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Fast image augmentation library and easy to use wrapper around other libraries

Project description

Albumentations

PyPI version CI

Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to increase the quality of trained models. The purpose of image augmentation is to create new training samples from the existing data.

Here is an example of how you can apply some augmentations from Albumentations to create new images from the original one: parrot

Why Albumentations

  • Albumentations supports all common computer vision tasks such as classification, semantic segmentation, instance segmentation, object detection, and pose estimation.
  • The library provides a simple unified API to work with all data types: images (RBG-images, grayscale images, multispectral images), segmentation masks, bounding boxes, and keypoints.
  • The library contains more than 70 different augmentations to generate new training samples from the existing data.
  • Albumentations is fast. We benchmark each new release to ensure that augmentations provide maximum speed.
  • It works with popular deep learning frameworks such as PyTorch and TensorFlow. By the way, Albumentations is a part of the PyTorch ecosystem.
  • Written by experts. The authors have experience both working on production computer vision systems and participating in competitive machine learning. Many core team members are Kaggle Masters and Grandmasters.
  • The library is widely used in industry, deep learning research, machine learning competitions, and open source projects.

Table of contents

Authors

Alexander Buslaev — Computer Vision Engineer at Mapbox | Kaggle Master

Alex Parinov — Computer Vision Architect at X5 Retail Group | Kaggle Master

Vladimir I. Iglovikov — Senior Computer Vision Engineer at Lyft Level5 | Kaggle Grandmaster

Evegene Khvedchenya — AI/ML Advisor and Independent researcher | Kaggle Master

Mikhail Druzhinin — Computer Vision Engineer at Simicon | Kaggle Expert

Installation

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

pip install -U albumentations

Other installation options are described in the documentation.

Documentation

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

A simple example

import albumentations as A
import cv2

# Declare an augmentation pipeline
transform = A.Compose([
    A.RandomCrop(width=256, height=256),
    A.HorizontalFlip(p=0.5),
    A.RandomBrightnessContrast(p=0.2),
])

# Read an image with OpenCV and convert it to the RGB colorspace
image = cv2.imread("image.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Augment an image
transformed = transform(image=image)
transformed_image = transformed["image"]

Getting started

I am new to image augmentation

Please start with the introduction articles about why image augmentation is important and how it helps to build better models.

I want to use Albumentations for the specific task such as classification or segmentation

If you want to use Albumentations for a specific task such as classification, segmentation, or object detection, refer to the set of articles that has an in-depth description of this task. We also have a list of examples on applying Albumentations for different use cases.

I want to know how to use Albumentations with deep learning frameworks

We have examples of using Albumentations along with PyTorch and TensorFlow.

I want to explore augmentations and see Albumentations in action

Check the online demo of the library. With it, you can apply augmentations to different images and see the result. Also, we have a list of all available augmentations and their targets.

Who is using Albumentations

See also:

List of augmentations

Pixel-level transforms

Pixel-level transforms will change just an input image and will leave any additional targets such as masks, bounding boxes, and keypoints unchanged. The list of pixel-level transforms:

Spatial-level transforms

Spatial-level transforms will simultaneously change both an input image as well as additional targets such as masks, bounding boxes, and keypoints. The following table shows which additional targets are supported by each transform.

Transform Image Masks BBoxes Keypoints
CenterCrop
CoarseDropout
Crop
CropNonEmptyMaskIfExists
ElasticTransform
Flip
GridDistortion
GridDropout
HorizontalFlip
IAAAffine
IAACropAndPad
IAAFliplr
IAAFlipud
IAAPerspective
IAAPiecewiseAffine
Lambda
LongestMaxSize
MaskDropout
NoOp
OpticalDistortion
PadIfNeeded
RandomCrop
RandomCropNearBBox
RandomGridShuffle
RandomResizedCrop
RandomRotate90
RandomScale
RandomSizedBBoxSafeCrop
RandomSizedCrop
Resize
Rotate
ShiftScaleRotate
SmallestMaxSize
Transpose
VerticalFlip

A few more examples of augmentations

Semantic segmentation on the Inria dataset

inria

Medical imaging

medical

Object detection and semantic segmentation on the Mapillary Vistas dataset

vistas

Keypoints augmentation

Benchmarking results

To run the benchmark yourself, follow the instructions in benchmark/README.md

Results for running the benchmark on the first 2000 images from the ImageNet validation set using an Intel Xeon Gold 6140 CPU. All outputs are converted to a contiguous NumPy array with the np.uint8 data type. The table shows how many images per second can be processed on a single core; higher is better.

albumentations
<small>0.5.0</small>
imgaug
<small>0.4.0</small>
torchvision (Pillow-SIMD backend)
<small>0.7.0</small>
keras
<small>2.4.3</small>
augmentor
<small>0.2.8</small>
solt
<small>0.1.9</small>
HorizontalFlip 9909 2821 2267 873 2301 6223
VerticalFlip 4374 2218 1952 4339 1968 3562
Rotate 371 296 163 27 60 345
ShiftScaleRotate 635 437 147 28 - -
Brightness 2751 1178 419 229 418 2300
Contrast 2756 1213 352 - 348 2305
BrightnessContrast 2738 699 195 - 193 1179
ShiftRGB 2757 1176 - 348 - -
ShiftHSV 597 284 58 - - 137
Gamma 2844 - 382 - - 946
Grayscale 5159 428 709 - 1064 1273
RandomCrop64 175886 3018 52103 - 41774 20732
PadToSize512 3418 - 574 - - 2874
Resize512 1003 634 1036 - 1016 977
RandomSizedCrop_64_512 3191 939 1594 - 1529 2563
Posterize 2778 - - - - -
Solarize 2762 - - - - -
Equalize 644 413 - - 735 -
Multiply 2727 1248 - - - -
MultiplyElementwise 118 209 - - - -
ColorJitter 368 78 57 - - -

Python and library versions: Python 3.8.6 (default, Oct 13 2020, 20:37:26) [GCC 8.3.0], numpy 1.19.2, pillow-simd 7.0.0.post3, opencv-python 4.4.0.44, scikit-image 0.17.2, scipy 1.5.2.

Contributing

To create a pull request to the repository, follow the documentation at https://albumentations.ai/docs/contributing/

Comments

In some systems, in the multiple GPU regime, PyTorch may deadlock the DataLoader if OpenCV was compiled with OpenCL optimizations. Adding the following two lines before the library import may help. For more details https://github.com/pytorch/pytorch/issues/1355

cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)

Citing

If you find this library useful for your research, please consider citing Albumentations: Fast and Flexible Image Augmentations:

@Article{info11020125,
    AUTHOR = {Buslaev, Alexander and Iglovikov, Vladimir I. and Khvedchenya, Eugene and Parinov, Alex and Druzhinin, Mikhail and Kalinin, Alexandr A.},
    TITLE = {Albumentations: Fast and Flexible Image Augmentations},
    JOURNAL = {Information},
    VOLUME = {11},
    YEAR = {2020},
    NUMBER = {2},
    ARTICLE-NUMBER = {125},
    URL = {https://www.mdpi.com/2078-2489/11/2/125},
    ISSN = {2078-2489},
    DOI = {10.3390/info11020125}
}

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