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

py-faster-rcnn-master

于 2020-12-11 发布 文件大小:654KB
0 257
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

  • deblur_code_1_2
    对较真实的运动模糊图像进行恢复,此种运动模糊图像限于水平或垂直的运动模糊图像。 参考文献:Removing Camera Shake from a Single Image ,siggraph06(On the more realistic motion-blurred image restoration, such motion-blurred images limited to horizontal or vertical motion-blurred images. References: Removing Camera Shake from a Single Image, siggraph06)
    2008-04-23 12:42:33下载
    积分:1
  • xiugai
    针对于图像复制粘贴篡改检测,对图像数据进行降维处理,并进行块匹配,比较其图像块欧几里得距离(For copy and paste the image tamper detection, image data dimensionality reduction, and block matching, comparing its image block Euclidean distance)
    2014-05-09 16:19:14下载
    积分:1
  • HASZR
    ADAPTIVETHRESHOLD An adaptive thresholding algorithm that se
    2017-05-15 21:54:14下载
    积分:1
  • lp-d-r
    对任意一副灰度图像进行拉普拉斯金字塔分解。(For any gray-scale image of a Laplacian pyramid decomposition.)
    2009-05-12 08:55:13下载
    积分:1
  • beyond_12041126
    论文+ppt,我的数字图像处理课程设计,题目是基于均值滤波和小波分析的图像去噪方法(Thesis+ppt, digital image processing course design, subject image denoising method based on mean filter and wavelet analysis)
    2012-11-27 16:34:09下载
    积分:1
  • Tamura_texture
    Tamura纹理特征提取程序,matlab编写,通过运行测试无误。另附有pdf介绍Tamura纹理特征定义,希望对大家有帮助。(Tamura texture feature extraction procedures, matlab prepared correctly by running the test. Tamura has attached pdf introduce the definition of texture features, in the hope that everyone has to help.)
    2008-08-02 20:20:06下载
    积分:1
  • face-detect-system
    一个不错的人脸检测演示系统里面有人脸检测的实际照片包+(face detect system)
    2013-11-11 18:15:48下载
    积分:1
  • ImageMagick-6.8.6-7.tar
    ImageMagick® is a software suite to create, edit, compose, or convert bitmap images. It can read and write images in a variety of formats (over 100) including DPX, EXR, GIF, JPEG, JPEG-2000, PDF, PhotoCD, PNG, Postscript, SVG, and TIFF. Use ImageMagick to resize, flip, mirror, rotate, distort, shear and transform images, adjust image colors, apply various special effects, or draw text, lines, polygons, ellipses and Bézier curves.
    2013-08-03 05:33:24下载
    积分:1
  • 32131312312321njianceok
    基于HOUGH变换的车道线检测和识别的程序,可以使用(Lane HOUGH transform detection and recognition based on the program, you can use)
    2014-04-21 15:16:13下载
    积分:1
  • JPEGCompression
    说明:  编码: (1)进行颜色转换,将RGB格式转换为YUV格式。 (2)将待编码的N×N的图像分解成(N/8)^ 2 个大小为8×8的子图像。 (3)对每个子图像进行DCT变换,得到各子图像的变换系数。这一步的实质是把空间域表示的图像转换成频率域表示的图像。 (4)对变换系数进行量化。 (5)进行Z字形重排 (6)使用霍夫曼变长变码编码器对量化的系数进行编码,得到压缩后的图像(数据)。 解码: (1) 对压缩的图像数据进行解码,得到用量化系数表示的图像数据。 (2) 进行反Z字型重排 (3)用与编码时相同的量化函数或量化值表对用量化系数表示的图像数据进行逆量化,得到每个子图像的变换系数。 (4)对逆量化得到的每个子图像的变换系数进行反向正交变换(如反向DCT变换等),得到(N/8)^2 个大小为8×8的子图像。 (5)将(N/8)^2 个大小为8×8的子图像重构成一个N×N的图像。 (6)进行颜色组合,将YUV格式转换为RGB格式图像。(JPEG compression and decompression process)
    2019-02-18 22:58:13下载
    积分:1
  • 696516资源总数
  • 106446会员总数
  • 9今日下载