image classification tensorflow

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On the Imagenet Image Classification Challenge, Alex proposed the Alexnet network structure model won the 2012-term championship. In order to study the application of the CNN type DL network model in image classification, we can not escape the research alexnet, which is the classic model of CNN in

each feature point (highlighted in Blue Circle in Figure 1 ). this obtains the "descriptor" layer. then, a codebook with mentries is applied to quantize each Descriptor and generate the "code" layer, where each descriptor is converted into anrmcode (highlighted in green circle ). if hard Vector Quantization (VQ) is used, each code has only one non-zero element, while for soft-VQ, a small group of elements can be non-zero. next in the "SPM" layer, multiple codes from inside each sub-region are p

First, IntroductionVgg and googlenet are the double males of the 2014 Imagenet race, and the two types of model structures have a common feature of Go deeper. Unlike Googlenet, Vgg inherits some of the lenet and alexnet frameworks, especially the alexnet frame, Vgg is also a convolution of 5 group, 2-Layer FC image feature, a layer FC classification feature, Can be seen as a total of 8 part as alexnet. Base

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Original URL: http://blog.csdn.net/sunbaigui/article/details/39938097 On the Imagenet Image Classification challenge the Alexnet network structure model which Alex proposed has won the 2012 championship. To study the application of the CNN type DL network model to the image classification, we can't escape the researc

This document describes: If you have trained a caffe network, how to use this network for image classification. The following is an example of a mnist network. Mnist is used to classify handwritten numerals 0-9. When the user has written a number, the image enters the Mnist network, and then the network calculates the probability of each number, which is consider

Match settings: 1000 categories of image classification problems, training data set 1.26 million images, validation set 50,000, test set 100,000 (callout not advertised). The data set is used by the 2012,2013,2014. The evaluation standard uses the TOP-5 error rate, that is, to predict an image 5 categories, as long as there is one and the same as the manual label

Atitit Classification of mixed folders based on image images and documentsdocuments that are too small ( txt doc csv exl ppt pptx ) Separate categories Mov10kminidocBut there may be some calligraphy pictures migrated because they are tiny and need to be listed separatelytoo small a junk file all migrations below 10kb, not in document format: In addition,JS, CSS, GIF format are all migrated. /atiplatf_cms/sr