-
-
Notifications
You must be signed in to change notification settings - Fork 16.1k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
How does YOLO perform with augmented data? #8504
Comments
👋 Hello @SanjayGhanagiri, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution. If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you. If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available. For business inquiries or professional support requests please visit https://ultralytics.com or email support@ultralytics.com. RequirementsPython>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started: git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install EnvironmentsYOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
StatusIf this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit. |
@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 HyperparametersThe hyperparameters used to define these augmentations are in your hyperparameter file (default
yolov5/data/hyps/hyp.scratch-low.yaml Lines 6 to 34 in b94b59e
Augmentation PreviewsYou 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 Albumentations IntegrationYOLOv5 🚀 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 Example Good luck 🍀 and let us know if you have any other questions! |
@glenn-jocher Thank you. Your answer was very informative. I have doubt from the augmented data.
|
@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. |
@glenn-jocher Thanks. Your comments were very helpful. |
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! 🚀 |
Search before asking
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?
Additional
No response
The text was updated successfully, but these errors were encountered: