Package Health Score

39 / 100

security
Security review needed
popularity
Limited
maintenance
Inactive
community
Limited

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Popularity

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GitHub Stars: 199
Forks: 20
Contributors: 1

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package autoalbument, we found that it has been starred 199 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Feb 21, 2021
Direct Vulnerabilities: 0
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Indirect Vulnerabilities: 0
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License Risk: 0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

MIT

Security Policy

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Maintenance

Inactive

Commit Frequency

No Recent Commits

Open Issues: 26
Open PR: 1
Last Release: 4 years ago
Last Commit: 4 years ago

Further analysis of the maintenance status of autoalbument based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for autoalbument is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Limited

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 1
Funding: No

This project has seen only 10 or less contributors.

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Package

Python Versions Compatibility: ==3.6.*, ==3.7.*, ==3.8.*

Age: 4 years
Latest Release: 4 years ago
Dependencies: 11 Direct / 62 Total
Versions: 10
Maintainers: 1
Wheels: OS Independent

Readme

AutoAlbument

AutoAlbument is an AutoML tool that learns image augmentation policies from data using the Faster AutoAugment algorithm. It relieves the user from the burden of manually selecting augmentations and tuning their parameters. AutoAlbument provides a complete ready-to-use configuration for an augmentation pipeline.

The library supports image classification and semantic segmentation tasks. You can use Albumentations to utilize policies discovered by AutoAlbument in your computer vision pipelines.

The documentation is available at https://albumentations.ai/docs/autoalbument/

Benchmarks

Here is a comparison between a baseline augmentation strategy and an augmentation policy discovered by AutoAlbument for different classification and semantic segmentation tasks. You can read more about these benchmarks in the autoalbument-benchmarks repository.

Classification

Dataset	Baseline Top-1 Accuracy	AutoAlbument Top-1 Accuracy
CIFAR10	91.79	96.02
SVHN	98.31	98.48
ImageNet	73.27	75.17

Semantic segmentation

Dataset	Baseline mIOU	AutoAlbument mIOU
Pascal VOC	73.34	75.55
Cityscapes	79.47	79.92

Installation

AutoAlbument requires Python 3.6 or higher. To install the latest stable version from PyPI:

pip install -U autoalbument

How to use AutoAlbument

You need to create a configuration file with AutoAlbument parameters and a Python file that implements a custom PyTorch Dataset for your data. Next, you need to pass those files to AutoAlbument.
AutoAlbument will use Generative Adversarial Network to discover augmentation policies and then create a file containing those policies.
Finally, you can use Albumentations to load augmentation policies from the file and utilize them in your computer vision pipelines.

You can read the detailed description of all steps at https://albumentations.ai/docs/autoalbument/how_to_use/

Examples

The examples directory contains example configs for different tasks and datasets:

Classification

Semantic segmentation

To run the search with an example config:

autoalbument-search --config-dir

autoalbument dependencies

albumentations click colorama hydra-core pytorch-lightning ruamel-yaml segmentation-models-pytorch tensorboard timm torch tqdm

FAQs

What is autoalbument?

AutoML for image augmentation. Visit Snyk Advisor to see a full health score report for autoalbument, including popularity, security, maintenance & community analysis.

Is autoalbument popular?

The python package autoalbument receives a total of 168 weekly downloads. As such, autoalbument popularity was classified as limited. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is autoalbument well maintained?

We found indications that autoalbument is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is autoalbument safe to use?

While scanning the latest version of autoalbument, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 2 November-2024, at 03:30 (UTC).

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autoalbument

AutoML for image augmentation For more information about how to use this package see README

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Package

Readme

AutoAlbument

Benchmarks

Classification

Semantic segmentation

Installation

How to use AutoAlbument

Examples

Classification

Semantic segmentation

autoalbument dependencies

FAQs

What is autoalbument?

Is autoalbument popular?

Is autoalbument well maintained?

Is autoalbument safe to use?

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Readme

AutoAlbument

Benchmarks

Classification

Semantic segmentation

Installation

How to use AutoAlbument

Examples

Classification

Semantic segmentation

autoalbument dependencies

FAQs

What is autoalbument?

Is autoalbument popular?

Is autoalbument well maintained?

Is autoalbument safe to use?

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Source Code

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