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py-faster-rcnn-master

于 2020-12-11 发布 文件大小:654KB
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代码说明:

  图像检测的算法,Faster R-CNN算法,先对整张图像进行卷积计算,然后通过感兴趣区域池化层(RoI Pooling Layer)将选择性搜索算法推荐出来的候选区域和卷积网络计算出的特征映射图进行融合,得到候选区域对应的特征矢量,这种共享卷积计算的操作极大地减少了卷积计算的次数。而且这些特征矢量的维度统一,方便后续的分类工作。通过感兴趣区域池化层处理卷积特征,并将得到的特征送往两个并行计算任务进行训练,分类和定位回归。通过这些方法和改进的框架,Fast R-CNN 用更短的训练和测试时长,取得了比 R-CNN 更好的效果(Faster R-CNN algorithm first convolutes the whole image, then fuses the candidate regions recommended by the selective search algorithm and the feature mapping maps calculated by the convolution network through the RoI Pooling Layer to get the corresponding feature vectors of the candidate regions, which greatly reduces the number of convolution calculations. Moreover, the dimension of these feature vectors is unified, which facilitates the subsequent classification work. The convolution feature is processed by the pooling layer of the region of interest, and the obtained feature is sent to two parallel computing tasks for training, classification and positioning regression. Through these methods and improved framework, Fast R-CNN uses shorter training and testing time and achieves better results than R-CNN.)

文件列表:

