Last Updated on July 5, 2019 There are discrete architectural elements from milestone models that you can use in the design of your own convolutional neural networks. Specifically, models that have achieved state-of-the-art results for tasks like image classification use discrete architecture elements repeated multiple times, such as the VGG block in the VGG models, the inception module in the GoogLeNet, and the residual module in the ResNet. Once you able to implement parameterized versions of these architecture elements, you […]

Last Updated on July 5, 2019 Pooling can be used to down sample the content of feature maps, reducing their width and height whilst maintaining their salient features. A problem with deep convolutional neural networks is that the number of feature maps often increases with the depth of the network. This problem can result in a dramatic increase in the number of parameters and computation required when larger filter sizes are used, such as 5×5 and 7×7. To address this […]

Last Updated on July 5, 2019 The rise in popularity and use of deep learning neural network techniques can be traced back to the innovations in the application of convolutional neural networks to image classification tasks. Some of the most important innovations have sprung from submissions by academics and industry leaders to the ImageNet Large Scale Visual Recognition Challenge, or ILSVRC. The ILSVRC is an annual computer vision competition developed upon a subset of a publicly available computer vision dataset […]

Last Updated on July 5, 2019 It is challenging to know how to best prepare image data when training a convolutional neural network. This involves both scaling the pixel values and use of image data augmentation techniques during both the training and evaluation of the model. Instead of testing a wide range of options, a useful shortcut is to consider the types of data preparation, train-time augmentation, and test-time augmentation used by state-of-the-art models that notably achieve the best performance […]

Last Updated on July 5, 2019 Deep learning neural networks are generally opaque, meaning that although they can make useful and skillful predictions, it is not clear how or why a given prediction was made. Convolutional neural networks, have internal structures that are designed to operate upon two-dimensional image data, and as such preserve the spatial relationships for what was learned by the model. Specifically, the two-dimensional filters learned by the model can be inspected and visualized to discover the […]

Last Updated on August 24, 2020 How to Develop a Convolutional Neural Network From Scratch for MNIST Handwritten Digit Classification. The MNIST handwritten digit classification problem is a standard dataset used in computer vision and deep learning. Although the dataset is effectively solved, it can be used as the basis for learning and practicing how to develop, evaluate, and use convolutional deep learning neural networks for image classification from scratch. This includes how to develop a robust test harness for […]

Last Updated on August 28, 2020 The Fashion-MNIST clothing classification problem is a new standard dataset used in computer vision and deep learning. Although the dataset is relatively simple, it can be used as the basis for learning and practicing how to develop, evaluate, and use deep convolutional neural networks for image classification from scratch. This includes how to develop a robust test harness for estimating the performance of the model, how to explore improvements to the model, and how […]

Last Updated on August 28, 2020 Discover how to develop a deep convolutional neural network model from scratch for the CIFAR-10 object classification dataset. The CIFAR-10 small photo classification problem is a standard dataset used in computer vision and deep learning. Although the dataset is effectively solved, it can be used as the basis for learning and practicing how to develop, evaluate, and use convolutional deep learning neural networks for image classification from scratch. This includes how to develop a […]

Last Updated on August 18, 2020 Deep convolutional neural network models may take days or even weeks to train on very large datasets. A way to short-cut this process is to re-use the model weights from pre-trained models that were developed for standard computer vision benchmark datasets, such as the ImageNet image recognition tasks. Top performing models can be downloaded and used directly, or integrated into a new model for your own computer vision problems. In this post, you will […]