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unet-master 2

于 2020-06-29 发布
0 213
下载积分: 1 下载次数: 4

代码说明:

说明:  使用unet对图像进行分割的源码,里面有训练集,可以根据自己的需要更换训练数据。(Use the source code of the image segmentation using UNET, which has a training set, you can change the training data according to your own needs.)

文件列表:

unet-master, 0 , 2020-06-24
unet-master\trainUnet.ipynb, 9916 , 2020-06-24
__MACOSX, 0 , 2020-06-29
__MACOSX\unet-master, 0 , 2020-06-29
__MACOSX\unet-master\._trainUnet.ipynb, 212 , 2020-06-24
unet-master\.DS_Store, 6148 , 2020-06-29
__MACOSX\unet-master\._.DS_Store, 120 , 2020-06-29
unet-master\dataPrepare.ipynb, 3831 , 2019-02-21
__MACOSX\unet-master\._dataPrepare.ipynb, 212 , 2019-02-21
unet-master\LICENSE, 1065 , 2019-02-21
__MACOSX\unet-master\._LICENSE, 212 , 2019-02-21
unet-master\Untitled.ipynb, 11919 , 2020-06-24
unet-master\__pycache__, 0 , 2020-06-24
unet-master\__pycache__\model.cpython-36.pyc, 2097 , 2020-06-24
unet-master\__pycache__\data.cpython-36.pyc, 3898 , 2020-06-24
unet-master\model.py, 3745 , 2019-02-21
__MACOSX\unet-master\._model.py, 212 , 2019-02-21
unet-master\README.md, 2552 , 2019-02-21
__MACOSX\unet-master\._README.md, 212 , 2019-02-21
unet-master\img, 0 , 2019-02-21
unet-master\img\0label.png, 178720 , 2019-02-21
__MACOSX\unet-master\img, 0 , 2020-06-29
__MACOSX\unet-master\img\._0label.png, 212 , 2019-02-21
unet-master\img\0test.png, 400739 , 2019-02-21
__MACOSX\unet-master\img\._0test.png, 212 , 2019-02-21
unet-master\img\u-net-architecture.png, 40580 , 2019-02-21
__MACOSX\unet-master\img\._u-net-architecture.png, 212 , 2019-02-21
__MACOSX\unet-master\._img, 212 , 2019-02-21
unet-master\.ipynb_checkpoints, 0 , 2020-06-24
unet-master\.ipynb_checkpoints\trainUnet-checkpoint.ipynb, 9802 , 2020-06-24
unet-master\.ipynb_checkpoints\Untitled-checkpoint.ipynb, 72 , 2020-06-24
unet-master\main.py, 821 , 2019-02-21
__MACOSX\unet-master\._main.py, 212 , 2019-02-21
unet-master\data, 0 , 2020-06-24
unet-master\data\.DS_Store, 6148 , 2020-06-29
__MACOSX\unet-master\data, 0 , 2020-06-29
__MACOSX\unet-master\data\._.DS_Store, 120 , 2020-06-29
unet-master\data\membrane, 0 , 2020-06-24
unet-master\data\membrane\.DS_Store, 8196 , 2020-06-29
__MACOSX\unet-master\data\membrane, 0 , 2020-06-29
__MACOSX\unet-master\data\membrane\._.DS_Store, 120 , 2020-06-29
unet-master\data\membrane\test, 0 , 2020-06-29
unet-master\data\membrane\test\.DS_Store, 6148 , 2020-06-29
__MACOSX\unet-master\data\membrane\test, 0 , 2020-06-29
__MACOSX\unet-master\data\membrane\test\._.DS_Store, 120 , 2020-06-29
unet-master\data\membrane\test\0_predict.png, 48695 , 2019-02-21
__MACOSX\unet-master\data\membrane\test\._0_predict.png, 212 , 2019-02-21
unet-master\data\membrane\test\1_predict.png, 54547 , 2019-02-21
__MACOSX\unet-master\data\membrane\test\._1_predict.png, 212 , 2019-02-21
unet-master\data\membrane\test\1.png, 213325 , 2019-02-21
__MACOSX\unet-master\data\membrane\test\._1.png, 212 , 2019-02-21
unet-master\data\membrane\test\0.png, 214932 , 2019-02-21
__MACOSX\unet-master\data\membrane\test\._0.png, 212 , 2019-02-21
__MACOSX\unet-master\data\membrane\._test, 212 , 2020-06-29
unet-master\data\membrane\test-volume.tif, 7871660 , 2019-02-21
__MACOSX\unet-master\data\membrane\._test-volume.tif, 212 , 2019-02-21
unet-master\data\membrane\train-volume.tif, 7870730 , 2019-02-21
__MACOSX\unet-master\data\membrane\._train-volume.tif, 212 , 2019-02-21
unet-master\data\membrane\train-labels.tif, 7869573 , 2019-02-21
__MACOSX\unet-master\data\membrane\._train-labels.tif, 212 , 2019-02-21
unet-master\data\membrane\train, 0 , 2020-06-24
unet-master\data\membrane\train\.DS_Store, 10244 , 2020-06-29
__MACOSX\unet-master\data\membrane\train, 0 , 2020-06-29
__MACOSX\unet-master\data\membrane\train\._.DS_Store, 120 , 2020-06-29
unet-master\data\membrane\train\aug, 0 , 2020-06-29
unet-master\data\membrane\train\aug\.DS_Store, 6148 , 2020-06-29
__MACOSX\unet-master\data\membrane\train\aug, 0 , 2020-06-29
__MACOSX\unet-master\data\membrane\train\aug\._.DS_Store, 120 , 2020-06-29
__MACOSX\unet-master\data\membrane\train\._aug, 212 , 2020-06-29
unet-master\data\membrane\train\label, 0 , 2020-06-29
unet-master\data\membrane\train\label\.DS_Store, 6148 , 2020-06-29
__MACOSX\unet-master\data\membrane\train\label, 0 , 2020-06-29
__MACOSX\unet-master\data\membrane\train\label\._.DS_Store, 120 , 2020-06-29
unet-master\data\membrane\train\label\4.png, 14312 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\label\._4.png, 212 , 2019-02-21
unet-master\data\membrane\train\label\2.png, 14052 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\label\._2.png, 212 , 2019-02-21
unet-master\data\membrane\train\label\3.png, 13829 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\label\._3.png, 212 , 2019-02-21
unet-master\data\membrane\train\label\1.png, 13977 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\label\._1.png, 212 , 2019-02-21
unet-master\data\membrane\train\label\0.png, 14322 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\label\._0.png, 212 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\._label, 212 , 2020-06-29
unet-master\data\membrane\train\image, 0 , 2020-06-29
unet-master\data\membrane\train\image\.DS_Store, 6148 , 2020-06-29
__MACOSX\unet-master\data\membrane\train\image, 0 , 2020-06-29
__MACOSX\unet-master\data\membrane\train\image\._.DS_Store, 120 , 2020-06-29
unet-master\data\membrane\train\image\4.png, 189054 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\image\._4.png, 212 , 2019-02-21
unet-master\data\membrane\train\image\2.png, 188971 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\image\._2.png, 212 , 2019-02-21
unet-master\data\membrane\train\image\3.png, 187963 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\image\._3.png, 212 , 2019-02-21
unet-master\data\membrane\train\image\1.png, 188189 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\image\._1.png, 212 , 2019-02-21
unet-master\data\membrane\train\image\0.png, 187651 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\image\._0.png, 212 , 2019-02-21
__MACOSX\unet-master\data\membrane\train\._image, 212 , 2020-06-29
__MACOSX\unet-master\data\membrane\._train, 212 , 2020-06-24

