AugStatic - A Light-Weight Image Augmentation Library
The rapid exponential increase in the data led to an abrupt mix of various data types, leading to a deficiency of helpful information. Creating new data with the existing different types of data are presented in this paper. Augmentation is adding up or modifying the dataset with extra data. There are many types of augmentation done for various kinds of datasets. Augmentation has been widely used in multiple pre-processing steps of diverse machine learning pipelines. Many libraries or packages are made for augmentation called augmentation libraries. There are many salient features that each library supports. This paper seeks to enhance the library that makes the AugStatic library much more lightweight and efficient. AugStatic is a custom-built image augmentation library with lower computation costs and more extraordinary salient features compared to other image augmentation libraries. This framework can be used for NumPy arrays and tensors too.
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Tasks
Classification
Data Augmentation
Data Visualization
Detecting Image Manipulation
Image Augmentation
Image Classification
Image Cropping
Image Denoising
Image Generation
Image Manipulation
Image Manipulation Detection
Image Matting
Image Morphing
image smoothing
Image Stitching
Image-Variation
ROI-based image generation
Datasets
Results from the Paper
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Image Augmentation | Intel Image Classification | Augstatic | Balanced Accuracy | 98 | # 1 |
