这是一个deeplabv3-plus-pytorch的源码，可以用于训练自己的模型。

Related tags

Deep Learningdeeplabv3-plus-pytorch
Overview

DeepLabv3+：Encoder-Decoder with Atrous Separable Convolution语义分割模型在Pytorch当中的实现

目录

  1. 性能情况 Performance
  2. 所需环境 Environment
  3. 注意事项 Attention
  4. 文件下载 Download
  5. 训练步骤 How2train
  6. 预测步骤 How2predict
  7. 评估步骤 miou
  8. 参考资料 Reference

性能情况

训练数据集 权值文件名称 测试数据集 输入图片大小 mIOU
VOC12+SBD deeplab_mobilenetv2.pth VOC-Val12 512x512 72.59
VOC12+SBD deeplab_xception.pth VOC-Val12 512x512 76.95

所需环境

torch==1.2.0

注意事项

代码中的deeplab_mobilenetv2.pth和deeplab_xception.pth是基于VOC拓展数据集训练的。训练和预测时注意修改backbone。

文件下载

训练所需的deeplab_mobilenetv2.pth和deeplab_xception.pth可在百度网盘中下载。
链接: https://pan.baidu.com/s/1KgLMbprQshlcpKgug9ECFg 提取码: 4ir8

VOC拓展数据集的百度网盘如下：
链接: https://pan.baidu.com/s/1BrR7AUM1XJvPWjKMIy2uEw 提取码: vszf

训练步骤

a、训练voc数据集

1、将我提供的voc数据集放入VOCdevkit中（无需运行voc_annotation.py）。
2、在train.py中设置对应参数，默认参数已经对应voc数据集所需要的参数了，所以只要修改backbone和model_path即可。
3、运行train.py进行训练。

b、训练自己的数据集

1、本文使用VOC格式进行训练。
2、训练前将标签文件放在VOCdevkit文件夹下的VOC2007文件夹下的SegmentationClass中。
3、训练前将图片文件放在VOCdevkit文件夹下的VOC2007文件夹下的JPEGImages中。
4、在训练前利用voc_annotation.py文件生成对应的txt。
5、在train.py文件夹下面，选择自己要使用的主干模型和下采样因子。本文提供的主干模型有mobilenet和xception。下采样因子可以在8和16中选择。需要注意的是，预训练模型需要和主干模型相对应。
6、注意修改train.py的num_classes为分类个数+1。
7、运行train.py即可开始训练。

预测步骤

a、使用预训练权重

1、下载完库后解压，如果想用backbone为mobilenet的进行预测，直接运行predict.py就可以了；如果想要利用backbone为xception的进行预测，在百度网盘下载deeplab_xception.pth，放入model_data，修改deeplab.py的backbone和model_path之后再运行predict.py，输入。

img/street.jpg

可完成预测。
2、在predict.py里面进行设置可以进行fps测试、整个文件夹的测试和video视频检测。

b、使用自己训练的权重

1、按照训练步骤训练。
2、在deeplab.py文件里面，在如下部分修改model_path、num_classes、backbone使其对应训练好的文件；model_path对应logs文件夹下面的权值文件，num_classes代表要预测的类的数量加1，backbone是所使用的主干特征提取网络。

_defaults = {
    #----------------------------------------#
    #   model_path指向logs文件夹下的权值文件
    #----------------------------------------#
    "model_path"        : 'model_data/deeplab_mobilenetv2.pth',
    #----------------------------------------#
    #   所需要区分的类的个数+1
    #----------------------------------------#
    "num_classes"       : 21,
    #----------------------------------------#
    #   所使用的的主干网络
    #----------------------------------------#
    "backbone"          : "mobilenet",
    #----------------------------------------#
    #   输入图片的大小
    #----------------------------------------#
    "input_shape"       : [512, 512],
    #----------------------------------------#
    #   下采样的倍数，一般可选的为8和16
    #   与训练时设置的一样即可
    #----------------------------------------#
    "downsample_factor" : 16,
    #--------------------------------#
    #   blend参数用于控制是否
    #   让识别结果和原图混合
    #--------------------------------#
    "blend"             : True,
    #-------------------------------#
    #   是否使用Cuda
    #   没有GPU可以设置成False
    #-------------------------------#
    "cuda"              : True,
}

