Papers with Code 아카이브

Image Augmentation

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하위 태스크: 0
평가표: 1
보존된 평가 기록: 1

Image Augmentation is a data augmentation method that generates more training data from the existing training samples. Image Augmentation is especially useful in domains where training data is limited or expensive to obtain like in biomedical applications.

Source: Improved Image Augmentation for Convolutional Neural Networks by Copyout and CopyPairing

( Image credit: Kornia )

데이터셋

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Intel Image Classification

Context This is image data of Natural Scenes around the world. Content This Data contains around 25k images of size 150x150 distributed under 6 categories. {'buildings' -> 0, 'forest' -> 1, 'glacier' -> 2, 'mountain' -> 3, 'sea' -> 4, 'street' -> 5 } The Train, Test and Prediction data is separated in each zip files. There are around 14k images in Train, 3k in Test and 7k in Prediction. This data was initially published on https://datahack.analyticsvidhya.com by Intel to host a Image classification Challenge. Acknowledgements Thanks to https://datahack.analyticsvidhya.com for the challenge and Intel for the Data Photo by Jan Böttinger on Unsplash Inspiration Want to build powerful Neural network that can classify these images with more accuracy.

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.