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发表评论

0 个回复

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    基于模拟退火的粒子群算法,主要是将模拟退火得概率问题用于粒子群,使其不易陷入局部最优(Simulated annealing particle swarm algorithm based on simulated annealing was mainly probability for PSO, it is not easy to fall into local optima)
    2020-11-21 11:09:37下载
    积分:1
  • enviread.m
    read envi headers images
    2020-10-20 14:17:25下载
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    经典的双树复小波分解包经典的双树复小波分解包经典的双树复小波分解包(a toolbox of dtcwta toolbox of dtcwta toolbox of dtcwt)
    2021-03-30 20:09:09下载
    积分:1
  • jubujunheng
    此代码从局部上来使用直方图均衡来达到增强图像的目的,非常的好,而且已经调试好了无差错(This code from the local authorities to use histogram equalization to achieve the purpose of enhanced images, very good, and has good debugging error-free)
    2008-05-17 09:34:33下载
    积分:1
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    该程序为一个小型的检索系统,通过提取颜色、边缘等特征进行图片检索。(The program is a small search system that retrieves images by extracting features such as colors and edges.)
    2018-05-23 19:23:13下载
    积分:1
  • pcnn
    脉冲耦合神经网络进行图像分割的matlab程序,利用PCNN点火捕获的相似性集群特性进行图像分割(Pulse Coupled Neural Net)
    2016-03-16 22:51:57下载
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    Matlab下,读取raw文件,实现多波段图像融合,高光谱与高分辨率图像融合,包括直接融合,傅里叶变换后融合,小波变换后融合及HIS变换后融合。(On Matlab,how to read and show raw files, achieve more band mergering,spectrum image and high resolution image fusiing,including direct fusion,Fourier transform fusion, wavelet transform fusion and HIS transformation fusion )
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