https://github.com/albumentations-team/autoalbument

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

augmentation automated-machine-learning automl computer-vision deep-learning image-augmentation machine-learning pytorch

Last synced: about 9 hours ago
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AutoML for image augmentation. AutoAlbument uses the Faster AutoAugment algorithm to find optimal augmentation policies. Documentation - https://albumentations.ai/docs/autoalbument/

• Host: GitHub
• URL: https://github.com/albumentations-team/autoalbument
• Owner: albumentations-team
• License: mit
• Created: 2020-09-28T14:58:01.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2021-09-04T14:42:15.000Z (about 3 years ago)
• Last Synced: 2024-11-16T03:18:30.016Z (about 15 hours ago)
• Topics: augmentation, automated-machine-learning, automl, computer-vision, deep-learning, image-augmentation, machine-learning, pytorch
• Language: Python
• Homepage: https://albumentations.ai/docs/autoalbument/
• Size: 240 KB
• Stars: 203
• Watchers: 7
• Forks: 20
• Open Issues: 27
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# AutoAlbument

AutoAlbument is an AutoML tool that learns image augmentation policies from data using the [Faster AutoAugment algorithm](https://arxiv.org/abs/1911.06987). 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](https://github.com/albumentations-team/albumentations) to utilize policies discovered by AutoAlbument in your computer vision pipelines.

The documentation is available at [https://albumentations.ai/docs/autoalbument/](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](https://github.com/albumentations-team/autoalbument-benchmarks) repository.

### Classification

| Dataset  | Baseline Top-1 Accuracy | AutoAlbument Top-1 Accuracy  |

|----------|:-----------------------:|:----------------------------:|

| [CIFAR10](https://github.com/albumentations-team/autoalbument-benchmarks#cifar-10-classification)  |          91.79          |           **96.02**          |

| [SVHN](https://github.com/albumentations-team/autoalbument-benchmarks#svhn-classification)     |          98.31          |           **98.48**          |

| [ImageNet](https://github.com/albumentations-team/autoalbument-benchmarks#imagenet-classification) |          73.27          |           **75.17**          |

### Semantic segmentation

| Dataset    | Baseline mIOU | AutoAlbument mIOU |

|------------|:-------------:|:-----------------:|

| [Pascal VOC](https://github.com/albumentations-team/autoalbument-benchmarks#pascal-voc-semantic-segmentation) |     73.34     |     **75.55**     |

| [Cityscapes](https://github.com/albumentations-team/autoalbument-benchmarks#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

![How to use AutoAlbument](https://albumentations.ai/docs/images/autoalbument/how_to_use/autoalbument_usage.png)

1. 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.

2. AutoAlbument will use Generative Adversarial Network to discover augmentation policies and then create a file containing those policies.

3. Finally, you can use [Albumentations](https://github.com/albumentations-team/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/](https://albumentations.ai/docs/autoalbument/how_to_use/)

## Examples

The [`examples`](https://github.com/albumentations-team/autoalbument/tree/master/examples) directory contains example configs for different tasks and datasets:

### Classification

- [CIFAR10](https://github.com/albumentations-team/autoalbument/tree/master/examples/cifar10)

- [SVHN](https://github.com/albumentations-team/autoalbument/tree/master/examples/svhn)

- [ImageNet](https://github.com/albumentations-team/autoalbument/tree/master/examples/imagenet)

### Semantic segmentation

- [Pascal VOC](https://github.com/albumentations-team/autoalbument/tree/master/examples/pascal_voc)

- [Cityscapes](https://github.com/albumentations-team/autoalbument/tree/master/examples/cityscapes)

To run the search with an example config:

```

autoalbument-search --config-dir 

```