3、运行predict.py，输入

img/street.jpg

可完成预测。
4、在predict.py里面进行设置可以进行fps测试、整个文件夹的测试和video视频检测。

评估步骤

1、设置get_miou.py里面的num_classes为预测的类的数量加1。
2、设置get_miou.py里面的name_classes为需要去区分的类别。
3、运行get_miou.py即可获得miou大小。

Reference

https://github.com/ggyyzm/pytorch_segmentation
https://github.com/bonlime/keras-deeplab-v3-plus

You might also like...
这是一个yolo3-tf2的源码，可以用于训练自己的模型。

YOLOV3：You Only Look Once目标检测模型在Tensorflow2当中的实现 目录 性能情况 Performance 所需环境 Environment 文件下载 Download 训练步骤 How2train 预测步骤 How2predict 评估步骤 How2eval 参考资料

Bubbliiiing 68 Dec 21, 2022
这是一个yolox-keras的源码，可以用于训练自己的模型。

YOLOX：You Only Look Once目标检测模型在Keras当中的实现 目录 性能情况 Performance 实现的内容 Achievement 所需环境 Environment 小技巧的设置 TricksSet 文件下载 Download 训练步骤 How2train 预测步骤 Ho

Bubbliiiing 64 Nov 10, 2022
A cross platform package to do curses-like operations, plus higher level APIs and widgets to create text UIs and ASCII art animations
A cross platform package to do curses-like operations, plus higher level APIs and widgets to create text UIs and ASCII art animations

ASCIIMATICS Asciimatics is a package to help people create full-screen text UIs (from interactive forms to ASCII animations) on any platform. It is li

null 3.2k Jan 9, 2023
CUDA integration for Python, plus shiny features

PyCUDA lets you access Nvidia's CUDA parallel computation API from Python. Several wrappers of the CUDA API already exist-so what's so special about P

Andreas Klöckner 1.4k Jan 2, 2023
It is a personal assistant chatbot, capable to perform many tasks same as Google Assistant plus more extra features...
It is a personal assistant chatbot, capable to perform many tasks same as Google Assistant plus more extra features...

PersonalAssistant It is an Personal Assistant, capable to perform many tasks with some unique features, that you haven'e seen yet.... Features / Tasks

Roshan Kumar 95 Dec 21, 2022
A3C LSTM Atari with Pytorch plus A3G design
A3C LSTM Atari with Pytorch plus A3G design

NEWLY ADDED A3G A NEW GPU/CPU ARCHITECTURE OF A3C FOR SUBSTANTIALLY ACCELERATED TRAINING!! RL A3C Pytorch NEWLY ADDED A3G!! New implementation of A3C

David Griffis 532 Jan 2, 2023
Face Recognition plus identification simply and fast | Python
Face Recognition plus identification simply and fast | Python

PyFaceDetection Face Recognition plus identification simply and fast Ubuntu Setup sudo pip3 install numpy sudo pip3 install cmake sudo pip3 install dl

Peyman Majidi Moein 16 Sep 22, 2022
Proof of Concept Exploit for ManageEngine ServiceDesk Plus CVE-2021-44077
Proof of Concept Exploit for ManageEngine ServiceDesk Plus CVE-2021-44077

CVE-2021-44077 Proof of Concept Exploit for CVE-2021-44077: PreAuth RCE in ManageEngine ServiceDesk Plus 11306 Based on: https://xz.aliyun.com/t/106

Horizon 3 AI Inc 25 Nov 9, 2022
Enigma-Plus - Python based Enigma machine simulator with some extra features

