ã€æŒ‘戦者求ム】ã¼ãã®è€ƒãˆã‚‹æœ€é€Ÿã®Data Loadingã¨Data Augmentation（Kaggle notebook） - Qiita
ã¯ã˜ã‚ã« ã¾ãタイトルã®é€šã‚Šãªã®ã§ã™ãŒã€Kaggle notebook上ã§è¡Œã†æœ€é€Ÿã®Data Loadingã¨Data Augmentationを考ãˆã¦ã¿ãŸã®ã§ç´¹ä»‹ã—ã¾ã™ã€‚より速ã„方法を知ã£ã¦ã„ã‚‹æ–¹ã¯æ•™ãˆã¦ãã ã•ã„ï¼ ä»Šå›žã®é¡Œæã¯ä»¥ä¸‹ã®ã‚ˆã†ã«è¨å®šã—ã¾ã™ã€‚ データ ã“ã¡ã‚‰ã®ã‚³ãƒ³ãƒšã®ãƒ‡ãƒ¼ã‚¿ã‚’使ã„ã¾ã™ã€‚10.2kæžšã®jpgå½¢å¼ã®çŠ¬ã®ç”»åƒã§ã™ã€‚ https://www.kaggle.com/c/dog-breed-identification/data 実行環境 GPUã‚’enableã«ã—ãŸKaggle notebookã§è¡Œã„ã¾ã™ã€‚ 2 CPU cores 13 GB RAM Tesla P100 æ¡ä»¶ trainデータ(ç”»åƒã¨ãƒ©ãƒ™ãƒ«)ã‚’ã™ã¹ã¦Tensorã«ã—ã¦GPUã«Loadã™ã‚‹ã®ã«ã‹ã‹ã‚‹æ™‚間を計測ã™ã‚‹ ãƒãƒƒãƒã‚µã‚¤ã‚ºã¯64 å‰å‡¦ç† & Data Augmentationã¨ã—ã¦ä»¥ä¸‹ã®å‡¦ç†ã‚’ã‹ã‘る。（
Learning Data Augmentation Strategies for Object Detection
Data augmentation is a critical component of training deep learning models. Although data augmentation has been shown to significantly improve image classification, its potential has not been thoroughly investigated for object detection. Given the additional cost for annotating images for object detection, data augmentation may be of even greater importance for this computer vision task. In this w
What are possible values for data_augmentation_options in the TensorFlow Object Detection pipeline configuration?
The list of options is provided in preprocessor.proto: NormalizeImage normalize_image = 1; RandomHorizontalFlip random_horizontal_flip = 2; RandomPixelValueScale random_pixel_value_scale = 3; RandomImageScale random_image_scale = 4; RandomRGBtoGray random_rgb_to_gray = 5; RandomAdjustBrightness random_adjust_brightness = 6; RandomAdjustContrast random_adjust_contrast = 7; RandomAdjustHue random_ad
GitHub - aleju/imgaug: Image augmentation for machine learning experiments.
Many augmentation techniques E.g. affine transformations, perspective transformations, contrast changes, gaussian noise, dropout of regions, hue/saturation changes, cropping/padding, blurring, ... Optimized for high performance Easy to apply augmentations only to some images Easy to apply augmentations in random order Support for Images (full support for uint8, for other dtypes see documentation)
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GitHub - conradry/copy-paste-aug: Copy-paste augmentation for segmentation and detection tasks
Learn to Augment: Joint Data Augmentation and Network Optimization for Text Recognition
GitHub - kakaobrain/fast-autoaugment: Official Implementation of 'Fast AutoAugment' in PyTorch.
A Survey on Face Data Augmentation
GitHub - albumentations-team/albumentations: Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
GitHub - petrosgk/Kaggle-Carvana-Image-Masking-Challenge: Solution based on U-Net for the Kaggle Carvana Image Masking Challenge
GitHub - mdbloice/Augmentor: Image augmentation library in Python for machine learning.
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