convolutional neural network stanford

The article does not write clearly please forgive QaqIn this article we will make a very simple image classifier with the CIFAR-10 data set. The CIFAR-10 dataset contains 60,000 images. In this dataset, there are 10 different categories, with 6,000 images in each category. The size of each image is x 32 pixels. While such a small size often poses difficulties in identifying the right category for humans, it is actually a simplification of the computer model and reduces the computational complexi

An example of image recognition based on convolutional neural network is the preprocessing of input image in common use. Step1:resize STEP2: Go to mean value. It should be noted here that the average is calculated for all training sample images, and then the average is subtracted from each sample picture. The test picture is also subtracted from the mean when i

The development of Googlenet inception V1:The well-designed Inception Module in the Inception V1 improves the utilization of the parameters, Nception V1 removes the final fully connected layer of the model, using the global average pooling layer (which changes the image size to 1x1), in the previous network, The whole connection layer occupies most of the network parameters, it is easy to produce the phenom

This article is reproduced from: Http://www.cnblogs.com/lillylin/p/6204099.html xiangbai--"AAAI2017" textboxes:a Fast Text detector with A/single Deep neural network Catalog Authors and related link methods summarize innovation points and contribution methods summary of experimental results and harvesting points author and related link author Thesis downloads Lio Minghui, Shi, Baixiang, Wang Xinggang L