A question about augmentation yolov5/utils/augmentations.py and yolov5/data/hyp.*.yaml files

[frabob2017]

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Question

I check the yolov5/utils/augmentations.py. It looks that by default, there is a rotation of 10 degree for data augmentation. However, in yolov5/data/hyp.*.yaml files, I found that the rotation degree is 0 degree. In this situation, which one determine rotation will be used for augmentation? If I want to change rotation degree to 20 degree, do I need to change yolov5/utils/augmentations.py to set degrees=20 or how can I change it in command line?

def random_perspective(im,
targets=(),
segments=(),
degrees=10,
translate=.1,
scale=.1,
shear=10,
perspective=0.0,
border=(0, 0)):

Additional

No response

[glenn-jocher]

@frabob2017 hyp file sets rotation, yes you can just change to 20 deg and it will apply to training.

[frabob2017]

@frabob2017 hyp file sets rotation, yes you can just change to 20 deg and it will apply to training.

There are multiple yolov5/data/hyp.*.yaml files, which one should I use? Does it depend on the model I use yolo5s, yolo5m, yolo5x etc? Now I use my own pre-trained weight. If I prefer to use yolov5/utils/augmentations.py? How to use this? I just set degrees=20 directly in this file?

[glenn-jocher]

You can see the default hyp file (low) used in train.py argparser. Higher augmentation reduces overfitting.

[frabob2017]

You can see the default hyp file (low) used in train.py argparser. Higher augmentation reduces overfitting.

Is there any tutorials how to use yolov5/utils/augmentations.py, it seems that you recommend to use yolov5/data/hyp.*.yaml.

[glenn-jocher]

👋 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.yaml

yolov5/data/hyps/hyp.scratch-low.yaml

Lines 6 to 34 in b94b59e

	lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
	lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
	momentum: 0.937 # SGD momentum/Adam beta1
	weight_decay: 0.0005 # optimizer weight decay 5e-4
	warmup_epochs: 3.0 # warmup epochs (fractions ok)
	warmup_momentum: 0.8 # warmup initial momentum
	warmup_bias_lr: 0.1 # warmup initial bias lr
	box: 0.05 # box loss gain
	cls: 0.5 # cls loss gain
	cls_pw: 1.0 # cls BCELoss positive_weight
	obj: 1.0 # obj loss gain (scale with pixels)
	obj_pw: 1.0 # obj BCELoss positive_weight
	iou_t: 0.20 # IoU training threshold
	anchor_t: 4.0 # anchor-multiple threshold
	# anchors: 3 # anchors per output layer (0 to ignore)
	fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
	hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
	hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
	hsv_v: 0.4 # image HSV-Value augmentation (fraction)
	degrees: 0.0 # image rotation (+/- deg)
	translate: 0.1 # image translation (+/- fraction)
	scale: 0.5 # image scale (+/- gain)
	shear: 0.0 # image shear (+/- deg)
	perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
	flipud: 0.0 # image flip up-down (probability)
	fliplr: 0.5 # image flip left-right (probability)
	mosaic: 1.0 # image mosaic (probability)
	mixup: 0.0 # image mixup (probability)
	copy_paste: 0.0 # segment copy-paste (probability)

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 #3882 implements this integration, which will automatically apply Albumentations transforms during YOLOv5 training if albumentations>=1.0.3 is installed in your environment. See #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!

[github-actions]

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