varying Scale of objects in detection

Akhp888 commented 2 years ago

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Question

Hello ,

My dataset has objects ranging from very tiny to 50X bigger , I see that the hyperparameter config file has a parameter "scale" . would like to know if playing around with it would yield me better results ?

Note : I tried with default 0.5 and there are quite a lot of objects in FN .

Thanks

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glenn-jocher commented 2 years ago

@Akhp888 👋 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!

Akhp888 commented 2 years ago

@glenn-jocher Thanks ,

so enabling Mosaic in hyperparameters adds objects of varying sizes ? Then i am curious to know what the "scale" does ? if there is any documentation for functionality of each variables in hyperparameter , would be great to know where i can find it .

MartinPedersenpp commented 2 years ago

https://medium.com/augmented-startups/how-hyperparameters-of-yolov5-works-ec4d25f311a2 These are the biggest ones

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Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

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glenn-jocher commented 11 months ago

@MartinPedersenpp thanks for sharing the link! The article seems to be a helpful resource for understanding YOLOv5 hyperparameters. As for your question about the "scale" hyperparameter, it controls the jitter of the image and grid sizes during training to allow some variation in object sizes and positions. You might tweak the scale for datasets with widely varying object sizes to permit better model understanding.

For all hyperparameters, including their functionality, you can refer to the official Ultralytics YOLOv5 documentation https://docs.ultralytics.com/yolov5/training-hyperrparameters. It comprehensively explains each hyperparameter and their effects during training.

Please let me know if you have any other questions or need further assistance!

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