mosaic augmentation

[joe660]

❔Question

Hello, use your own data set to train on yolov5. For some reasons, you need to turn off mosaic augmentation to get some important information. May I ask how much the removal of mosaic augmentation affects the performance of the model.

Additional context

Hello, use your own data set to train on yolov5. For some reasons, you need to turn off mosaic augmentation to get some important information. May I ask how much the removal of mosaic augmentation affects the performance of the model.

[github-actions[bot]]

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[github-actions[bot]]

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[AzharSindhi]

I think this is a good question, anyone has some insights?

[glenn-jocher]

@AzharSindhi in general training will perform worse without mosaic augmentation. Recommend you train with all default settings, including default mosaic setting.

[alevangel]

I would like to remove the mosaic augmentation, also the scaling augmentation. How can I do that?

[glenn-jocher]

@alevangel you can modify hyperparameters in your hyperparameters file: https://github.com/ultralytics/yolov5/blob/e96c74b5a1c4a27934c5d8ad52cde778af248ed8/data/hyps/hyp.scratch.yaml#L1-L34

[Tautvydas-byte]

Hello, How to check how much new images were created and gave to the model after augmentation ? For example I give 117 images and using default mosaic augmentation with default hyper-params.

[glenn-jocher]

@Tautvydas-byte 👋 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!

[Tautvydas-byte]

Thank you, for your fast answer. Speaking about augmentation what we see in the mosaic. Only these what we can see for example in the train_batch0.jpg is giving to model for every epoch or mosaic is changing in every epoch?

[glenn-jocher]

@Tautvydas-byte there's no such thing as two identical mosaics during training.