DeepLabv3+：Encoder-Decoder with Atrous Separable Convolution语义分割模型在tensorflow2当中的实现

Related tags

Deep Learningdeeplabv3-plus-tf2
Overview

DeepLabv3+：Encoder-Decoder with Atrous Separable Convolution语义分割模型在tensorflow2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 deeplabv3_mobilenetv2.h5 VOC-Val12 512x512 72.50
VOC12+SBD deeplabv3_xception.h5 VOC-Val12 512x512 87.10

所需环境

tensorflow==2.2.0

注意事项

代码中的deeplabv3_mobilenetv2.h5和deeplabv3_xception.h5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deeplabv3_mobilenetv2.h5和deeplabv3_xception.h5可在百度网盘中下载。
链接: https://pan.baidu.com/s/1zVRshWRkb5C3kmDMwEf89A 提取码: ccq5

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.h5，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/deeplabv3_mobilenetv2.h5',
    #----------------------------------------#
    #   所需要区分的类的个数+1
    #----------------------------------------#
    "num_classes"       : 21,
    #----------------------------------------#
    #   所使用的的主干网络：mobilenet、xception    
    #----------------------------------------#
    "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,
}

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...
Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning (ICLR 2021)

Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning (ICLR 2021) Citation Please cite as: @inproceedings{liu2020understan

Sunbow Liu 22 Nov 25, 2022
《LXMERT: Learning Cross-Modality Encoder Representations from Transformers》(EMNLP 2020)

The Most Important Thing. Our code is developed based on: LXMERT: Learning Cross-Modality Encoder Representations from Transformers

null 53 Dec 16, 2022
Official implementation for Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020

Likelihood-Regret Official implementation of Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020. T

Xavier 33 Oct 12, 2022
Official Implementation for
Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" https://arxiv.org/abs/2104.02699

ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement Recently, the power of unconditional image synthesis has significantly advanced th

null 967 Jan 4, 2023
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Phil Wang 12.6k Jan 9, 2023
A Joint Video and Image Encoder for End-to-End Retrieval
A Joint Video and Image Encoder for End-to-End Retrieval

Frozen️ in Time ❄️ ️️️️ ⏳ A Joint Video and Image Encoder for End-to-End Retrieval project page | arXiv | webvid-data Repository containing the code,

null 225 Dec 25, 2022
PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.
PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.

VAENAR-TTS - PyTorch Implementation PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.

Keon Lee 67 Nov 14, 2022
Code for the paper
Code for the paper "Adversarial Generator-Encoder Networks"

This repository contains code for the paper "Adversarial Generator-Encoder Networks" (AAAI'18) by Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky. Pr

Dmitry Ulyanov 279 Jun 26, 2022
PyTorch implementation of SQN based on CloserLook3D's encoder

SQN_pytorch This repo is an implementation of Semantic Query Network (SQN) using CloserLook3D's encoder in Pytorch. For TensorFlow implementation, che

PointCloudYC 1 Oct 21, 2021
Comments
  • How to reproduce the model deeplabv3_xception.h5?

    How to reproduce the model deeplabv3_xception.h5?

    Hi, I'm trying to train deeplabv3 with xception backbone on voc + SBD dataset. You provided the voc pretrained model deeplabv3_xception.h5. But If I want to reproduce your training result, I should not use it as pretrained model, right? So I comment out the line in train.py to not loading pretrained weights. But after 100 epochs, my model accuracy is poor, compared with your model. Did I miss something, do I need something like an ImageNet pretrained model or COCO pretrained model? Thanks!

    opened by zhimengf 6
  • How to export a .pb file

    How to export a .pb file

    Hi, I have problem exporting a .pb file with the current produced .h5 files. I do not think you have provided the export method in the project file. Could you please give me some advice on that? @bubbliiiing

    opened by 77knight 1
Releases(v3.0)

  • v3.0(Apr 22, 2022)

    重要更新

    • 支持step、cos学习率下降法。
    • 支持adam、sgd优化器选择。
    • 支持学习率根据batch_size自适应调整。
    • 支持不同预测模式的选择，单张图片预测、文件夹预测、视频预测、图片裁剪。
    • 更新summary.py文件，用于观看网络结构。
    • 增加了多GPU训练。
    Source code(tar.gz)
    Source code(zip)

  • v2.0(Mar 4, 2022)

    重要更新

    • 更新train.py文件，增加了大量的注释，增加多个可调整参数。
    • 更新predict.py文件，增加了大量的注释，增加fps、视频预测、批量预测等功能。
    • 更新deeplab.py文件，增加了大量的注释，增加先验框选择、置信度、非极大抑制等参数。
    • 合并get_dr_txt.py、get_gt_txt.py和get_map.py文件，通过一个文件来实现数据集的评估。
    • 更新voc_annotation.py文件，增加多个可调整参数。
    • 更新summary.py文件，用于观看网络结构。
    Source code(tar.gz)
    Source code(zip)

  • v1.0(Sep 9, 2021)

Owner
Bubbliiiing
Bubbliiiing
GitHub Repository
A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-t

Facebook Research 5.1k Jan 04, 2023
Source code for From Stars to Subgraphs

GNNAsKernel Official code for From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness Visualizations GNN-AK(+) GNN-AK(+) with Subgra

44 Dec 19, 2022
Spline is a tool that is capable of running locally as well as part of well known pipelines like Jenkins (Jenkinsfile), Travis CI (.travis.yml) or similar ones.

