Caffe Installation Problems

1. Can not find hdf5.h

Solution:

--- INCLUDE) /usr/local/INCLUDE_DIRS := $(PYTHON_include /usr/include/hdf5/serial/
+++ INCLUDE) /usr/local/include/hdf5/serial/
--- LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5
+++ LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial
时间： 2024-10-14 11:33:33

Caffe Installation Problems的相关文章

Caffe Installation

当前,计算机视觉研究如火如荼,Deep Learning更是被捧上神坛,要靠自己完全实现DNN是一个很困难的过程,一个好的框架显得至关重要.BVLC发布的caffe就是一个很好用的DeepLearning框架.关于caffe的安装教程网上有很多,本文主要根据自己的安装经历讲讲如何快速的安装caffe. OS:Ubuntu 14.04 LTS/15.04 cuda version :7.5 cudnn 7.0 v3 Matlab R2015b 驱动安装 显卡驱动推荐用ubuntu自带的软件库安装

caffe学习记录(五) SSD训练+SSDpelee测试

SSD就不多介绍了,是今年非常流行的Object detection 模型:对各大数据集的测试结果如下表. 首先要git weiliu版本的caffe git clone https://github.com/weiliu89/caffe.git 然后在unbantu上对caffe进行配置,百度一下教程 # Modify Makefile.config according to your Caffe installation. cp Makefile.config.example Makefil

caffe—ssd安装教程

环境: ubuntu16.04 cuda8.0 cudnn5.0 已安装过caffe1.0 tensorflow1.2 教程 https://github.com/weiliu89/caffe/tree/ssd Installation Get the code. We will call the directory that you cloned Caffe into $CAFFE_ROOT git clone https://github.com/weiliu89/caffe.git cd

【caffe】caffe在linux环境下的安装与编译

网上的caffe的安装教程繁杂而散乱,对初学者很不友好,尤其对该框架理解不深的童鞋.总的来说,caffe的安装不外乎几个固定的步骤,对每一步有了一定的理解,安装只是time-consuming的问题!关键是自己要理解每一步是做什么的,最起码要有思维惯性.对Ubuntu活Redhat等,都是大同小异. [一]安装相关依赖包 1.编译工具等,如gcc(对版本有要求),git.python-pip等: 2.编译依赖包,如cuda/cudnn/hdf5等,具体参考官网: ps:若有sudo权限,需要下载

物体检测算法 SSD 的训练和测试

GitHub:https://github.com/stoneyang/caffe_ssd Paper: https://arxiv.org/abs/1512.02325 1. 安装 caffe_SSD: git clone https://github.com/weiliu89/caffe.git cd caffe git checkout ssd 2. 编译该 caffe 文件,在主目录下: # Modify Makefile.config according to your Caffe i

Jetson TX1 install py-faster-rcnn

Install py-faster-rcnn following the official version https://github.com/rbgirshick/py-faster-rcnn Build the Cython modules cd $FRCN_ROOT/lib make Errors: Traceback (most recent call last): File "setup.py", line 58, in <module> CUDA = loca

TRANSFORMING IMAGES TO FEATURE VECTORS

TRANSFORMING IMAGES TO FEATURE VECTORS I’m keen to explore some challenges in multimodal learning, such as jointly learning visual and textual semantics. However, I would rather not start by attempting to train an image recognition system from scratc

xmpp之配置Xcode（1）

介绍 ios上的XMPPFramework你能够在Xcode/iPhoneXMPP 目录找到,它只是实现了XMPP的一小部分功能. 下面主要介绍在开发XMPPFramework ios应用之前的配置工作. 你最好一步一步的来. 第一步 下载最新的XMPPFramework 代码.下载 第二步 XMPPFramework 有许多关联文件,它们已经内置在你抓下来的工程中,下面是它们的简单介绍. CocoaLumberjack    一个日志框架   第三步 CocoaAsyncSocket 轻量级的

SSD: Single Shot MultiBox Detector

By Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg. Introduction SSD is an unified framework for object detection with a single network. You can use the code to train/evaluate a network for o