登录
首页 » Python » py-faster-rcnn-master

py-faster-rcnn-master

于 2020-12-11 发布 文件大小:654KB
0 309
下载积分: 1 下载次数: 14

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

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • 用FCM实现分割
    基于模糊C-均值聚类算法的图像分割,消除二值分割的非此即彼的缺陷(Image segmentation based on fuzzy C- means clustering algorithm eliminates the defects of two value segmentation.)
    2018-05-30 16:54:45下载
    积分:1
  • ScSR
    Jianchao Yang 的基于稀疏表示的单幅图像重建的原始代码,先将高低训练图像分块,再将块训练成高低字典,将测试图像映射到低字典上,得到系数,再乘以高子典就得到最后的图像。对学习超分辨率同学的参考作用很大。(This is the original matlab code for super resolution by Jianchao Yang 。The method is sparse represent based on the overcomplete dictionary。)
    2020-11-24 00:39:33下载
    积分:1
  • PCA&SVD_Denoising
    使用PCA和SVD进行数据去噪,利用数据主要的特征向量进行数据恢复和重建(Using PCA and SVD for data denoising)
    2018-08-21 09:57:25下载
    积分:1
  • SAR合成孔径雷达图像点目标(附matlab代码)
    说明:  SAR合成孔径雷达的点目标仿真报告,含matlab代码(SAR synthetic aperture radar point target simulation report, including Matlab code)
    2020-06-26 23:00:02下载
    积分:1
  • BP-classification
    BP神经网络图像分类程序,代码很好懂,适合初学者。样本选的不是很大。(BP neural network image classification procedures, code easy to understand for beginners. Sample selected is not great.)
    2013-07-24 18:19:51下载
    积分:1
  • 19
    说明:  对图像进行中值滤波,去除噪声,并与原图像进行比较(Median filter on the image to remove noise, compared with the original image)
    2011-04-09 19:28:58下载
    积分:1
  • qtimer
    The QTimer class provides repetitive and single-shot timers for Linux.
    2013-09-19 03:12:19下载
    积分:1
  • ms_ssim
    Multi-Scale Structural Similarity Index (MS-SSIM). Download here. Please cite the following paper in any published work if you use this software. Z. Wang, E. P. Simoncelli and A. C. Bovik, "Multi-scale structural similarity for image quality assessment," IEEE Asilomar Conference Signals, Systems and Computers, Nov. 2003.
    2010-03-02 01:50:24下载
    积分:1
  • CircularHough_Grd
    houph变换,通过梯度函数得到图像的梯度矩阵,通过检测圆检测眼睛位置。(houph transform, through the gradient function to be the image gradient matrix, circle detection by detecting the location of the eyes.)
    2008-07-29 19:24:53下载
    积分:1
  • CVmodel
    实现了Chan-Vese算法,参考文献active contour without edges, 图像处理方面引用率最高的文献之一(Realize the Chan-Vese algorithm, reference active contour without edges, image processing, to quote one of the highest rates of the literature)
    2008-01-31 22:14:47下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载