Welcome to spline - the pipeline tool Important note: Since change in my job I didn't had the chance to continue on this project. My main new project

Thomas Lehmann 29 Aug 22, 2022
Benchmarks for Model-Based Optimization

Design-Bench Design-Bench is a benchmarking framework for solving automatic design problems that involve choosing an input that maximizes a black-box

Brandon Trabucco 43 Dec 20, 2022
iNAS: Integral NAS for Device-Aware Salient Object Detection

iNAS: Integral NAS for Device-Aware Salient Object Detection Introduction Integral search design (jointly consider backbone/head structures, design/de

顾宇超 77 Dec 02, 2022
Fast, differentiable sorting and ranking in PyTorch

Torchsort Fast, differentiable sorting and ranking in PyTorch. Pure PyTorch implementation of Fast Differentiable Sorting and Ranking (Blondel et al.)

Teddy Koker 655 Jan 04, 2023
NNR conformation conditional and global probabilities estimation and analysis in peptides or proteins fragments

NNR and global probabilities estimation and analysis in peptides or protein fragments This module calculates global and NNR conformation dependent pro

0 Jul 15, 2021
Learning to See by Looking at Noise

Learning to See by Looking at Noise This is the official implementation of Learning to See by Looking at Noise. In this work, we investigate a suite o

Manel Baradad Jurjo 82 Dec 24, 2022
Logistic Bandit experiments. Official code for the paper "Jointly Efficient and Optimal Algorithms for Logistic Bandits".

Code for the paper Jointly Efficient and Optimal Algorithms for Logistic Bandits, by Louis Faury, Marc Abeille, Clément Calauzènes and Kwang-Sun Jun.

Faury Louis 1 Jan 22, 2022
Python implementation of cover trees, near-drop-in replacement for scipy.spatial.kdtree

This is a Python implementation of cover trees, a data structure for finding nearest neighbors in a general metric space (e.g., a 3D box with periodic

Patrick Varilly 28 Nov 25, 2022
unet for image segmentation

Implementation of deep learning framework -- Unet, using Keras The architecture was inspired by U-Net: Convolutional Networks for Biomedical Image Seg

zhixuhao 4.1k Dec 31, 2022
This is a simple face recognition mini project that was completed by a team of 3 members in 1 week's time

PeekingDuckling 1. Description This is an implementation of facial identification algorithm to detect and identify the faces of the 3 team members Cla

Eric Kwok 2 Jan 25, 2022
Weakly Supervised Learning of Rigid 3D Scene Flow

Weakly Supervised Learning of Rigid 3D Scene Flow This repository provides code and data to train and evaluate a weakly supervised method for rigid 3D

Zan Gojcic 124 Dec 27, 2022
Code for 'Self-Guided and Cross-Guided Learning for Few-shot segmentation. (CVPR' 2021)'

SCL Introduction Code for 'Self-Guided and Cross-Guided Learning for Few-shot segmentation. (CVPR' 2021)' We evaluated our approach using two baseline

34 Oct 08, 2022
Codebase for ECCV18 "The Sound of Pixels"

Sound-of-Pixels Codebase for ECCV18 "The Sound of Pixels". *This repository is under construction, but the core parts are already there. Environment T

Hang Zhao 318 Dec 20, 2022
A collection of IPython notebooks covering various topics.

ipython-notebooks This repo contains various IPython notebooks I've created to experiment with libraries and work through exercises, and explore subje

John Wittenauer 2.6k Jan 01, 2023
Memory Efficient Attention (O(sqrt(n)) for Jax and PyTorch

Memory Efficient Attention This is unofficial implementation of Self-attention Does Not Need O(n^2) Memory for Jax and PyTorch. Implementation is almo

Amin Rezaei 126 Dec 27, 2022
使用yolov5训练自己数据集(详细过程)并通过flask部署

使用yolov5训练自己的数据集（详细过程）并通过flask部署 依赖库 torch torchvision numpy opencv-python lxml tqdm flask pillow tensorboard matplotlib pycocotools Windows，请使用 pycoc

HB.com 19 Dec 28, 2022
PyTorch implementation of "Image-to-Image Translation Using Conditional Adversarial Networks".

pix2pix-pytorch PyTorch implementation of Image-to-Image Translation Using Conditional Adversarial Networks. Based on pix2pix by Phillip Isola et al.

mrzhu 383 Dec 17, 2022
ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts

ANEA The goal of Automatic (Named) Entity Annotation is to create a small annotated dataset for NER extracted from German domain-specific texts. Insta

Anastasia Zhukova 2 Oct 07, 2022