caffe.berkeleyvision.org
Caffe | CaffeNet C++ Classification example
http://caffe.berkeleyvision.org/gathered/examples/cpp_classification.html
Deep learning framework by the BVLC. Classifying ImageNet: using the C API. Caffe, at its core, is written in C . It is possible to use the C API of Caffe to implement an image classification application similar to the Python code presented in one of the Notebook examples. To look at a more general-purpose example of the Caffe C API, you should study the source code of the command line tool. A simple C code is proposed in. In your build directory. The ImageNet labels file (also called the. Build/examples...
computervisionblog.com
Tombone's Computer Vision Blog: Deep Learning vs Big Data: Who owns what?
http://www.computervisionblog.com/2015/05/deep-learning-vs-big-data-who-owns-what.html
Tombone's Computer Vision Blog. Deep Learning, Computer Vision, and the algorithms that are shaping the future of Artificial Intelligence. Tuesday, May 05, 2015. Deep Learning vs Big Data: Who owns what? In order to learn anything useful, large-scale multi-layer deep neural networks (aka Deep Learning systems) require a large amount of labeled data. There is clearly a need for big data, but only a few places where big visual data. In other words, when deep learning touches your data, who owns what. Also ...
joelouismarino.github.io
Blog - GoogLeNet in Keras
http://joelouismarino.github.io/blog_posts/blog_googlenet_keras.html
Joe Marino - June 2016. In this new era of deep learning, a number of software libraries have cropped up, each promising users speed, ease of use, and compatibility with state-of-the-art models and techniques. The go-to library in the Caltech vision lab has been Caffe. An open-source library developed by Yangquing Jia. And maintained by the Berkeley Vision and Learning Center (BVLC). A python library developed by François Chollet. That runs on top of Theano. Download the Keras Model. Before we begin, if ...
caffe.berkeleyvision.org
Caffe | Siamese Network Tutorial
http://caffe.berkeleyvision.org/gathered/examples/siamese.html
Deep learning framework by the BVLC. Siamese Network Training with Caffe. This example shows how you can use weight sharing and a contrastive loss function to learn a model using a siamese network in Caffe. We will assume that you have caffe successfully compiled. If not, please refer to the Installation page. This example builds on the MNIST tutorial. So it would be a good idea to read that before continuing. Data/mnist/get mnist.sh ./examples/siamese/create mnist siamese.sh. Define the Siamese Network.
petewarden.com
Why GEMM is at the heart of deep learning « Pete Warden's blog
https://petewarden.com/2015/04/20/why-gemm-is-at-the-heart-of-deep-learning
Pete Warden's blog. Ever tried. Ever failed. No matter. Try Again. Fail again. Fail better. Why GEMM is at the heart of deep learning. April 20, 2015. Photo by Anthony Catalano. I spend most of my time worrying about how to make deep learning with neural networks faster and more power efficient. In practice that means focusing on a function called GEMM. It’s part of the BLAS (Basic Linear Algebra Subprograms) library that was first created in 1979. So what is GEMM? Fully-connected layers are the classic ...
petewarden.com
2015 April « Pete Warden's blog
https://petewarden.com/2015/04
Pete Warden's blog. Ever tried. Ever failed. No matter. Try Again. Fail again. Fail better. Monthly Archives: April 2015. Why you should visit the Living Computer Museum in Seattle. April 26, 2015. I’d never heard of the Living Computer Museum. But what I found was a lot more interesting. As soon as I walked in, the first exhibit I saw was a PDP-7. With a teletype attached that I could actually play with! I had fun playing through Oregon Trail, until one of my party contracted typhoid and I decided that ...
image-net.org
ImageNet Large Scale Visual Recognition Challenge Tutorial
http://image-net.org/tutorials/cvpr2015
Large Scale Visual Recognition Challenge Tutorial. June 9, 2015: All slides are now online. June 7, 2015: Tutorial takes place. May 5, 2015: Schedule posted. January 8, 2015: Organization of the ImageNet Large Scale Visual Recognition Challenge Tutorial is underway. This half-day tutorial will focus on providing a high-level overview of the challenge, with the goal of sharing some of the lessons learned by the organizers and participants in the past five years. Concretely, we aim to:. University of Michi...
