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@ternaus ternaus released this 14 Oct 17:02
0.4.0
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Table of Contents

New transforms

ISONoise

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Target: image

This transform mimics the noise that images will have if the ISO parameter of the camera is high. Wiki

Solarize

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Targets: image

Solarize inverts all pixels above some threshold. It is an essential part of the work AutoAugment: Learning Augmentation Policies from Data.

Equilize

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Target: image

Equalizes image histogram. It is an essential part of the work AutoAugment: Learning Augmentation Policies from Data.

Posterize

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Target: image

Reduce the number of bits for each pixel. It is an essential part of the work AutoAugment: Learning Augmentation Policies from Data.

ImageCompression

Target: image
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Decrease Jpeg or WebP compression to the image.

Downscale

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Target: image

Decreases image quality by downscaling and upscaling back.

RandomResizedCrop

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Targets: image, mask, bboxes, keypoints

Crop the given Image to the random size and aspect ratio. This transform is an essential part of many image classification pipelines. Very popular for ImageNet classification.

It has the same API as RandomResizedCrop in torchvision.

RandomGridShuffle

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Targets: image, mask

Partition an image into tiles. Shuffle them and merge back.

CropNonEmptyMaskIfExists

Targets: image, mask, bboxes, keypoints

Crop area with a mask if the mask is non-empty, else make a random crop.

ToTensorV2

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Targets: image, mask

Convert image and mask to torch.Tensor

New features

Added YOLO format to bounding boxes.

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The Yolo format of a bounding box has a format [x, y, width, height], where values normalized to the size of the image. Ex: [0.3, 0.1, 0.05, 0.07]

Added Deterministic / Replay mode

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Augmentations pipeline has a lot of randomnesses, which is hard to debug. We added Determentsic / Replay mode in which you can track what parameters were applied to the input and use precisely the same transform to another input if necessary.

Jupyter notebook with an example.

Added fill_value to the Cutout transform.

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Separated fill_value for images and masks

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One of the use cases is it to use mask_value, which is equal to the ignore_index of your loss. This will decrease the level of noise and may improve convergence.

Speedup in the RGBShift

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3.2 times faster for uint8 images.

Speedup in HueSaturationValue

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2 times faster for uint8 images.

Speedup in RandomBrightnessContrast

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2.7 times faster for uint8 images.

Speedup in RandomGamma

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4 times faster for uint8 images.

Added support for images and masks with more than 3 channels

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Added key points support

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Not all spatial tranforms jave keypoints support yet. In this release we added Crop, CropNonEmptyMaskIfExists, LongestMaxSize, RandomCropNearBBox, Resize, SmallestMaxSize, and Transpose.

Add per channel transform composition 7fb635c

Bug Fixes

Documentation Updated

Added a page that lists pre-prints and papers that cite albumentations

We are delighted that albumentations are helpful to the academic community. We extended documentation with a page that lists all papers and preprints that cite albumentations in their work. This page is automatically generated by parsing Google Scholar. At this moment, this number is 24.

Added a page that lists competitions in which top teams used albumentations.

We are delighted that albumentations help people to get top results in machine learning competitions at Kaggle and other platforms. We added a "Hall of Fame" where people can share their achievements. This page is manually created. We encourage people to add more information about their results with pull requests, following the contributing guide.

People that made this release happen

@albu @Dipet @creafz @BloodAxe @ternaus @vfdev-5 @arsenyinfo @qubvel @toshiks @jae-hyuck @BelBES @alekseynp @Timeous @jveitchmichaelis @bfialkoff

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