DALib: A Curated Repository of Libraries for Data Augmentation in Computer Vision
Abstract
:1. Introduction
- Computer Vision—Data augmentation is extensively employed in computer vision tasks, such as image classification, object detection, and semantic segmentation. By applying transformations like rotations, translations, flips, zooms, and color variations, data augmentation helps models learn to recognize objects from different perspectives, lighting conditions, scales, and orientations [1,2,3,4]. This enhances the generalization ability of the models and makes them more robust to variations in real-world images.
- Natural Language Processing (NLP)—Data augmentation techniques are also applicable to NLP tasks, including text classification, sentiment analysis, and machine translation [5,6,7]. Methods like back-translation, word replacement, and synonym substitution can be used to generate augmented text samples, effectively increasing the diversity of the training data and improving the language model’s understanding and generalization.
- Speech Recognition—In speech recognition tasks, data augmentation techniques can be used to introduce variations in audio samples, such as adding background noise, altering pitch or speed, or applying reverberations [8,9,10]. Augmenting the training data with these transformations helps a model to be more robust to different acoustic environments and improves its performance in real-world scenarios.
- Time Series Analysis—Data augmentation can be applied to time series data, such as sensor data, stock market data, or physiological signals. Techniques like time warping, scaling, jittering, and random noise injection can create augmented samples that capture various temporal patterns, trends, and noise characteristics [11,12,13]. Augmentation in time series data helps models to learn and generalize better to different temporal variations and noisy conditions.
- Medical Imaging—In the field of medical imaging, data augmentation is crucial due to the scarcity and high cost of labeled data. Augmentation techniques such as rotations, translations, and elastic deformations can simulate anatomical variations, different imaging angles, and distortions in medical images [14,15,16,17]. By augmenting the dataset, models trained on limited labeled medical images can generalize better and be more robust to variability in patient anatomy and imaging conditions.
- Anomaly Detection—Data augmentation can be used to create artificial anomalies or perturbations in normal data samples, thereby generating augmented data that contains both normal and anomalous instances [18,19,20]. This augmented dataset can then be used for training anomaly detection models, enabling them to learn a wider range of normal and abnormal patterns and improving their detection accuracy.
- We collect information on data augmentation libraries designed for computer vision applications. Existing reviews focus on data augmentation methods and not on the availability of such methods in public libraries;
- Our work is not limited to a specific application scenario. The libraries have been chosen to offer the readers a comprehensive survey of available methods to be used in different computer vision applications;
- To the best of our knowledge, this is the first comprehensive review of data augmentation libraries that also offers a curated taxonomy;
- We also provide a dedicated public website to serve as a centralized repository where the taxonomy, methods, and examples associated with the surveyed data augmentation can be explored;
- This is an ongoing work that will be expanded when new libraries become available.
2. Data Augmentation Libraries
2.1. Albumentations
2.2. AugLy
2.3. Augmentor
2.4. Augraphy
2.5. Automold
2.6. CLoDSA
2.7. imgaug
2.8. KerasCV
2.9. Kornia
2.10. SOLT
2.11. Torchvision
3. Data Augmentation Taxonomy
4. Data Augmentation Techniques
4.1. Generic Traditional Techniques
4.1.1. Geometry
Basic
Distortion
4.1.2. Photometry
Contrast
4.1.3. Quality
Blur Filters
Sharpening Techniques
Mathematical Morphology
Style Filters
Noise Injection
Tessellation
Other Worsening Techniques
4.1.4. Miscellaneous
Regional Dropout
Composition
4.2. Task-Driven Traditional Techniques
4.2.1. Weather
4.2.2. Street
4.2.3. Paper Documents
4.2.4. Social Network
4.3. Generative Deep Learning
4.3.1. GANs
4.3.2. VAE
4.3.3. Diffusion Models
4.3.4. Style Transfer
5. The DALib Website
5.1. The Homepage
5.2. The Taxonomy Page
5.3. The Library Pages
5.4. A Few Use Cases
6. Challenges
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Library Name | GitHub Repository | Last Update |
---|---|---|
Albumentations [21] | albumentations-team/albumentations (https://github.com/albumentations-team/albumentations) | 2023 |
AugLy [22] | facebookresearch/AugLy (https://github.com/facebookresearch/AugLy) | 2023 |
Augmentor [23] | mdbloice/Augmentor (https://github.com/mdbloice/Augmentor) | 2023 |
Augraphy [24] | sparkfish/augraphy (https://github.com/sparkfish/augraphy) | 2023 |
Automold [25] | UjjwalSaxena/Automold–Road-Augmentation-Library (https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library) | 2022 |
CLoDSA [26] | joheras/CLoDSA (https://github.com/joheras/CLoDSA) | 2021 |
imgaug [27] | aleju/imgaug (https://github.com/aleju/imgaug) | 2020 |
KerasCV [28] | keras-team/keras-cv (https://github.com/keras-team/keras-cv) | 2023 |
Kornia [29] | kornia/kornia (https://github.com/kornia/kornia) | 2023 |
SOLT [30] | Oulu-IMEDS/solt (https://github.com/Oulu-IMEDS/solt) | 2022 |
Torchvision [31] | pytorch/vision (https://github.com/pytorch/vision) | 2023 |
Library Name | Language | ML Frameworks | # | GitHub Stars | Citations | Referenced |
---|---|---|---|---|---|---|
Albumentations [21] | Python | PyTorch, Keras | 83 | 12,600 | 1457 | 1540 |
AugLy [22] | Python | PyTorch | 38 | 4800 | 33 | 117 |
Augmentor [23] | Python, Julia | PyTorch, Keras, Flux | 23 | 5000 | 292 | 729 |
Augraphy [24] | Python | — | 26/40 | 233 | 4 | 9 |
Automold [25] | Python | — | 17 | 566 | 6 | 54 |
CLoDSA [26] | Python | Keras | 26 | 119 | 65 | 86 |
imgaug [27] | Python | — | 164 | 13,800 | 200 | 1460 |
KerasCV [28] | Python | Keras | 21 | 838 | — | 39 |
Kornia [29] | Python | PyTorch | 64 | 8700 | 233 | 645 |
SOLT [30] | Python | PyTorch | 21 | 258 | 11 | 11 |
Torchvision [31] | Python, C++, Java | PyTorch | 27 | 14,600 | 415 | 6770 |
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Share and Cite
Amarù, S.; Marelli, D.; Ciocca, G.; Schettini, R. DALib: A Curated Repository of Libraries for Data Augmentation in Computer Vision. J. Imaging 2023, 9, 232. https://doi.org/10.3390/jimaging9100232
Amarù S, Marelli D, Ciocca G, Schettini R. DALib: A Curated Repository of Libraries for Data Augmentation in Computer Vision. Journal of Imaging. 2023; 9(10):232. https://doi.org/10.3390/jimaging9100232
Chicago/Turabian StyleAmarù, Sofia, Davide Marelli, Gianluigi Ciocca, and Raimondo Schettini. 2023. "DALib: A Curated Repository of Libraries for Data Augmentation in Computer Vision" Journal of Imaging 9, no. 10: 232. https://doi.org/10.3390/jimaging9100232