autoalbument VS YOLO-Mosaic

Compare autoalbument vs YOLO-Mosaic and see what are their differences.

autoalbument

AutoML for image augmentation. AutoAlbument uses the Faster AutoAugment algorithm to find optimal augmentation policies. Documentation - https://albumentations.ai/docs/autoalbument/ (by albumentations-team)

YOLO-Mosaic

Perform mosaic image augmentation on data for training a YOLO model (by arijitgupta42)
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autoalbument YOLO-Mosaic
1 1
135 3
3.0% -
1.1 3.0
5 months ago 10 months ago
Python Python
MIT License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

autoalbument

Posts with mentions or reviews of autoalbument. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-06-01.

YOLO-Mosaic

Posts with mentions or reviews of YOLO-Mosaic. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing autoalbument and YOLO-Mosaic you can also consider the following projects:

albumentations - Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

imgaug - Image augmentation for machine learning experiments.

imagezmq - A set of Python classes that transport OpenCV images from one computer to another using PyZMQ messaging.