| #include <iostream> | |
| #include <fstream> | |
| #include <string> | |
| #include <vector> | |
| #include <unordered_map> | |
| #include <boost/algorithm/string.hpp> | |
| #include <utf8proc.h> | |
| //https://unicode.org/reports/tr15/#Norm_Forms | |
| //https://ssl.icu-project.org/apiref/icu4c/uchar_8h.html |
| ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ | |
| ## Created by: Hang Zhang, Rutgers University, Email: zhang.hang@rutgers.edu | |
| ## Modified by Thomas Wolf, HuggingFace Inc., Email: thomas@huggingface.co | |
| ## Copyright (c) 2017-2018 | |
| ## | |
| ## This source code is licensed under the MIT-style license found in the | |
| ## LICENSE file in the root directory of this source tree | |
| ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ | |
| """Encoding Data Parallel""" |
| # coding=utf-8 | |
| # auc值的大小可以理解为: 随机抽一个正样本和一个负样本，正样本预测值比负样本大的概率 | |
| # 根据这个定义，我们可以自己实现计算auc | |
| import random | |
| import time | |
| def timeit(func): | |
| """ | |
| 装饰器，计算函数执行时间 |
| { | |
| 0: 'tench, Tinca tinca', | |
| 1: 'goldfish, Carassius auratus', | |
| 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias', | |
| 3: 'tiger shark, Galeocerdo cuvieri', | |
| 4: 'hammerhead, hammerhead shark', | |
| 5: 'electric ray, crampfish, numbfish, torpedo', | |
| 6: 'stingray', | |
| 7: 'cock', | |
| 8: 'hen', |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
| #include "caffeclassifier.h" | |
| CaffeClassifier::CaffeClassifier(const string& model_file, | |
| const string& trained_file, | |
| const string& mean_file, | |
| const string& label_file, | |
| const bool use_GPU, | |
| const int batch_size) { | |
| if (use_GPU) | |
| Caffe::set_mode(Caffe::GPU); |
| # Hello, and welcome to makefile basics. | |
| # | |
| # You will learn why `make` is so great, and why, despite its "weird" syntax, | |
| # it is actually a highly expressive, efficient, and powerful way to build | |
| # programs. | |
| # | |
| # Once you're done here, go to | |
| # http://www.gnu.org/software/make/manual/make.html | |
| # to learn SOOOO much more. |
Probably the most straight forward way to start generating Point Clouds from a set of pictures.
VisualSFM is a GUI application for 3D reconstruction using structure from motion (SFM). The reconstruction system integrates several of my previous projects: SIFT on GPU(SiftGPU), Multicore Bundle Adjustment, and Towards Linear-time Incremental Structure from Motion. VisualSFM runs fast by exploiting multicore parallelism for feature detection, feature matching, and bundle adjustment.
For dense reconstruction, this program supports Yasutaka Furukawa's PMVS/CMVS tool chain, and can prepare data for Michal Jancosek's CMP-MVS. In addition, the output of VisualSFM is natively supported by Mathias Rothermel and Konrad Wenzel's [SURE]
| import time | |
| import numpy as np | |
| from sklearn.datasets.samples_generator import make_blobs | |
| from sklearn.neighbors import LSHForest | |
| from sklearn.neighbors import NearestNeighbors | |
| from sklearn.preprocessing import normalize | |
| from annoy import AnnoyIndex | |
| from pyflann import FLANN | |
| n_iter = 100 |
| #!/bin/bash | |
| # Bash script to install latest version of ffmpeg and its dependencies on Ubuntu 12.04 or 14.04 | |
| # Inspired from https://gist.github.com/faleev/3435377 | |
| # Remove any existing packages: | |
| sudo apt-get -y remove ffmpeg x264 libav-tools libvpx-dev libx264-dev | |
| # Get the dependencies (Ubuntu Server or headless users): | |
| sudo apt-get update |
