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SketchRecognise
说明： 基于勾勒图的图像检索源码（download from github）(code for DeepSBIR-master which is download from github)
- 2019-12-05 16:11:35下载
- 积分：1
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求最大公约数和最小公倍数
【实例简介】
- 2021-08-07 00:31:07下载
- 积分：1
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7623
Energy spectrum analysis and calculation, Including the MUSIC algorithm, ESPRIT algorithm ROOT-MUSIC algorithm, Fractional Fourier transform computing.
- 2017-11-06 19:59:56下载
- 积分：1
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python打印玫瑰花
python打印玫瑰花
- 2020-12-31 15:38:59下载
- 积分：1
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Learning_Python_(5th_Edition)
说明： learning python第五版，经典书籍，学习python必看。(Learning Python fifth edition, classic books, learning must see.)
- 2020-06-24 03:20:01下载
- 积分：1
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LDA
说明： dataset-dimensionality-reduction-python-master
- 2020-06-25 19:00:01下载
- 积分：1
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149110
说明： shows test signal TestDAC1 on pin AUX1 and TestDAC2 on pin AUX2 when the
TestDAC1Reg register is programmed with a slope defined by values 00h to 3Fh and the
TestDAC2Reg register is programmed with a rectangular signal defined by values 00h
and 3Fh.
- 2020-06-20 03:20:02下载
- 积分：1
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compute-stress
abaqus的python脚本开发，计算平均应力(abaqus python script development, calculate the average stress)
- 2020-06-29 05:40:01下载
- 积分：1
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全国招聘数据
说明： 用于爬去前程无忧招聘信息，可以爬取 人工智能 python 等十几种职业(Used to climb the future worry-free recruitment information)
- 2019-01-26 12:32:03下载
- 积分：1
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py-faster-rcnn-master
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.)
- 2020-12-11 15:39:18下载
- 积分：1
