python 3.x. Recommended usingAnaconda3 ...
PyTorch: https://github.com/shanglianlm0525/PyTorch-Networks 1 概述 Fast SCNN 受 two-branch 结构和 encoder-decoder 网络启发,用于高分辨率(1024×2048)图像上的实时语义分割任务,帧率达到123.5,准确率达到68%; 设计了低容量的Fast-SCNN,并且通过经验验证了在这个网络架构上运行更多迭代次数训练...
I created this lane_detection file and wrote this: import argparse import sys from time import time, clock from os.path import splitext, basename, exists from model import SCNN from utils.check_extension import is_video, is_image from ut...
SCNN-pytorch/models/fast_scnn.py", line 186, in forward x = self.ppm(x) File "/home/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__ result = self.forward(*input, **kwargs) File "/home/Fast-SCNN-pytorch/models/fast...
SCNN UFLD RESA LaneATT CondLane [] CLRNet(coming soon) Installation Clone this repository git clone https://github.com/turoad/lanedet.git We call this directory as$LANEDET_ROOT Create a conda virtual environment and activate it (conda is optional) ...
PyTorch implementation of over 30 realtime semantic segmentations models, e.g. BiSeNetv1, BiSeNetv2, CGNet, ContextNet, DABNet, DDRNet, EDANet, ENet, ERFNet, ESPNet, ESPNetv2, FastSCNN, ICNet, LEDNet, LinkNet, PP-LiteSeg, SegNet, ShelfNet, STDC, SwiftNet, and support knowledge distillation,...
PyTorch implementation of over 30 realtime semantic segmentations models, e.g. BiSeNetv1, BiSeNetv2, CGNet, ContextNet, DABNet, DDRNet, EDANet, ENet, ERFNet, ESPNet, ESPNetv2, FastSCNN, ICNet, LEDNet, LinkNet, PP-LiteSeg, SegNet, ShelfNet, STDC, SwiftNet, and support knowledge distillation,...
Lightweight models for real-time semantic segmentationon PyTorch (include SQNet, LinkNet, SegNet, UNet, ENet, ERFNet, EDANet, ESPNet, ESPNetv2, LEDNet, ESNet, FSSNet, CGNet, DABNet, Fast-SCNN, ContextNet, FPENet, etc.) - xiaoyufenfei/Efficient-Segmentati