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
Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes

Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized C

Sam Bond-Taylor 139 Jan 04, 2023
Breaking the Dilemma of Medical Image-to-image Translation

Breaking the Dilemma of Medical Image-to-image Translation Supervised Pix2Pix and unsupervised Cycle-consistency are two modes that dominate the field

Kid Liet 86 Dec 21, 2022
g9.py - Torch interactive graphics

g9.py - Torch interactive graphics A Torch toy in the browser. Demo at https://srush.github.io/g9py/ This is a shameless copy of g9.js, written in Pyt

Sasha Rush 13 Nov 16, 2022
A repo that contains all the mesh keys needed for mesh backend, along with a code example of how to use them in python

Mesh-Keys A repo that contains all the mesh keys needed for mesh backend, along with a code example of how to use them in python Have been seeing alot

Joseph 53 Dec 13, 2022
Official code of the paper "ReDet: A Rotation-equivariant Detector for Aerial Object Detection" (CVPR 2021)

ReDet: A Rotation-equivariant Detector for Aerial Object Detection ReDet: A Rotation-equivariant Detector for Aerial Object Detection (CVPR2021), Jiam

csuhan 334 Dec 23, 2022
Given a 2D triangle mesh, we could randomly generate cloud points that fill in the triangle mesh

generate_cloud_points Given a 2D triangle mesh, we could randomly generate cloud points that fill in the triangle mesh. Run python disp_mesh.py Or you

Peng Yu 2 Dec 24, 2021
Small-bets - Ergodic Experiment With Python

Ergodic Experiment Based on this video. Run this experiment with this command: p

Michael Brant 3 Jan 11, 2022
This is an official implementation for "AS-MLP: An Axial Shifted MLP Architecture for Vision".

AS-MLP architecture for Image Classification Model Zoo Image Classification on ImageNet-1K Network Resolution Top-1 (%) Params FLOPs Throughput (image

SVIP Lab 106 Dec 12, 2022
PyTorch implementation for ComboGAN

ComboGAN This is our ongoing PyTorch implementation for ComboGAN. Code was written by Asha Anoosheh (built upon CycleGAN) [ComboGAN Paper] If you use

Asha Anoosheh 139 Dec 20, 2022
Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 130+ Indicators

Pandas TA - A Technical Analysis Library in Python 3 Pandas Technical Analysis (Pandas TA) is an easy to use library that leverages the Pandas package

Kevin Johnson 3.2k Jan 09, 2023
A Python package for causal inference using Synthetic Controls

Synthetic Control Methods A Python package for causal inference using synthetic controls This Python package implements a class of approaches to estim

Oscar Engelbrektson 107 Dec 28, 2022
A multi-entity Transformer for multi-agent spatiotemporal modeling.

baller2vec This is the repository for the paper: Michael A. Alcorn and Anh Nguyen. baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotempor

Michael A. Alcorn 56 Nov 15, 2022
Recurrent Neural Network Tutorial, Part 2 - Implementing a RNN in Python and Theano

Please read the blog post that goes with this code! Jupyter Notebook Setup System Requirements: Python, pip (Optional) virtualenv To start the Jupyter

Denny Britz 863 Dec 15, 2022
Implement object segmentation on images using HOG algorithm proposed in CVPR 2005

HOG Algorithm Implementation Description HOG (Histograms of Oriented Gradients) Algorithm is an algorithm aiming to realize object segmentation (edge

Leo Hsieh 2 Mar 12, 2022
Your interactive network visualizing dashboard

Your interactive network visualizing dashboard Documentation: Here What is Jaal Jaal is a python based interactive network visualizing tool built usin

Mohit 177 Jan 04, 2023
ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs This is the code of paper ConE: Cone Embeddings for Multi-Hop Reasoning over Knowl

MIRA Lab 33 Dec 07, 2022
"NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search".

NAS-Bench-301 This repository containts code for the paper: "NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search". The

AutoML-Freiburg-Hannover 57 Nov 30, 2022
Keeper for Ricochet Protocol, implemented with Apache Airflow

Ricochet Keeper This repository contains Apache Airflow DAGs for executing keeper operations for Ricochet Exchange. Usage You will need to run this us

Ricochet Exchange 5 May 24, 2022
A human-readable PyTorch implementation of "Self-attention Does Not Need O(n^2) Memory"

memory_efficient_attention.pytorch A human-readable PyTorch implementation of "Self-attention Does Not Need O(n^2) Memory" (Rabe&Staats'21). def effic

Ryuichiro Hataya 7 Dec 26, 2022
A Strong Baseline for Image Semantic Segmentation

A Strong Baseline for Image Semantic Segmentation Introduction This project is an open source semantic segmentation toolbox based on PyTorch. It is ba

Clark He 49 Sep 20, 2022