登录
首页 » matlab » CNN_face_detection-master

CNN_face_detection-master

于 2016-04-27 发布 文件大小:8176KB
0 254
下载积分: 1 下载次数: 111

代码说明:

  CVPR2015级联CNN进行人脸检测的MATLAB实现代码,按照流程实现,效果绝佳(CVPR2015 cascade CNN for face detection MATLAB implementation code, according to the sequence, and excellent results)

文件列表:

CNN_face_detection-master
.........................\face_calibration
.........................\................\calibration_AFLW.py,3626,2015-12-22
.........................\................\calibration_CACD.py,2131,2015-12-22
.........................\................\shuffle_write_calibration.py,953,2015-12-22
.........................\................\write_train_val_calibration.py,2505,2015-12-22
.........................\face_detection
.........................\..............\create_mean_numpy.py,508,2015-12-22
.........................\..............\detections
.........................\..............\..........\ContROC.txt,26681,2015-12-22
.........................\..............\..........\DiscROC-01.txt,42393,2015-12-22
.........................\..............\..........\DiscROC-02.txt,44091,2015-12-22
.........................\..............\..........\DiscROC-03.txt,43093,2015-12-22
.........................\..............\..........\DiscROC-04.txt,44472,2015-12-22
.........................\..............\..........\DiscROC-05.txt,44802,2015-12-22
.........................\..............\..........\DiscROC-06.txt,45816,2015-12-22
.........................\..............\..........\DiscROC-07.txt,39458,2015-12-22
.........................\..............\..........\DiscROC-08.txt,42233,2015-12-22
.........................\..............\..........\DiscROC-09.txt,40040,2015-12-22
.........................\..............\..........\DiscROC-10.txt,44786,2015-12-22
.........................\..............\..........\fold-01-out.txt,48332,2015-12-22
.........................\..............\..........\fold-02-out.txt,49260,2015-12-22
.........................\..............\..........\fold-03-out.txt,47400,2015-12-22
.........................\..............\..........\fold-04-out.txt,49861,2015-12-22
.........................\..............\..........\fold-05-out.txt,49954,2015-12-22
.........................\..............\..........\fold-06-out.txt,40960,2015-12-22
.........................\..............\..........\fold-07-out.txt,50576,2015-12-22
.........................\..............\..........\fold-08-out.txt,53602,2015-12-22
.........................\..............\..........\fold-09-out.txt,50384,2015-12-22
.........................\..............\..........\fold-10-out.txt,56699,2015-12-22
.........................\..............\DiscROC.txt,431184,2015-12-22
.........................\..............\evaluate_fddb.py,1025,2015-12-22
.........................\..............\face_12c_detect.py,27726,2015-12-22
.........................\..............\face_48_AFW.py,27228,2015-12-22
.........................\..............\face_48_fddb.py,28745,2015-12-22
.........................\..............\face_48_fddb_fullconv.py,28464,2015-12-22
.........................\..............\face_cascade_AFW.py,27228,2015-12-22
.........................\..............\face_cascade_fddb.py,28745,2015-12-22
.........................\..............\face_cascade_fullconv_fddb.py,3549,2015-12-22
.........................\..............\face_cascade_fullconv_fddb_single_crop.py,2989,2015-12-22
.........................\..............\face_cascade_fullconv_quantize_fddb.py,3734,2015-12-22
.........................\..............\face_cascade_fullconv_signal_quantize_fddb.py,22189,2015-12-22
.........................\..............\face_cascade_fullconv_single_crop_single_image.py,1468,2015-12-22
.........................\..............\face_detection_example.py,794,2015-12-22
.........................\..............\face_detection_functions.py,40359,2015-12-22
.........................\..............\face_detection_functions.pyc,24466,2015-12-22
.........................\..............\face_ViolaJones.py,2886,2015-12-22
.........................\..............\FDDB-fold
.........................\..............\.........\FDDB-fold-01.txt,6747,2015-12-22
.........................\..............\.........\FDDB-fold-02.txt,6646,2015-12-22
.........................\..............\.........\FDDB-fold-03.txt,6392,2015-12-22
.........................\..............\.........\FDDB-fold-04.txt,7018,2015-12-22
.........................\..............\.........\FDDB-fold-05.txt,6937,2015-12-22
.........................\..............\.........\FDDB-fold-06.txt,7024,2015-12-22
.........................\..............\.........\FDDB-fold-07.txt,6489,2015-12-22
.........................\..............\.........\FDDB-fold-08.txt,6458,2015-12-22
.........................\..............\.........\FDDB-fold-09.txt,6014,2015-12-22
.........................\..............\.........\FDDB-fold-10.txt,6528,2015-12-22
.........................\..............\fold-01-out.txt,17176,2015-12-22
.........................\..............\fold-02-out.txt,17146,2015-12-22
.........................\..............\fold-03-out.txt,16870,2015-12-22
.........................\..............\fold-04-out.txt,18345,2015-12-22
.........................\..............\fold-05-out.txt,17735,2015-12-22
.........................\..............\fold-06-out.txt,17959,2015-12-22
.........................\..............\fold-07-out.txt,16995,2015-12-22
.........................\..............\fold-08-out.txt,16964,2015-12-22
.........................\..............\fold-09-out.txt,16284,2015-12-22
.........................\..............\fold-10-out.txt,17664,2015-12-22
.........................\..............\load_model_functions.py,8499,2015-12-22
.........................\..............\load_model_functions.pyc,4483,2015-12-22
.........................\..............\many2oneText_fold.py,595,2015-12-22
.........................\..............\many2oneText_out.py,589,2015-12-22
.........................\..............\old_detection_methods_backup.py,16932,2015-12-22
.........................\..............\sortROC.py,600,2015-12-22
.........................\..............\test.py,16248,2015-12-22
.........................\..............\test_mean.py,1264,2015-12-22
.........................\face_net_surgery
.........................\................\face12c_full_conv_params.txt,119429,2015-12-22
.........................\................\face12c_full_conv_quantize_3.py,3983,2015-12-22
.........................\................\face12c_full_conv_quantize_3.txt,119429,2015-12-22
.........................\................\face12c_full_conv_quantize_3_params.txt,38333,2015-12-22
.........................\................\face12c_full_conv_quantize_3_to_9.py,3573,2015-12-22
.........................\................\face12c_full_conv_test.py,4698,2015-12-22
.........................\................\face_12_cal_quantize_3_to_9.py,4423,2015-12-22
.........................\................\face_12_full_conv_params.txt,119429,2015-12-22
.........................\................\face_12_full_conv_test.py,4649,2015-12-22
.........................\................\face_12_quantize_3.py,3983,2015-12-22
.........................\................\face_12_quantize_8.py,1467,2015-12-22
.........................\................\face_12_surgery.py,2393,2015-12-22
.........................\................\face_24c_quantize_3_to_9.py,3942,2015-12-22
.........................\................\face_24_cal_quantize_3_to_9.py,3947,2015-12-22
.........................\................\face_48c_quantize_3_to_9.py,4028,2015-12-22
.........................\................\face_48_cal_quantize_3_to_9.py,4037,2015-12-22
.........................\................\face_48_full_conv_test.py,1704,2015-12-22
.........................\................\face_48_surgery.py,2030,2015-12-22
.........................\................\face_range.txt,3093,2015-12-22
.........................\................\face_range_quantize_objective.txt,4465,2015-12-22
.........................\................\face_weight_bias_range.py,6716,2015-12-22
.........................\................\htm" target=_blank>file_write,1469,2015-12-22
.........................\................\full_conv_blobs_ranges.txt,3723,2015-12-22

