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---
title: README
emoji: 👁
colorFrom: red
colorTo: yellow
sdk: static
pinned: false
license: mit
---
# Albumentations
Efficient Image Augmentation for Machine Learning in Python
[Albumentations](https://albumentations.ai/) is a fast, flexible image augmentation library designed for machine learning practitioners working on computer vision tasks. Our aim is simple: provide a comprehensive set of tools that can transform any image to augment your datasets, thereby improving model accuracy and robustness.
Features:
- [Wide Range of Augmentations](https://albumentations.ai/docs/api_reference/full_reference/): Supports geometric transforms, color augmentations, flips, rotations, and more, tailored for classification, segmentation, object detection, and working with key points.
- [Easy Integration](https://albumentations.ai/docs/#examples): Designed to easily fit into any machine learning pipeline.
- [Performance Optimized](https://albumentations.ai/docs/benchmarking_results/): Minimizes CPU/GPU load with efficient implementation.
- Community Driven: Open to contributions and feedback. We evolve with your needs.
```python
from albumentations import (
HorizontalFlip, Affine, CLAHE, RandomRotate90,
Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,
GaussNoise, MotionBlur, MedianBlur,
RandomBrightnessContrast, Flip, OneOf, Compose
)
import numpy as np
def strong_aug(p=0.5):
return Compose([
RandomRotate90(),
Flip(),
Transpose(),
GaussNoise(),
OneOf([
MotionBlur(p=0.2),
MedianBlur(blur_limit=3, p=0.1),
Blur(blur_limit=3, p=0.1),
], p=0.2),
Affine(translate_percent=0.0625, scale=(0.8, 1.2), rotate_limit=(-45, 45), p=0.2),
OneOf([
OpticalDistortion(p=0.3),
GridDistortion(p=0.1)
], p=0.2),
OneOf([
CLAHE(clip_limit=2),
RandomBrightnessContrast(),
], p=0.3),
HueSaturationValue(p=0.3),
], p=p)
image = np.ones((300, 300, 3), dtype=np.uint8)
mask = np.ones((300, 300), dtype=np.uint8)
whatever_data = "my name"
augmentation = strong_aug(p=0.9)
data = {"image": image, "mask": mask, "whatever_data": whatever_data, "additional": "hello"}
augmented = augmentation(**data)
image, mask, whatever_data, additional = augmented["image"], augmented["mask"], augmented["whatever_data"], augmented["additional"]
```
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