The convolutional layer in convolutional neural networks systematically applies filters to an input and creates output feature maps. Although the convolutional layer is very simple, it is capable of achieving sophisticated and impressive results. Nevertheless, it can be challenging to develop an intuition for how the shape of the filters impacts the shape of the […]
The post A Gentle Introduction to Padding and Stride for Convolutional Neural Networks appeared first on Machine Learning Mastery.
Convolution and the convolutional layer are the major building blocks used in convolutional neural networks. A convolution is the simple application of a filter to an input that results in an activation. Repeated application of the same filter to an input results in a map of activations called a feature map, indicating the locations and […]
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Data augmentation is a technique often used to improve performance and reduce generalization error when training neural network models for computer vision problems. The image data augmentation technique can also be applied when making predictions with a fit model in order to allow the model to make predictions for multiple different versions of each image […]
The post How to Use Test-Time Augmentation to Improve Model Performance for Image Classification appeared first on Machine Learning Mastery.
Image data augmentation is a technique that can be used to artificially expand the size of a training dataset by creating modified versions of images in the dataset. Training deep learning neural network models on more data can result in more skillful models, and the augmentation techniques can create variations of the images that can […]
The post How to Configure Image Data Augmentation When Training Deep Learning Neural Networks appeared first on Machine Learning Mastery.
There are conventions for storing and structuring your image dataset on disk in order to make it fast and efficient to load and when training and evaluating deep learning models. Once structured, you can use tools like the ImageDataGenerator class in the Keras deep learning library to automatically load your train, test, and validation datasets. […]
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Deep Learning for Computer Vision Crash Course. Bring Deep Learning Methods to Your Computer Vision Project in 7 Days. We are awash in digital images from photos, videos, Instagram, YouTube, and increasingly live video streams. Working with image data is hard as it requires drawing upon knowledge from diverse domains such as digital signal processing, […]
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It can be convenient to use a standard computer vision dataset when getting started with deep learning methods for computer vision. Standard datasets are often well understood, small, and easy to load. They can provide the basis for testing techniques and reproducing results in order to build confidence with libraries and methods. In this tutorial, […]
The post How to Load and Visualize Standard Computer Vision Datasets With Keras appeared first on Machine Learning Mastery.
Color images have height, width, and color channel dimensions. When represented as three-dimensional arrays, the channel dimension for the image data is last by default, but may be moved to be the first dimension, often for performance-tuning reasons. The use of these two “channel ordering formats” and preparing data to meet a specific preferred channel […]
The post A Gentle Introduction to Channels First and Channels Last Image Formats for Deep Learning appeared first on Machine Learning Mastery.
The pixel values in images must be scaled prior to providing the images as input to a deep learning neural network model during the training or evaluation of the model. Traditionally, the images would have to be scaled prior to the development of the model and stored in memory or on disk in the scaled […]
The post How to Normalize, Center, and Standardize Images With the ImageDataGenerator in Keras appeared first on Machine Learning Mastery.
The Keras deep learning library provides a sophisticated API for loading, preparing, and augmenting image data. Also included in the API are some undocumented functions that allow you to quickly and easily load, convert, and save image files. These functions can be convenient when getting started on a computer vision deep learning project, allowing you […]
The post How to Load, Convert, and Save Images With the Keras API appeared first on Machine Learning Mastery.
