Learn how to use data augmentation to improve your CNN image classification models. Avoid common pitfalls and follow best practices for choosing, applying, evaluating, and optimizing data ...

The article demonstrates how to do data augmentation to increase the size of the data. We will first build a deep learning model without performing augmentation and will compute the accuracy. After ...

Building data input pipelines using the tf.keras.preprocessing.image.ImageDataGenerator class to efficiently work with data on disk to use with the model. Overfitting —How to identify and prevent it.

In this project , we will look into one such image classification problem namely Flower Species Recognition, which is a hard problem because there are millions of flower species around the world. As ...

Then, through randomly sampling the coefficient in the data mixture model, we obtain several independent classifiers and fuse them with a voting strategy to produce the final classification results.

Data augmentation is the process of generating samples by transforming training data, with the target of improving the accuracy and robustness of classifiers. In this paper, we propose a new automatic ...

This study presents a non-invasive approach to detect anxiety and depression through gait analysis and machine learning, ...

Cibaca continues to explore image classification and deep learning from a research-first perspective. Her ongoing work ...

Perez, L. and Wang, J. (2017) The Effectiveness of Data Augmentation in Image Classification Using Deep Learning. ArXiv: 1712.04621. has been cited by the following article: TITLE: Building Detection ...