这是一个unet-pytorch的源码，可以训练自己的模型

Related tags

Deep Learningunet-pytorch
Overview

Unet：U-Net: Convolutional Networks for Biomedical Image Segmentation目标检测模型在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. 预测步骤 How2predict
  6. 训练步骤 How2train
  7. miou计算 miou
  8. 参考资料 Reference

性能情况

unet并不适合VOC此类数据集，其更适合特征少，需要浅层特征的医药数据集之类的。

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 unet_voc.pth VOC-Val12 512x512 55.11

所需环境

torch==1.2.0
torchvision==0.4.0

注意事项

unet_voc.pth是基于VOC拓展数据集训练的。
unet_medical.pth是使用示例的细胞分割数据集训练的。
在使用时需要注意区分。

文件下载

训练所需的unet_voc.pth和unet_medical.pth可在百度网盘中下载。
链接: https://pan.baidu.com/s/1AUBpqsSgamoQGEYpNjJg7A 提取码: i3ck

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

预测步骤

一、使用预训练权重

a、VOC预训练权重

  1. 下载完库后解压，如果想要利用voc训练好的权重进行预测，在百度网盘或者release下载unet_voc.pth，放入model_data，运行即可预测。
img/street.jpg
  1. 利用video.py可进行摄像头检测。

b、医药预训练权重

  1. 下载完库后解压，如果想要利用医药数据集训练好的权重进行预测，在百度网盘或者release下载unet_medical.pth，放入model_data，修改unet.py中的model_path和num_classes；
_defaults = {
    "model_path"        : 'model_data/unet_voc.pth',
    "model_image_size"  : (512, 512, 3),
    "num_classes"       : 21,
    "cuda"              : True,
    #--------------------------------#
    #   blend参数用于控制是否
    #   让识别结果和原图混合
    #--------------------------------#
    "blend"             : True
}
  1. 运行即可预测。
img/cell.png

二、使用自己训练的权重

  1. 按照训练步骤训练。
  2. 在unet.py文件里面，在如下部分修改model_path、backbone和num_classes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。
_defaults = {
    "model_path"        : 'model_data/unet_voc.pth',
    "model_image_size"  : (512, 512, 3),
    "num_classes"       : 21,
    "cuda"              : True,
    #--------------------------------#
    #   blend参数用于控制是否
    #   让识别结果和原图混合
    #--------------------------------#
    "blend"             : True
}
  1. 运行predict.py，输入
img/street.jpg
  1. 利用video.py可进行摄像头检测。

训练步骤

一、训练voc数据集

  1. 将我提供的voc数据集放入VOCdevkit中（无需运行voc2unet.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。

二、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voc2unet.py文件生成对应的txt。
  5. 注意修改train.py的num_classes为分类个数+1。
  6. 运行train.py即可开始训练。

三、训练医药数据集

  1. 下载VGG的预训练权重到model_data下面。
  2. 按照默认参数运行train_medical.py即可开始训练。

miou计算

参考miou计算视频和博客。

Reference

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

You might also like...
Comments
  • 询问一下预训练的问题

    询问一下预训练的问题

    你好，打扰了。我是想问下主干模型是指的是在下采样过程中使用的vgg吗？如果我不改变上采样是不是就不用使用imagenet训练。然后注销掉model_path=‘’ 以及 if model_path !=‘’这段。然后使用自己的数据集去进行训练。 谢谢大佬！！！！！！。实际上大佬你的voc的权重文件是不是为二次预训练的数据。 不好意思，语言表达能力不行。俺不晓得这样说大佬明不明白。

    opened by Nine9844 5
  • 训练一段时间后，CE loss变为NAN

    训练一段时间后，CE loss变为NAN

    您好，看了您的教程我试着自己搭建了一个U-Net模型，并采用Dice + CE loss作为损失函数，但在迭代几十个epoch后，我的CE loss返回了NAN值，反馈的结果是 ‘Function 'LogSoftmaxBackward' returned nan values in its 0th output.’ 同样的数据在您源码上运行没有出现这个问题，请问您是否知道些解决方法？

    opened by Breeze-Zero 2
  • 为啥在dataloader第40行转换的array的shape和cv2不一样呢

    为啥在dataloader第40行转换的array的shape和cv2不一样呢

    我使用json_to_dataset.py转化mask后尝试使用代码查看shape import cv2 import numpy as np from PIL import Image

    file = '/home/fut/Downloads/unet-pytorch-main/mydata/masks/ID_1110_json.png' img = cv2.imread(file, cv2.IMREAD_UNCHANGED) print(img.shape)

    pil = Image.open(file) img2 = np.array(pil) print(img2.shape) 结果会是： (800, 800, 3) (800, 800) 为什么PIL读取后通道就没了，正是因为这个原因你的项目会很好跑起来。

    opened by futureflsl 1
  • from tqdm import tqdm 报错

    from tqdm import tqdm 报错

    import os import time

    import numpy as np import torch import torch.backends.cudnn as cudnn import torch.optim as optim from torch.utils.data import DataLoader from tqdm import tqdm

    opened by Luke-Wei 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)

  • v2.2(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unet.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callback.py文件，防止多线程错误。
    • 更新summary.py文件，用于观看网络结构。
    Source code(tar.gz)
    Source code(zip)

  • v1.0(Mar 12, 2021)

Owner
Bubbliiiing
Bubbliiiing
GitHub Repository
Measures input lag without dedicated hardware, performing motion detection on recorded or live video

What is InputLagTimer? This tool can measure input lag by analyzing a video where both the game controller and the game screen can be seen on a webcam

