| # Holds repositories directory | |
| sudo chown -R -v git:root /var/opt/gitlab/git-data | |
| sudo chmod -R -v 0700 /var/opt/gitlab/git-data | |
| # Holds git repositories | |
| sudo chown -R -v git:git /var/opt/gitlab/git-data/repositories | |
| sudo chmod -R -v 2770 /var/opt/gitlab/git-data/repositories | |
| # Holds large object directories | |
| sudo chown -R -v git:gitlab-www /var/opt/gitlab/gitlab-rails/shared |
原文：深度学习大讲堂
点击上方“深度学习大讲堂”可订阅哦！
编者按：文字，区别于变幻莫测的图像和视频，有着更强的逻辑性和更概括的表达力。随着互联网和移动互联网技术的高速发展，越来越多的新型应用场景需要利用图像中的文字信息。从海量视频中快速检索感兴趣的文字，可以极大提高人类的认知效率。因此，自然场景中的文字提取技术，即从照片或视频中将文字识别出来，成为了近几年计算机视觉领域的热门研究课题。在本文中，来自华中科技大学的白翔教授，将为大家讲述多方向场景文字检测的奥秘。文末，大讲堂特别提供文中提到所有文章的下载链接。
| charles=/Applications/Charles.app/Contents/Java/charles.jar | |
| dir=charleshack | |
| mkdir $dir | |
| cd $dir | |
| cat >> psPJ.java <<EOF | |
| package com.xk72.charles; | |
| public final class psPJ { | |
| public static boolean qIvM() { return true; } | |
| public static String tCiz() { return "https://0x0d.im"; } |
| """ | |
| Use in PyTorch. | |
| """ | |
| def accuracy(output, target): | |
| """Computes the accuracy for multiple binary predictions""" | |
| pred = output >= 0.5 | |
| truth = target >= 0.5 | |
| acc = pred.eq(truth).sum() / target.numel() | |
| return acc |
| 127.0.0.1 api.ad.xiaomi.com | |
| 127.0.0.1 sdkconfig.ad.xiaomi.com | |
| 127.0.0.1 ad.mi.com | |
| 127.0.0.1 ad.xiaomi.com | |
| 127.0.0.1 ad1.xiaomi.com | |
| 127.0.0.1 adv.sec.miui.com | |
| 127.0.0.1 test.ad.xiaomi.com | |
| 127.0.0.1 new.api.ad.xiaomi.com |
| """ | |
| Clean and simple Keras implementation of network architectures described in: | |
| - (ResNet-50) [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf). | |
| - (ResNeXt-50 32x4d) [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf). | |
| Python 3. | |
| """ | |
| from keras import layers | |
| from keras import models |
This is an Keras implementation of ResNet-101 with ImageNet pre-trained weights. I converted the weights from Caffe provided by the authors of the paper. The implementation supports both Theano and TensorFlow backends. Just in case you are curious about how the conversion is done, you can visit my blog post for more details.
ResNet Paper:
Deep Residual Learning for Image Recognition.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
arXiv:1512.03385
This is an Keras implementation of ResNet-152 with ImageNet pre-trained weights. I converted the weights from Caffe provided by the authors of the paper. The implementation supports both Theano and TensorFlow backends. Just in case you are curious about how the conversion is done, you can visit my blog post for more details.
ResNet Paper:
Deep Residual Learning for Image Recognition.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
arXiv:1512.03385
This is a guide for installing OpenCV 3.1 on Ubuntu 16.04 with Cuda 8 support. This has been tested using a system with a GeForce GTX 1060 and on one with a GeForce GTX 1080.
Install Nvidia drivers
# Start clean
sudo apt purge nvidia-*
# Add the PPA
sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update
| # coding: utf-8 | |
| import requests | |
| import time | |
| import os | |
| store_url = 'https://reserve-prime.apple.com/CN/zh_CN/reserve/iPhoneX/stores.json' | |
| stores = [('R388', '西单'), ('R448', '王府井')] | |
| product = 'MQA82CH/A' | |
| availability_url = 'https://reserve-prime.apple.com/CN/zh_CN/reserve/iPhoneX/availability.json' |