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

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  • The library is faster than other libraries on most of the transformations.
  • Based on numpy, OpenCV, imgaug picking the best from each of them.
  • Simple, flexible API that allows the library to be used in any computer vision pipeline.
  • Large, diverse set of transformations.
  • Easy to extend the library to wrap around other libraries.
  • Easy to extend to other tasks.
  • Supports transformations on images, masks, key points and bounding boxes.
  • Supports python 2.7-3.7
  • Easy integration with PyTorch.
  • Easy transfer from torchvision.
  • Was used to get top results in many DL competitions at Kaggle, topcoder, CVPR, MICCAI.
  • Written by Kaggle Masters.

How to use

All in one showcase notebook - showcase.ipynb

Classification - example.ipynb

Object detection - example_bboxes.ipynb

Non-8-bit images - example_16_bit_tiff.ipynb

Image segmentation example_kaggle_salt.ipynb

Custom tasks such as autoencoders, more then three channel images - refer to Compose class documentation to use additional_targets.

You can use this Google Colaboratory notebook to adjust image augmentation parameters and see the resulting images.






Alexander Buslaev

Alex Parinov

Vladimir I. Iglovikov

Evegene Khvedchenya



You can use pip to install albumentations:

pip install albumentations

If you want to get the latest version of the code before it is released on PyPI you can install the library from GitHub:

pip install -U git+


To install albumentations using conda we need first to install imgaug with pip

pip install imgaug
conda install albumentations -c albumentations


The full documentation is available at

Migrating from torchvision to albumentations

Migrating from torchvision to albumentations is simple - you just need to change a few lines of code. Albumentations has equivalents for common torchvision transforms as well as plenty of transforms that are not presented in torchvision. migrating_from_torchvision_to_albumentations.ipynb shows how one can migrate code from torchvision to albumentations.

Benchmarking results

To run the benchmark yourself follow the instructions in benchmark/

Results for running the benchmark on first 2000 images from the ImageNet validation set using an Intel Core i7-7800X CPU. The table shows how many images per second can be processed on a single core, higher is better.

torchvision (Pillow backend)
torchvision (Pillow-SIMD backend)
RandomCrop64 754387 6730 94557 97446 - 69562 7932
PadToSize512 7516 - 798 772 - - 3102
Resize512 2898 1272 379 1441 - 378 1822
HorizontalFlip 1093 1008 6475 5972 1093 6346 1154
VerticalFlip 11048 5429 7845 8213 10760 7677 3823
Rotate 1079 772 124 206 37 52 267
ShiftScaleRotate 2198 1223 107 184 40 - -
Brightness 772 884 425 563 199 425 134
Contrast 894 826 304 401 - 303 1028
BrightnessContrast 690 408 173 229 - 173 119
ShiftHSV 216 151 57 74 - - 142
ShiftRGB 728 884 - - 665 - -
Gamma 1151 - 1655 1692 - - 918
Grayscale 2710 509 1183 1515 - 2891 3872

Python and library versions: Python 3.6.8 | Anaconda, numpy 1.16.1, pillow 5.4.1, pillow-simd 5.3.0.post0, opencv-python, scikit-image 0.14.2, scipy 1.2.0.


  1. Clone the repository:
    git clone
    cd albumentations
  2. Install the library in development mode:
    pip install -e .[tests]
  3. Run tests:
  4. Run flake8 to perform PEP8 and PEP257 style checks and to check code for lint errors.

Building the documentation

  1. Go to docs/ directory
    cd docs
  2. Install required libraries
    pip install -r requirements.txt
  3. Build html files
    make html
  4. Open _build/html/index.html in browser.

Alternatively, you can start a web server that rebuilds the documentation automatically when a change is detected by running make livehtml


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



If you find this library useful for your research, please consider citing:

    author = {A. Buslaev, A. Parinov, E. Khvedchenya, V.~I. Iglovikov and A.~A. Kalinin},
     title = "{Albumentations: fast and flexible image augmentations}",
   journal = {ArXiv e-prints},
    eprint = {1809.06839},
      year = 2018      

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