Images Generator - мощный графический редактор, обладающий полным набором инструментов двухмерной графики. Кроме того присутствует возможность редактирования изображений, полученных с помощью фотокамер, сканеров или других программ. Images Generator может использоваться для создания элементов компьютерной графики, для оформления печатной продукции, создания рисунка тканей, выполнения технических рисунков, изделий рукоделия, эмблем, орнаментов, логотипов и др.
Отличительные особенности Images Generator:
- Наличие нескольких способов создания объемных изображений.
- Большое количество производных шаблонов формы.
- Градиентная заливка с учетом формы границ закрашиваемой области.
- Возможность использования растровых и векторных кистей.
- Наличие функций, обеспечивающих величину уровня прозрачности для каждой линии из их последовательности.
- Трансформация растровых изображений (может применяться например при обработке фотографий).
- Редактирование изображений, полученных с помощью фотокамер, сканеров или других программ.
Knowing that I was going to write a tutorial on data augmentation, two weekends ago I decided to have some fun and purposely post a semi-trick question on my Twitter feed. Instead, the ImageDataGenerator accepts the original data, randomly transforms it, and returns only the new, transformed data. Technically , all the answers are correct — but the only way you know if a given definition of data augmentation is correct is via the context of its application. Our goal when applying data augmentation is to increase the generalizability of the model. Given that our network is constantly seeing new, slightly modified versions of the input data, the network is able to learn more robust features. Training a machine learning model on this data may result in us modeling the distribution exactly — however, in real-world applications, data rarely follows such a nice, neat distribution. Instead, to increase the generalizability of our classifier, we may first randomly jitter points along the distribution by adding some random values drawn from a random distribution right.
Tutorial 26- Create Image Dataset using Data Augmentation using Keras-Deep Learning-Data Science:
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Images Generator tutorial:
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