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cnn-compression-master

于 2020-12-08 发布
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代码说明：

说明：  compression压缩感知算法用于图像处理。信号压缩(compressionCompressed sensing algorithm)

文件列表：

cnn-compression-master, 0 , 2020-11-16
cnn-compression-master\.gitignore, 28 , 2020-11-16
cnn-compression-master\FALCON2, 0 , 2020-11-16
cnn-compression-master\FALCON2\README.md, 6786 , 2020-11-16
cnn-compression-master\FALCON2\requirements.txt, 110 , 2020-11-16
cnn-compression-master\FALCON2\script, 0 , 2020-11-16
cnn-compression-master\FALCON2\script\imagenet_vgg_test.sh, 1226 , 2020-11-16
cnn-compression-master\FALCON2\script\imagenet_vgg_train.sh, 1272 , 2020-11-16
cnn-compression-master\FALCON2\script\inference.sh, 1176 , 2020-11-16
cnn-compression-master\FALCON2\script\train.sh, 1183 , 2020-11-16
cnn-compression-master\FALCON2\src, 0 , 2020-11-16
cnn-compression-master\FALCON2\src\imagenetutils, 0 , 2020-11-16
cnn-compression-master\FALCON2\src\imagenetutils\__init__.py, 319 , 2020-11-16
cnn-compression-master\FALCON2\src\imagenetutils\dataloaders.py, 10774 , 2020-11-16
cnn-compression-master\FALCON2\src\imagenetutils\eval.py, 808 , 2020-11-16
cnn-compression-master\FALCON2\src\imagenetutils\logger.py, 3876 , 2020-11-16
cnn-compression-master\FALCON2\src\imagenetutils\misc.py, 2283 , 2020-11-16
cnn-compression-master\FALCON2\src\imagenetutils\visualize.py, 3879 , 2020-11-16
cnn-compression-master\FALCON2\src\models, 0 , 2020-11-16
cnn-compression-master\FALCON2\src\models\falcon.py, 9475 , 2020-11-16
cnn-compression-master\FALCON2\src\models\model_imageNet.py, 12589 , 2020-11-16
cnn-compression-master\FALCON2\src\models\resnet.py, 9919 , 2020-11-16
cnn-compression-master\FALCON2\src\models\stconv_branch.py, 14877 , 2020-11-16
cnn-compression-master\FALCON2\src\models\vgg.py, 4684 , 2020-11-16
cnn-compression-master\FALCON2\src\train_test, 0 , 2020-11-16
cnn-compression-master\FALCON2\src\train_test\imagenet.py, 25320 , 2020-11-16
cnn-compression-master\FALCON2\src\train_test\main.py, 8387 , 2020-11-16
cnn-compression-master\FALCON2\src\train_test\test.py, 2310 , 2020-11-16
cnn-compression-master\FALCON2\src\train_test\train.py, 5432 , 2020-11-16
cnn-compression-master\FALCON2\src\train_test\trained_model, 0 , 2020-11-16
cnn-compression-master\FALCON2\src\train_test\trained_model\conv=FALCON,model=ResNet,data=cifar100,rank=1,alpha=0.77,init,opt=SGD,lr=0.1.pkl, 5319454 , 2020-11-16
cnn-compression-master\FALCON2\src\train_test\validation.py, 2131 , 2020-11-16
cnn-compression-master\FALCON2\src\utils, 0 , 2020-11-16
cnn-compression-master\FALCON2\src\utils\compression_cal.py, 5365 , 2020-11-16
cnn-compression-master\FALCON2\src\utils\default_param.py, 5291 , 2020-11-16
cnn-compression-master\FALCON2\src\utils\load_data.py, 6807 , 2020-11-16
cnn-compression-master\FALCON2\src\utils\lr_decay.py, 1306 , 2020-11-16
cnn-compression-master\FALCON2\src\utils\optimizer_option.py, 2401 , 2020-11-16
cnn-compression-master\FALCON2\src\utils\save_restore.py, 9455 , 2020-11-16
cnn-compression-master\FALCON2\src\utils\timer.py, 1787 , 2020-11-16
cnn-compression-master\LICENSE, 11357 , 2020-11-16
cnn-compression-master\NOTICE, 162 , 2020-11-16

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0 个回复

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    2012-07-25 06:25:54下载
    积分：1
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    说明：  最二小乘影像匹配，精度可以达到0.01个像素，也可以最二小乘影像模板匹配。经过修改后可以直接用，只要调用一个函数就搞定，函数参数的说明很清楚，绝对不会有任何困难，非常好用。(Most two Theravada image matching accuracy can reach 0.01 pixel images can also be the most two Theravada template matching. After modifications can be directly used, as long as is you call a function, function parameters, the description is clear and will not have any difficulties, very easy to use.)
    2010-04-30 16:42:22下载
    积分：1
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    包滤波器，主要功能是在给定的滑动窗口大小下，对每个窗口内的像素值进行相加求和(Packet filter, the main function is given under the sliding window size for each window of the pixel values ​ ​ summed)
    2011-10-21 15:19:28下载
    积分：1
  • matlab
    说明：  图像识别的matlab程序实现，基于神经网络的图像识别(Image recognition matlab program implementation, image recognition based on neural networks)
    2021-04-26 16:48:45下载
    积分：1
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  • kmeans
    kmeans算法实现，对图像处理。亲自使用过，可用，供参考。(Kmeans algorithm for image processing.)
    2013-10-17 10:57:41下载
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    提出一种基于非采样contourlet变换的红外与可见光图像融合方法。高频系数结合活性度量和多分辨率系数间相关性来实现加权融合；低频系数通过局部梯度进行活性度量，再采用加权与选择相结合的规则。(Proposed a contourlet transform infrared and visible image fusion method based on non-sampling. The high-frequency coefficient binding activity to measure the correlation between multi-resolution coefficient weighted fusion activity measure low frequency coefficients of the local gradient, and then weighted and selection combining rules.)
    2020-07-17 17:58:48下载
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