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albumentations | Fast image augmentation library and an easy-to-use wrapper | Computer Vision library

 by   albumentations-team Python Version: 1.3.0 License: MIT

 by   albumentations-team Python Version: 1.3.0 License: MIT

kandi X-RAY | albumentations Summary

albumentations is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Computer Vision, Deep Learning, Tensorflow applications. albumentations has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can install using 'pip install albumentations' or download it from GitHub, PyPI.
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.
Support
Support
Quality
Quality
Security
Security
License
License
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Reuse

kandi-support Support

  • albumentations has a highly active ecosystem.
  • It has 11445 star(s) with 1461 fork(s). There are 124 watchers for this library.
  • There were 1 major release(s) in the last 6 months.
  • There are 322 open issues and 408 have been closed. On average issues are closed in 54 days. There are 20 open pull requests and 0 closed requests.
  • It has a negative sentiment in the developer community.
  • The latest version of albumentations is 1.3.0
albumentations Support
Best in #Computer Vision
Average in #Computer Vision
albumentations Support
Best in #Computer Vision
Average in #Computer Vision

quality kandi Quality

  • albumentations has 0 bugs and 0 code smells.
albumentations Quality
Best in #Computer Vision
Average in #Computer Vision
albumentations Quality
Best in #Computer Vision
Average in #Computer Vision

securitySecurity

  • albumentations has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • albumentations code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
albumentations Security
Best in #Computer Vision
Average in #Computer Vision
albumentations Security
Best in #Computer Vision
Average in #Computer Vision

license License

  • albumentations is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
albumentations License
Best in #Computer Vision
Average in #Computer Vision
albumentations License
Best in #Computer Vision
Average in #Computer Vision

buildReuse

  • albumentations releases are available to install and integrate.
  • Deployable package is available in PyPI.
  • Build file is available. You can build the component from source.
  • Installation instructions, examples and code snippets are available.
  • albumentations saves you 4443 person hours of effort in developing the same functionality from scratch.
  • It has 12056 lines of code, 1135 functions and 52 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
albumentations Reuse
Best in #Computer Vision
Average in #Computer Vision
albumentations Reuse
Best in #Computer Vision
Average in #Computer Vision
Top functions reviewed by kandi - BETA

kandi has reviewed albumentations and discovered the below as its top functions. This is intended to give you an instant insight into albumentations implemented functionality, and help decide if they suit your requirements.

  • Transform an image using elastic transformation
    • Generates a random number
    • Generate a random distribution
    • Return a function to process image chunks
  • Return the parameters dependent on the image
    • Order the points according to the x coordinate system
    • Expand a transformation matrix
    • Draw a normal distribution
  • Adjust contrast of the contrast of the image
    • Make a table of transformers
      • Evaluate a cv2 image
        • Return the params dependent on the image
          • Get params dependent on target
            • Get installed package versions
              • Apply elastic transformation to a bounding box
                • Handle translate argument
                  • Returns information about the transforms
                    • Get parameters dependent on target
                      • Load a transform from a file
                        • Generate parameters dependent on target
                          • Adjusts the size of the pad if needed
                            • Returns a dictionary of parameters dependent on the image
                              • Return parameters dependent on target image
                                • Check for outdated styles
                                  • Update params
                                    • Filter bounding boxes by visibility

                                      Get all kandi verified functions for this library.

                                      Get all kandi verified functions for this library.

                                      albumentations Key Features

                                      Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

                                      albumentations Examples and Code Snippets

                                      See all related Code Snippets

                                      Community Discussions

                                      Trending Discussions on albumentations
                                      • Image augmentation on deep learning training data
                                      • Normalization before and after Albumentations augmentations?
                                      • Colab: (0) UNIMPLEMENTED: DNN library is not found
                                      • How resize dataset label in albumentations label to work with tensorflow image_dataset_from_directory function?
                                      • colab notebook in Chapter 3 of Underactuated Robotics is not working
                                      • How to make conda use its own gcc version?
                                      • Augmentation using Albumentations in Pytorch OD
                                      • Multipoint(df['geometry']) key error from dataframe but key exist. KeyError: 13 geopandas
                                      • pytorch model predicts fixed label when it exports to onnx
                                      • using ImageFolder with albumentations in pytorch
                                      Trending Discussions on albumentations

                                      QUESTION

                                      Image augmentation on deep learning training data

                                      Asked 2022-Mar-15 at 09:41

                                      I have a question about mean and standard deviation in image augmentation.

                                      Are the two parameters recommended to be filled in?

                                      If so, how could I know the number? Do I have to iterate through the data, also each channel of image, before the train to get it?

                                      import albumentations as A
                                      train_transform = A.Compose(
                                              [
                                                  A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),
                                                  A.ColorJitter(brightness=0.3, hue=0.3, p=0.3),
                                                  A.Rotate(limit=5, p=1.0),
                                                  # A.HorizontalFlip(p=0.3),
                                                  # A.VerticalFlip(p=0.2),
                                                  A.Normalize(
                                                      mean=[0.0, 0.0, 0.0],# <-----------this parameter
                                                      std=[1.0, 1.0, 1.0],# <-----------this parameter
                                                      max_pixel_value=255.0,
                                                  ),
                                                  ToTensorV2(),
                                              ],
                                          )
                                      

                                      ANSWER

                                      Answered 2022-Mar-14 at 22:43

                                      Yes it is strongly recommended to normalize your images in most of the cases, obviously you will face some situations that does not require normalization. The reason is to keep the values in a certain range. The output of the network, even if the network is 'big', is strongly influenced by the input data range. If you keep your input range out of control, your predictions will drastically change from one to another. Thus, the gradient would be out of control too and might make your training unefficient. I invite you to read this and that answers to have more details about the 'why' behind normalization and have a deeper understanding of the behaviours.

                                      It is quite common to normalize images with imagenet mean & standard deviation : mean = [0.485, 0.456, 0.406], std = [0.229, 0.224, 0.225]. Of course you could also consider, if your dataset is enough realistic, in a production context, to use its own mean and std instead of imagenet's.

                                      Finally keep in mind those values since, once your model will be trained, you will still need to normalize any new image to achieve a good accuracy with your future inferences.

                                      Source https://stackoverflow.com/questions/71472904

                                      Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                                      Vulnerabilities

                                      No vulnerabilities reported

                                      Install albumentations

                                      Albumentations requires Python 3.6 or higher. To install the latest version from PyPI:. Other installation options are described in the documentation.

                                      Support

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

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                                      Install
                                      • pip install albumentations

                                      Clone
                                      • https://github.com/albumentations-team/albumentations.git

                                      • gh repo clone albumentations-team/albumentations

                                      • git@github.com:albumentations-team/albumentations.git

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