py-faster-rcnn-master\.gitignore, 84 , 2018-12-17
py-faster-rcnn-master\.gitmodules, 131 , 2018-12-17
py-faster-rcnn-master\data\.gitignore, 70 , 2018-12-17
py-faster-rcnn-master\data\demo\000456.jpg, 105302 , 2018-12-17
py-faster-rcnn-master\data\demo\000542.jpg, 115536 , 2018-12-17
py-faster-rcnn-master\data\demo\001150.jpg, 88635 , 2018-12-17
py-faster-rcnn-master\data\demo\001763.jpg, 73424 , 2018-12-17
py-faster-rcnn-master\data\demo\004545.jpg, 123072 , 2018-12-17
py-faster-rcnn-master\data\pylintrc, 56 , 2018-12-17
py-faster-rcnn-master\data\README.md, 2516 , 2018-12-17
py-faster-rcnn-master\data\scripts\fetch_faster_rcnn_models.sh, 842 , 2018-12-17
py-faster-rcnn-master\data\scripts\fetch_imagenet_models.sh, 825 , 2018-12-17
py-faster-rcnn-master\data\scripts\fetch_selective_search_data.sh, 858 , 2018-12-17
py-faster-rcnn-master\experiments\cfgs\faster_rcnn_alt_opt.yml, 78 , 2018-12-17
py-faster-rcnn-master\experiments\cfgs\faster_rcnn_end2end.yml, 227 , 2018-12-17
py-faster-rcnn-master\experiments\logs\.gitignore, 7 , 2018-12-17
py-faster-rcnn-master\experiments\README.md, 185 , 2018-12-17
py-faster-rcnn-master\experiments\scripts\faster_rcnn_alt_opt.sh, 1509 , 2018-12-17
py-faster-rcnn-master\experiments\scripts\faster_rcnn_end2end.sh, 1781 , 2018-12-17
py-faster-rcnn-master\experiments\scripts\fast_rcnn.sh, 1448 , 2018-12-17
py-faster-rcnn-master\lib\datasets\coco.py, 16560 , 2018-12-17
py-faster-rcnn-master\lib\datasets\ds_utils.py, 1336 , 2018-12-17
py-faster-rcnn-master\lib\datasets\factory.py, 1403 , 2018-12-17
py-faster-rcnn-master\lib\datasets\imdb.py, 9811 , 2018-12-17
py-faster-rcnn-master\lib\datasets\pascal_voc.py, 14217 , 2018-12-17
py-faster-rcnn-master\lib\datasets\tools\mcg_munge.py, 1451 , 2018-12-17
py-faster-rcnn-master\lib\datasets\VOCdevkit-matlab-wrapper\get_voc_opts.m, 231 , 2018-12-17
py-faster-rcnn-master\lib\datasets\VOCdevkit-matlab-wrapper\voc_eval.m, 1332 , 2018-12-17
py-faster-rcnn-master\lib\datasets\VOCdevkit-matlab-wrapper\xVOCap.m, 258 , 2018-12-17
py-faster-rcnn-master\lib\datasets\voc_eval.py, 6938 , 2018-12-17
py-faster-rcnn-master\lib\datasets\__init__.py, 248 , 2018-12-17
py-faster-rcnn-master\lib\fast_rcnn\bbox_transform.py, 2540 , 2018-12-17
py-faster-rcnn-master\lib\fast_rcnn\config.py, 9213 , 2018-12-17
py-faster-rcnn-master\lib\fast_rcnn\nms_wrapper.py, 642 , 2018-12-17
py-faster-rcnn-master\lib\fast_rcnn\test.py, 11120 , 2018-12-17
py-faster-rcnn-master\lib\fast_rcnn\train.py, 6076 , 2018-12-17
py-faster-rcnn-master\lib\fast_rcnn\__init__.py, 248 , 2018-12-17
py-faster-rcnn-master\lib\Makefile, 56 , 2018-12-17
py-faster-rcnn-master\lib\nms\.gitignore, 15 , 2018-12-17
py-faster-rcnn-master\lib\nms\cpu_nms.pyx, 2241 , 2018-12-17
py-faster-rcnn-master\lib\nms\gpu_nms.hpp, 146 , 2018-12-17
py-faster-rcnn-master\lib\nms\gpu_nms.pyx, 1110 , 2018-12-17
py-faster-rcnn-master\lib\nms\nms_kernel.cu, 5064 , 2018-12-17
py-faster-rcnn-master\lib\nms\py_cpu_nms.py, 1051 , 2018-12-17
py-faster-rcnn-master\lib\nms\__init__.py, 0 , 2018-12-17
py-faster-rcnn-master\lib\pycocotools\coco.py, 14881 , 2018-12-17
py-faster-rcnn-master\lib\pycocotools\cocoeval.py, 19735 , 2018-12-17
py-faster-rcnn-master\lib\pycocotools\license.txt, 1533 , 2018-12-17
py-faster-rcnn-master\lib\pycocotools\mask.py, 4058 , 2018-12-17
py-faster-rcnn-master\lib\pycocotools\maskApi.c, 7704 , 2018-12-17
py-faster-rcnn-master\lib\pycocotools\maskApi.h, 1928 , 2018-12-17
py-faster-rcnn-master\lib\pycocotools\UPSTREAM_REV, 80 , 2018-12-17
py-faster-rcnn-master\lib\pycocotools\_mask.pyx, 10709 , 2018-12-17
py-faster-rcnn-master\lib\pycocotools\__init__.py, 21 , 2018-12-17
py-faster-rcnn-master\lib\roi_data_layer\layer.py, 7450 , 2018-12-17
py-faster-rcnn-master\lib\roi_data_layer\minibatch.py, 8169 , 2018-12-17
py-faster-rcnn-master\lib\roi_data_layer\roidb.py, 5611 , 2018-12-17
py-faster-rcnn-master\lib\roi_data_layer\__init__.py, 248 , 2018-12-17
py-faster-rcnn-master\lib\rpn\anchor_target_layer.py, 11344 , 2018-12-17
py-faster-rcnn-master\lib\rpn\generate.py, 3894 , 2018-12-17
py-faster-rcnn-master\lib\rpn\generate_anchors.py, 3110 , 2018-12-17
py-faster-rcnn-master\lib\rpn\proposal_layer.py, 6803 , 2018-12-17
py-faster-rcnn-master\lib\rpn\proposal_target_layer.py, 7495 , 2018-12-17
py-faster-rcnn-master\lib\rpn\README.md, 780 , 2018-12-17
py-faster-rcnn-master\lib\rpn\__init__.py, 262 , 2018-12-17
py-faster-rcnn-master\lib\setup.py, 5665 , 2018-12-17
py-faster-rcnn-master\lib\transform\torch_image_transform_layer.py, 2000 , 2018-12-17
py-faster-rcnn-master\lib\transform\__init__.py, 0 , 2018-12-17
py-faster-rcnn-master\lib\utils\.gitignore, 9 , 2018-12-17
py-faster-rcnn-master\lib\utils\bbox.pyx, 1756 , 2018-12-17
py-faster-rcnn-master\lib\utils\blob.py, 1625 , 2018-12-17
py-faster-rcnn-master\lib\utils\timer.py, 948 , 2018-12-17
py-faster-rcnn-master\lib\utils\__init__.py, 248 , 2018-12-17
py-faster-rcnn-master\LICENSE, 3745 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG16\faster_rcnn_end2end\solver.prototxt, 387 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG16\faster_rcnn_end2end\test.prototxt, 8754 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG16\faster_rcnn_end2end\train.prototxt, 9840 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG16\fast_rcnn\solver.prototxt, 395 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG16\fast_rcnn\test.prototxt, 6774 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG16\fast_rcnn\train.prototxt, 6625 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG_CNN_M_1024\faster_rcnn_end2end\solver.prototxt, 392 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG_CNN_M_1024\faster_rcnn_end2end\test.prototxt, 6973 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG_CNN_M_1024\faster_rcnn_end2end\train(1).prototxt, 7282 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG_CNN_M_1024\fast_rcnn\solver.prototxt, 398 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG_CNN_M_1024\fast_rcnn\test.prototxt, 4037 , 2018-12-17
py-faster-rcnn-master\models\coco\VGG_CNN_M_1024\fast_rcnn\train.prototxt, 4051 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\faster_rcnn_test.pt, 6263 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\rpn_test.pt, 5305 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\stage1_fast_rcnn_solver30k40k.pt, 390 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\stage1_fast_rcnn_train.pt, 8241 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\stage1_rpn_solver60k80k.pt, 378 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\stage1_rpn_train.pt, 8062 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\stage2_fast_rcnn_solver30k40k.pt, 390 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\stage2_fast_rcnn_train.pt, 8337 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\stage2_rpn_solver60k80k.pt, 378 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_alt_opt\stage2_rpn_train.pt, 8126 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_end2end\solver.prototxt, 407 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_end2end\test.prototxt, 8945 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\faster_rcnn_end2end\train.prototxt, 10209 , 2018-12-17
py-faster-rcnn-master\models\pascal_voc\VGG16\fast_rcnn\solver.prototxt, 400 , 2018-12-17