Bruno Gonzalez 4 Aug 18, 2022
Official implementation of CATs: Cost Aggregation Transformers for Visual Correspondence NeurIPS'21

CATs: Cost Aggregation Transformers for Visual Correspondence NeurIPS'21 For more information, check out the paper on [arXiv]. Training with different

Sunghwan Hong 120 Jan 04, 2023
A series of Jupyter notebooks with Chinese comment that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

Hands-on-Machine-Learning 目的 这份笔记旨在帮助中文学习者以一种较快较系统的方式入门机器学习， 是在学习Hands-on Machine Learning with Scikit-Learn and TensorFlow这本书的 时候做的个人笔记: 此项目的可取之处 原书的

Baymax 1.5k Dec 21, 2022
Yolo algorithm for detection + centroid tracker to track vehicles

Vehicle Tracking using Centroid tracker Algorithm used : Yolo algorithm for detection + centroid tracker to track vehicles Backend : opencv and python

6 Dec 21, 2022
A Traffic Sign Recognition Project which can help the driver recognise the signs via text as well as audio. Can be used at Night also.

Traffic-Sign-Recognition In this report, we propose a Convolutional Neural Network(CNN) for traffic sign classification that achieves outstanding perf

Mini Project 64 Nov 19, 2022
Generalized hybrid model for mode-locked laser diodes with an extended passive cavity

GenHybridMLLmodel Generalized hybrid model for mode-locked laser diodes with an extended passive cavity This hybrid simulation strategy combines a tra

Stijn Cuyvers 3 Sep 21, 2022
MOOSE (Multi-organ objective segmentation) a data-centric AI solution that generates multilabel organ segmentations to facilitate systemic TB whole-person research

MOOSE (Multi-organ objective segmentation) a data-centric AI solution that generates multilabel organ segmentations to facilitate systemic TB whole-person research.The pipeline is based on nn-UNet an

QIMP team 30 Jan 01, 2023
Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included.

pixel_character_generator Generating retro pixel game characters with Generative Adversarial Networks. Dataset "TinyHero" included. Dataset TinyHero D

Agnieszka Mikołajczyk 88 Nov 17, 2022
Qt-GUI implementation of the YOLOv5 algorithm (ver.6 and ver.5)

YOLOv5-GUI 🎉 YOLOv5算法(ver.6及ver.5)的Qt-GUI实现 🎉 Qt-GUI implementation of the YOLOv5 algorithm (ver.6 and ver.5). 基于YOLOv5的v5版本和v6版本及Javacr大佬的UI逻辑进行编写

EricFang 12 Dec 28, 2022
U^2-Net - Portrait matting This repository explores possibilities of using the original u^2-net model for portrait matting.

U^2-Net - Portrait matting This repository explores possibilities of using the original u^2-net model for portrait matting.

Dennis Bappert 104 Nov 25, 2022
Pure python PEMDAS expression solver without using built-in eval function

pypemdas Pure python PEMDAS expression solver without using built-in eval function. Supports nested parenthesis. Supported operators: + - * / ^ Exampl

1 Dec 22, 2021
We propose a new method for effective shadow removal by regarding it as an exposure fusion problem.

Auto-exposure fusion for single-image shadow removal We propose a new method for effective shadow removal by regarding it as an exposure fusion proble

Qing Guo 146 Dec 31, 2022
Next-gen Rowhammer fuzzer that uses non-uniform, frequency-based patterns.

Blacksmith Rowhammer Fuzzer This repository provides the code accompanying the paper Blacksmith: Scalable Rowhammering in the Frequency Domain that is

Computer Security Group @ ETH Zurich 173 Nov 16, 2022
Scientific Computation Methods in C and Python (Open for Hacktoberfest 2021)

Sci - cpy README is a stub. Do expand it. Objective This repository is meant to be a ready reference for scientific computation methods. Do ⭐ it if yo

Sandip Dutta 7 Oct 12, 2022
Fine-grained Control of Image Caption Generation with Abstract Scene Graphs

Faster R-CNN pretrained on VisualGenome This repository modifies maskrcnn-benchmark for object detection and attribute prediction on VisualGenome data

Shizhe Chen 7 Apr 20, 2021
4th place solution to datafactory challenge by Intermarché.

Solution to Datafactory challenge by Intermarché. 4th place solution to datafactory challenge by Intermarché. The objective of the challenge is to pre

Raphael Sourty 11 Mar 19, 2022
PyTorch Implementation for "ForkGAN with SIngle Rainy NIght Images: Leveraging the RumiGAN to See into the Rainy Night"

ForkGAN with Single Rainy Night Images: Leveraging the RumiGAN to See into the Rainy Night By Seri Lee, Department of Engineering, Seoul National Univ

Seri Lee 52 Oct 12, 2022
Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation

NeuralPDE NeuralPDE.jl is a solver package which consists of neural network solvers for partial differential equations using scientific machine learni

SciML Open Source Scientific Machine Learning 680 Jan 02, 2023
unofficial pytorch implementation of RefineGAN

RefineGAN unofficial pytorch implementation of RefineGAN (https://arxiv.org/abs/1709.00753) for CSMRI reconstruction, the official code using tensorpa

xinby17 5 Jul 21, 2022
Direct LiDAR Odometry: Fast Localization with Dense Point Clouds

Direct LiDAR Odometry: Fast Localization with Dense Point Clouds DLO is a lightweight and computationally-efficient frontend LiDAR odometry solution w

VECTR at UCLA 369 Dec 30, 2022