glenn-jocher
commented
2 years ago @Wfast 👋 Hello! Thanks for asking about image augmentation. YOLOv5 🚀 applies online imagespace and colorspace augmentations in the trainloader (but not the val_loader) to present a new and unique augmented Mosaic (original image + 3 random images) each time an image is loaded for training. Images are never presented twice in the same way.
Augmentation Hyperparameters
The hyperparameters used to define these augmentations are in your hyperparameter file (default data/hyp.scratch.yaml) defined when training:
python train.py --hyp hyp.scratch-low.yamlAugmentation Previews
You can view the effect of your augmentation policy in your train_batch*.jpg images once training starts. These images will be in your train logging directory, typically yolov5/runs/train/exp:
train_batch0.jpg shows train batch 0 mosaics and labels:
YOLOv5 Albumentations Integration
YOLOv5 🚀 is now fully integrated with Albumentations, a popular open-source image augmentation package. Now you can train the world's best Vision AI models even better with custom Albumentations 😃!
PR https://github.com/ultralytics/yolov5/pull/3882 implements this integration, which will automatically apply Albumentations transforms during YOLOv5 training if albumentations>=1.0.3 is installed in your environment. See https://github.com/ultralytics/yolov5/pull/3882 for full details.
Example train_batch0.jpg on COCO128 dataset with Blur, MedianBlur and ToGray. See the YOLOv5 Notebooks to reproduce: 
Good luck 🍀 and let us know if you have any other questions!
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Question
this work good job, and i have a question about data augmentation. in my hpy.yaml, i set this hsv_h: 0.1 # image HSV-Hue augmentation (fraction) hsv_s: 1.5 # image HSV-Saturation augmentation (fraction) hsv_v: 1.5 # image HSV-Value augmentation (fraction)
i check the hsv function， whether this setting is correct? or the hsv has a range? thank you very much!
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