Is there a document defined for hyperparameters?

JeongJihong commented 2 years ago

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Question

I found that hyperparameters can be used for image augmentation. However, detailed explanation of each hyperparameter is required. I want to know the range of each hyperparameter value. And what kind of effect does that value have? Is there a document organized about hyperparameters? I was already looking for Hyperparameter Evolution # 607, but I don't have the information I want here.

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github-actions[bot] commented 2 years ago

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glenn-jocher commented 2 years ago

@JeongJihong 👋 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.

YOLOv5 augmentation

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.yaml

https://github.com/ultralytics/yolov5/blob/b94b59e199047aa8bf2cdd4401ae9f5f42b929e6/data/hyps/hyp.scratch-low.yaml#L6-L34

Augmentation 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!

JeongJihong commented 2 years ago

@glenn-jocher I want to change the values in 'hyp.scratch-low.yaml'. But I don't know what that value means. e.g. How are 'shear: 0.0' and 'shear: 0.5' different? And I don't know what that values range. e.g. Is 'shear: 10.0' possible? Is there any way to get the meaning of that value?

glenn-jocher commented 2 years ago

@JeongJihong flip up-down probability

dongjuns commented 2 years ago

@JeongJihong Hi, I think you could find what does parameters mean here,
https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml

and the range of the values is here.
https://github.com/ultralytics/yolov5/blob/9a7f289eedb489fa598dc7d3e1756c5b82b77e99/train.py#L570

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👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

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