Enigma-Plus Python based Enigma machine simulator with some extra features Examp

null 1 Jan 5, 2022
Plone Interface contracts, plus basic features and utilities

plone.base This package is the base package of the CMS Plone https://plone.org. It contains only interface contracts and basic features and utilitie

Plone Foundation 1 Oct 3, 2022
Strawberry-django-plus - Enhanced Strawberry GraphQL integration with Django

strawberry-django-plus Enhanced Strawberry integration with Django. Built on top

BLB Ventures 138 Dec 28, 2022
Comments
  • 输出坐标

    输出坐标

    image if self.mix_type == 0: seg_img = np.zeros((np.shape(pr)[0], np.shape(pr)[1], 3)) for c in range(self.num_classes): seg_img[:, :, 0] += ((pr[:, :] == c ) * self.colors[c][0]).astype('uint8') seg_img[:, :, 1] += ((pr[:, :] == c ) * self.colors[c][1]).astype('uint8') seg_img[:, :, 2] += ((pr[:, :] == c ) * self.colors[c][2]).astype('uint8')

    请问如果我想得到这个猫的左上方坐标和右下方坐标该怎么print呢

    opened by SSTato 3
  • TF/Keras 版和 PyTorch 版的性能差异

    TF/Keras 版和 PyTorch 版的性能差异

    首先感谢提供多种版本的代码。 我注意到同样是VOC12数据集，deeplabv3-plus-tf2/deeplabv3-plus-keras在测试集上的结果显著优于deeplabv3-plus-pytorch版本。 是否因为前者使用的是dice_loss_with_CE而后者只使用DiceLoss进行训练？还是有其它别的原因？

    opened by fyang93 3
  • 关于替换为我自己的数据集训练的问题

    关于替换为我自己的数据集训练的问题

    我替换为自己的数据集进行训练，但是一直遇到ValueError: Expected more than 1 value per channel when training, got input size torch.Size([1, 256, 1, 1])，这个报错，百度了都说是batch_size可能多出来了，把drop_last设置为true就好了。可是我看你的代码里是设置的true啊，请问up怎么解决呢，我这边cuda：10.1 pytorch：1.2.0

    opened by Codingaworld 1
  • 模型输出问题

    模型输出问题

    老师好，想请问一下，默认导出onnx之后，模型输出是三通道的RGB图像，怎么将输出改为0或1的标签呀，比如我就对两个类进行分割（背景和目标），然后我希望输出二值图（背景为0，目标为1），就好像训练的数据集的标签一样 模型转onnx格式之后，输出是3通道的float图，仔细观察后通道1和通道2都有点奇怪，通道3是我想要的（需要格式转换），怎么控制网络直接输出灰度标签呢

    opened by YuriGao 4
Releases(v3.0)
Owner
Bubbliiiing
Bubbliiiing
GitHub Repository
Unofficial PyTorch implementation of MobileViT based on paper "MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer".

MobileViT RegNet Unofficial PyTorch implementation of MobileViT based on paper MOBILEVIT: LIGHT-WEIGHT, GENERAL-PURPOSE, AND MOBILE-FRIENDLY VISION TR

Hong-Jia Chen 91 Dec 02, 2022
The repository contains reproducible PyTorch source code of our paper Generative Modeling with Optimal Transport Maps, ICLR 2022.

Generative Modeling with Optimal Transport Maps The repository contains reproducible PyTorch source code of our paper Generative Modeling with Optimal

Litu Rout 30 Dec 22, 2022
Official implementation of Deep Convolutional Dictionary Learning for Image Denoising.

DCDicL for Image Denoising Hongyi Zheng*, Hongwei Yong*, Lei Zhang, "Deep Convolutional Dictionary Learning for Image Denoising," in CVPR 2021. (* Equ

Z80 91 Dec 21, 2022
Modifications of the official PyTorch implementation of StyleGAN3. Let's easily generate images and videos with StyleGAN2/2-ADA/3!

Alias-Free Generative Adversarial Networks (StyleGAN3) Official PyTorch implementation of the NeurIPS 2021 paper Alias-Free Generative Adversarial Net

Diego Porres 185 Dec 24, 2022
Object DGCNN and DETR3D, Our implementations are built on top of MMdetection3D.