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  • 700977
    反混淆直线 采用未加权的区域采样算法 效果很不错()
    2018-05-27 14:24:12下载
    积分:1
  • medical-picture-process
    《医学图像编程技术》,周振环。一书的源码(the source code of the "medical picture programing")
    2012-03-14 15:02:31下载
    积分:1
  • bemd
    说明:  二维经验模态分解,分解图像等二维信号,可用于算法验证与学习(EMD This program calculates the Bidimensional EMD of a 2-d signal using the process of sifting. It is dependent on the function SIFT.)
    2011-02-26 20:03:08下载
    积分:1
  • deleteLittleAre
    算法实现消除小面积的区域,保留面积大的区域.对去除小斑点很稳健.(Algorithm to achieve the elimination of small areas of the region to retain the large area of the region. For the removal of small spots very sound.)
    2007-07-16 17:23:00下载
    积分:1
  • byxf
    本程序能够实现图像处理中的图像细化,是基于matlab的。(This procedure can realize image processing image thinning is based on the matlab.)
    2008-03-16 11:01:19下载
    积分:1
  • total_mkl_matlab
    MKL多核学习综述文章对应的很多MKL代码的合集,里面有很多有代表性的代码,非常详细!(MKL multicore learn many review articles MKL code corresponding collection, there are a lot of representative code, very detailed!)
    2021-04-19 15:08:51下载
    积分:1
  • Radar_imaging_technology
    保铮院士的《雷达成像技术》一书,对雷达成像原理等方面做了较详的介绍,对学习微波方面的有借鉴。(Bao Zheng academician of the radar imaging technology, a book on the radar imaging principle, etc. have done a more detailed description of the study area are from the microwave.)
    2008-01-22 16:06:31下载
    积分:1
  • tuxingxue
    画矩形的代码,包含各种算法,具体的自己看吧,这是图形处理的作业之一(Draw a rectangle of the code, contains a variety of algorithms, specifically for yourself, this is one of the graphics processing jobs)
    2013-09-26 14:42:11下载
    积分:1
  • SVM-Classification
    Matlab写的SVM图像分类程序,是完整的可运行的图像分类程序,高手可以研究一下。(Matlab write SVM image classification procedure is complete working image classification procedures, experts can look at.)
    2021-04-07 15:19:01下载
    积分:1
  • DIPUM2E_AppendixA
    数字图像处理有关的论文,很优秀,很有指导意义的一篇论文。(Digital image processing the papers, very good, very instructive paper.)
    2012-05-17 21:23:52下载
    积分:1
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