How does YOLO perform with augmented data?

[SanjayGhanagiri]

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Question

We see a lot of augmented data being used for many machine learning models. Of course model performs well when we provide around 1500 images per class which has realistic possible images in training set.

What happens when we provide augmented images which has black and white, colourised , rotated etc, these variations might not be present in a realistic scenario. Does this help model in accuracy?

In the below example it can be seen that original image of a parrot is far different from augmented versions. Does this help the model?

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[github-actions[bot]]

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[glenn-jocher]

@SanjayGhanagiri 👋 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

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!

[SanjayGhanagiri]

@glenn-jocher Thank you. Your answer was very informative. I have doubt from the augmented data.

1. From the albumentations data, will it not confuse the model if we use green parrot and train the model, where in reality the original image of the parrot is red?
2. YOLOv5 Training considers colour of the image right ?

[glenn-jocher]

@SanjayGhanagiri yes color is used by YOLOv5.

Some augmentation is useful to prevent overfitting and allow training to progress longer, but excess augmentation will hurt performance, it's up to you as the domain expert to put in place an appropriate augmentation strategy.

[SanjayGhanagiri]

@glenn-jocher Thanks. Your comments were very helpful.

[glenn-jocher]

You're welcome, @SanjayGhanagiri! I'm glad I could help. If you have any more questions or need further assistance, feel free to ask. Good luck with your YOLOv5 training! 🚀