caffe

Changing the solver parameters in Caffe through pycaffe

由 隐身守侯 提交于 2019-12-07 03:22:30
问题 How can I change the solver parameter in Caffe through pycaffe? E.g. right after calling solver = caffe.get_solver(solver_prototxt_filename) I would like to change the solver's parameters (learning rate, stepsize, gamma, momentum, base_lr, power, etc.), without having to change solver_prototxt_filename . 回答1: Maybe you can create a temporary file. First of all, load your solver parameters with from caffe.proto import caffe_pb2 from google.protobuf import text_format solver_config = caffe_pb2

What is average_loss field in Caffe solver for?

由 你离开我真会死。 提交于 2019-12-07 02:05:54
问题 What is the use for average_loss ? Would someone please give an example of it or explain it in layman's terms? 回答1: You can check in the caffe.proto file. Line 151 in the present version gives the following comment for average_loss: Display the loss averaged over the last average_loss iterations 来源： https://stackoverflow.com/questions/40190377/what-is-average-loss-field-in-caffe-solver-for

Multiple pretrained networks in Caffe

由 痴心易碎 提交于 2019-12-07 00:33:33
问题 Is there a simple way (e.g. without modifying caffe code) to load wights from multiple pretrained networks into one network? The network contains some layers with same dimensions and names as both pretrained networks. I am trying to achieve this using NVidia DIGITS and Caffe. EDIT : I thought it wouldn't be possible to do it directly from DIGITS, as confirmed by answers. Can anyone suggest a simple way to modify the DIGITS code to be able to select multiple pretrained networks? I checked the

IoU for semantic segmentation implementation in python/caffe per class

由 巧了我就是萌 提交于 2019-12-06 22:26:40
Is there any recommendable per class IoU(intersection over union) per pixel accuracy(different from bounding box) implementation.I am using caffe and managed to get the mean IoU but i am having difficulty in doing IoU for per class accuracy.I would appreciate a lot if someone could point out a good implementation in any language so far this the only close semantic segmentation with multiple pixel label i ve seen so far here 来源： https://stackoverflow.com/questions/44041096/iou-for-semantic-segmentation-implementation-in-python-caffe-per-class

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由 孤街浪徒 提交于 2019-12-06 21:11:28
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TensorFlow人工智能引擎入门教程之十二 Caffe转换tensorflow并 跨平台调用

由 耗尽温柔 提交于 2019-12-06 20:17:41
前面好多章节 讲了 人工智能引擎 tensorflow 怎么使用，如果是tensorflow 训练的模型 ，使用起来 很简单，前面章节锁了 saver 的使用，但是如果 是caffe 训练的模型 ，也没关系，所以 如果大家 使用caffe 与 使用tensorflow 都一样的。 随你喜欢。 首先我们进入caffe 官方github 下载 把相关的网络模型deploy.txt 文件以及 训练的最后模型文件caffemodel 下载下来。 然后使用 https://github.com/ethereon/caffe-tensorflow caffe-tensorflow 这个开源项目就好了 ，直接可以转换caffe 模型文件 为 tensorflow 模型参数 转换训练的模型 为 tensorflow 训练的模型文件格式，交给tensorflow调用，tensorflow 有C++ PYTHON 的api 而且支持手机端，各种平台，所以网上很多 吹牛逼的 项目手机上调用 ，手机上扫描显示，直接用tensorflow 调用即可，你可以使用tensorflow 来训练，也可以使用caffe来训练，caffe训练还是比tensorflow 暂用更少的内存，速度稍微快了那么一点点。 比如 我们前面 caffe 定义的 googleNet 该命令 可以把googleNet 的caffe

CMake Error: Could not create named generator Visual Studio 14 2015 win64

由 匆匆过客 提交于 2019-12-06 19:11:26
I want to use caffe deep learning. when I want to run caffe-windows\build_win I recive an error.what is problem? how can I resolve it? caffe is best for deep learning and I want to use it. I installed anaconda, visual studio 2015, cmake, cuda and I want to use caffe now. C:\Users\mohsen\Desktop\Propozal\DeepLearning\Install option for caffe\caffe-win dows>scripts\build_win The system cannot find the drive specified. The system cannot find the drive specified. INFO: ============================================================ INFO: Summary: INFO: ================================================

Caffe constant multiply layer

由 谁说胖子不能爱 提交于 2019-12-06 15:42:00
How can I define multiply constant layer in Caffe (like MulConstant in Torch). I need to add it predefined const to existing network. Caffe fails to parse my attempt to scale everything by 0.85: layers { name: "caffe.ConstantMul_0" type: "Eltwise" bottom: "caffe.SpatialConvolution_0" top: "caffe.ConstantMul_0" eltwise_param { op: MUL coeff: 0.85 } } It is possible to do with Power Layer , just set up power to 1 and scale to whatever you need: layer { name: "caffe.ConstantMul_1" bottom: "caffe.SpatialConvolution_3" top: "caffe.ConstantMul_1" type: "Power" power_param { power: 1 scale: 0.85

Caffe : train network accuracy = 1 constant ! Accuracy issue

由 好久不见. 提交于 2019-12-06 14:50:45
Right now, I am train network with with 2 class data... but accuracy is constant 1 after first iteration ! Input data is grayscale images. both class images are randomly selected when HDF5Data creation. Why is that happened ? What's wrong or where is mistake ! network.prototxt : name: "brainMRI" layer { name: "data" type: "HDF5Data" top: "data" top: "label" include: { phase: TRAIN } hdf5_data_param { source: "/home/shivangpatel/caffe/brainMRI1/train_file_location.txt" batch_size: 10 } } layer { name: "data" type: "HDF5Data" top: "data" top: "label" include: { phase: TEST } hdf5_data_param {

How do I get ILSVRC12 data in image format or how to create ilsvrc12_val_lmdb?

由 假如想象 提交于 2019-12-06 14:03:44
问题 I am trying to run imagenet example in Caffe. In this(https://github.com/BVLC/caffe/tree/master/examples/imagenet) page they say We assume that you already have downloaded the ImageNet training data and validation data, and they are stored on your disk like: /path/to/imagenet/train/n01440764/n01440764_10026.JPEG /path/to/imagenet/val/ILSVRC2012_val_00000001.JPEG Where do I find this data? 回答1: It's a bit of a process. 1. Got to imagenet's download page and select "Download Image URLs". 2.
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