Albumentation – fast & flexible image augmentations

Image Augmentations is a powerful technique to improve model robustness and performance. There are many image augmentations libraries on the market: torchvision, imgaug, DALI, Augmentor, SOLT, etc.
In all of them, authors focussed on variety at the cost of speed, or the speed at the cost of flexibility.

Requirements for augmentations:
* Variety: they want to have a large set of standard and exotic augmentation for image classification, segmentation, and detection in one place.
* Performance: transforms should be as fast as possible.
* Flexibility: it should be easy to add new transforms or new types of transforms.
* Conciseness: all complexity of implementation should be hidden behind the API.

Albumentations were born out of necessity. The authors were actively participating in various Deep Learning competitions. To get to the top they needed something better than what was already available. All of them, independently, started working on more powerful augmentation pipelines. Later they merged their efforts and released the code in the form of the library.

To date Albumentations has more than 70 transforms and supports image classification, #segmentation, object and keypoint detection tasks.

The library was adopted by academics, Kaggle, and other communities.

ODS: #tool_albumentations
Link: https://albumentations.ai/
Github: https://github.com/albumentations-team/albumentations
Paper: https://www.mdpi.com/2078-2489/11/2/125

P.S. Following trend setup by #Catalyst team, we provide extensive description of project with the help of its creators.

#guestpost #augmentation #CV #DL #imageprocessing #ods #objectdetection #imageclassification #tool

​​Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification

An enhanced augmentation strategy based on CutMix

Recently a large variety of regional dropout strategies have been proposed, such as Cutout, DropBlock, CutMix, etc. These methods help models to generalize better by partially occluding the discriminative parts of objects. However, they usually do it randomly, so a reasonable improvement would be to find some strategy of selecting the patches.
Attentive CutMix uses pretrained neural nets to find the most descriptive regions and replaces them. This further improves generalization because we make sure that patches are pasted not on the background, but on the areas of interest.

Authors train four variants each of ResNet, DenseNet and EfficientNet architectures on CIFAR-10, CIFAR-100, and ImageNet.
Attentive CutMix consistently provides an average increase of 1.5% over other methods which validates the effectiveness of our attention mechanism.

Paper: https://arxiv.org/abs/2003.13048

#deeplearning #augmentation

​​ObjectAug: Object-level Data Augmentation for Semantic Image Segmentation

The authors suggest ObjectAug perform object-level augmentation for semantic image segmentation.
This approach has the following steps:
- decouple the image into individual objects and the background using the semantic labels;
- augment each object separately;
- restore the black area brought by object augmentation using image inpainting;
- assemble the augmented objects and background;

Thanks to the fact that objects are separate, we can apply different augmentations to different categories and combine them with image-level augmentation methods.


Paper: https://arxiv.org/abs/2102.00221

#deeplearning #augmentation #imageinpainting #imagesegmentation

​​Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning

Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category.
The authors suggest several practical ideas to improving model performance on this task:
- using augmentations (synonym replacement, random insertion, random swap, random deletion) together with triplet loss
- using curriculum learning (two-stage and gradual)

Paper: https://arxiv.org/abs/2103.07552

Code: https://github.com/jasonwei20/triplet-loss

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-nlptriplettricks


#deeplearning #nlp #fewshotlearning #augmentation #curriculumlreaning

​​NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation

This paper presents a new participatory Python-based natural language augmentation framework that supports the creation of transformations (modifications to the data) and filters (data splits according to specific features).

The current version of the framework contains 117 transformations and 23 filters for a variety of natural language tasks.

The authors demonstrate the efficacy of NL-Augmenter by using several of its transformations to analyze the robustness of popular natural language models.

Paper: https://arxiv.org/abs/2112.02721
Code: https://github.com/GEM-benchmark/NL-Augmenter

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-nlaugmenter

#deeplearning #nlp #augmentation #robustness