Object DGCNN & DETR3D This repo contains the implementations of Object DGCNN (https://arxiv.org/abs/2110.06923) and DETR3D (https://arxiv.org/abs/2110

Wang, Yue 539 Jan 07, 2023
deep learning model that learns to code with drawing in the Processing language

sketchnet sketchnet - processing code generator can we teach a computer to draw pictures with code. We use Processing and java/jruby code paired with

41 Dec 12, 2022
CV backbones including GhostNet, TinyNet and TNT, developed by Huawei Noah's Ark Lab.

CV Backbones including GhostNet, TinyNet, TNT (Transformer in Transformer) developed by Huawei Noah's Ark Lab. GhostNet Code TinyNet Code TNT Code Pyr

HUAWEI Noah's Ark Lab 3k Jan 08, 2023
Multivariate Time Series Forecasting with efficient Transformers. Code for the paper "Long-Range Transformers for Dynamic Spatiotemporal Forecasting."

Spacetimeformer Multivariate Forecasting This repository contains the code for the paper, "Long-Range Transformers for Dynamic Spatiotemporal Forecast

QData 440 Jan 02, 2023
The sixth place winning solution (6/220) in 2021 Gaofen Challenge.

SwinTransformer + OBBDet The sixth place winning solution (6/220) in the track of Fine-grained Object Recognition in High-Resolution Optical Images, 2

ming71 46 Dec 02, 2022
Implementation of Hierarchical Transformer Memory (HTM) for Pytorch

Hierarchical Transformer Memory (HTM) - Pytorch Implementation of Hierarchical Transformer Memory (HTM) for Pytorch. This Deepmind paper proposes a si

Phil Wang 63 Dec 29, 2022
Learning Saliency Propagation for Semi-supervised Instance Segmentation

Learning Saliency Propagation for Semi-supervised Instance Segmentation PyTorch Implementation This repository contains: the PyTorch implementation of

Berkeley DeepDrive 68 Oct 18, 2022
UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model

UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model Official repository for the ICCV 2021 paper: UltraPose: Syn

MomoAILab 92 Dec 21, 2022
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

NNI Doc | 简体中文 NNI (Neural Network Intelligence) is a lightweight but powerful toolkit to help users automate Feature Engineering, Neural Architecture

Microsoft 12.4k Dec 31, 2022
Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment"

DSN-IQA Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment" Requirements Python =3.8.0 Pytorch =1.7.1 Usage wit

7 Oct 13, 2022
Official PyTorch implementation of the paper "Graph-based Generative Face Anonymisation with Pose Preservation" in ICIAP 2021

Contents AnonyGAN Installation Dataset Preparation Generating Images Using Pretrained Model Train and Test New Models Evaluation Acknowledgments Citat

Nicola Dall'Asen 10 May 24, 2022
This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of Coordinate Independent Convolutional Networks.

Orientation independent Möbius CNNs This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of

Maurice Weiler 59 Dec 09, 2022
YoHa - A practical hand tracking engine.

YoHa - A practical hand tracking engine.

2k Jan 06, 2023
Face Mask Detection is a project to determine whether someone is wearing mask or not, using deep neural network.

face-mask-detection Face Mask Detection is a project to determine whether someone is wearing mask or not, using deep neural network. It contains 3 scr

amirsalar 13 Jan 18, 2022
This is the solution for 2nd rank in Kaggle competition: Feedback Prize - Evaluating Student Writing.

Feedback Prize - Evaluating Student Writing This is the solution for 2nd rank in Kaggle competition: Feedback Prize - Evaluating Student Writing. The

Udbhav Bamba 41 Dec 14, 2022
Chainer Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

fcn - Fully Convolutional Networks Chainer implementation of Fully Convolutional Networks. Installation pip install fcn Inference Inference is done as

Kentaro Wada 218 Oct 27, 2022