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

  • peizhun
    互信息matlab, 混沌时间序列分析与预测工具箱 Version1.0 点这里下载: 混沌时间序列分析与预测工具箱 Version1.0 chaotic time series analysis and prediction matlab toolbox - version 1.0 1、该工具箱包括了混沌时间序列分析与预测的常用方法,有 (Mutual information matlab, chaotic time series analysis and forecast Version1.0 point toolbox here to download: Chaotic Time Series Analysis and Forecast of the toolbox Version1.0chaotic time series analysis and prediction matlab toolbox- version 1.01, the kit includes a chaotic time series analysis with the projected common method, there are)
    2008-01-03 20:08:46下载
    积分:1
  • m_fh_simulatin_err_ratio
    利用m序列生成跳频序列并用于跳频通信,并给出误码率曲线(Use of m sequences generated hopping sequence and used for frequency hopping communications, and give the bit error rate curve)
    2021-04-01 20:59:07下载
    积分:1
  • chapitre2
    this file contains matlab software
    2011-09-15 15:16:21下载
    积分:1
  • LMSandKalman
    一个用来学习和使用的标准kalman和lms变步长最小均方滤波算法源程序代码(One source code to learn and use the standard kalman and least mean square filtering algorithm)
    2013-11-01 08:14:33下载
    积分:1
  • 64QAM_modem
    64QAM的设计论文,有非常高的参考价值(64QAM design papers, there is a very high reference value)
    2013-11-22 18:02:16下载
    积分:1
  • MeanShiftCluster
    经典的meanshift聚类算法。算法描述的很详尽,可以直接复用。(Meanshift classical clustering algorithm. Algorithm description is very detailed, can be directly reused.)
    2010-06-12 11:28:16下载
    积分:1
  • fake_randoner
    伪随机数发生器: “利用随机序列进行间隔控制,选择消息隐藏位”的描述算法、Matlab程序(可参考所给的程序)、原理与步骤(some thing about watermark)
    2009-05-26 18:39:15下载
    积分:1
  • mybp
    MATLAB语言编写的利用遗传算法优化小波神经网络的一个例子(MATLAB language, an example of the use of genetic algorithms to optimize the wavelet neural network)
    2012-05-04 21:59:25下载
    积分:1
  • 10.1.1.193.161
    国外的一篇关于飞行器滑模控制的文章,写的比较好,对我启发比较 (Abroad an aircraft sliding mode control article, write better, I am inspired comparison)
    2013-04-09 22:08:37下载
    积分:1
  • CMA_Equalizer
    盲均衡的经典算法-恒模算法,实验效果良好,是入门学习的必须(Classic blind equalization algorithm- constant modulus algorithm, the experimental result is good, the entry must learn)
    2020-09-11 22:18:02下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载