# data augmentation

Data augmentation is a machine-learning technique that expands training datasets by applying random or class-preserving transformations to data points.

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## Enhancing industrial safety AI with synthetic data on Amazon SageMaker AI

DevFeed: [Enhancing industrial safety AI with synthetic data on Amazon SageMaker AI](<https://devfeed.tech/articles/enhancing-industrial-safety-ai-with-synthetic-data-on-amazon-sagemaker-ai-42130.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/machine-learning/enhancing-industrial-safety-ai-with-synthetic-data-on-amazon-sagemaker-ai/>)

Author: Dimitri Voytan

Published: 2026-09-17T15:28:08Z

Content type: tutorial

Language: en

Sources: [Artificial Intelligence](<https://devfeed.tech/sources/artificial-intelligence.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [synthetic-data](<https://devfeed.tech/topics/synthetic-data.md>), [Amazon SageMaker](<https://devfeed.tech/topics/amazon-sagemaker.md>), [data augmentation](<https://devfeed.tech/topics/data-augmentation.md>), [object-detection](<https://devfeed.tech/topics/object-detection.md>), [Person Detection](<https://devfeed.tech/topics/person-detection.md>), [Computer vision](<https://devfeed.tech/topics/computer-vision.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [data-processing](<https://devfeed.tech/topics/data-processing.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>)

Tags: [advanced-300](<https://devfeed.tech/tags/advanced-300.md>), [ai](<https://devfeed.tech/tags/ai.md>), [amazon-sagemaker-ai](<https://devfeed.tech/tags/amazon-sagemaker-ai.md>), [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [data-augmentation](<https://devfeed.tech/tags/data-augmentation.md>), [industrial](<https://devfeed.tech/tags/industrial.md>), [object-detection](<https://devfeed.tech/tags/object-detection.md>), [person-detection](<https://devfeed.tech/tags/person-detection.md>), [pipeline](<https://devfeed.tech/tags/pipeline.md>), [safety](<https://devfeed.tech/tags/safety.md>), [synthetic-data](<https://devfeed.tech/tags/synthetic-data.md>), [technical-how-to](<https://devfeed.tech/tags/technical-how-to.md>), [training](<https://devfeed.tech/tags/training.md>), [up](<https://devfeed.tech/tags/up.md>)

### AI overview

This tutorial shows how to build a synthetic data augmentation pipeline with Amazon SageMaker AI and Amazon Rekognition for industrial safety computer vision. The pipeline generates photo-realistic, automatically labeled training images for rare and hazardous scenarios near heavy machinery, with experiments reporting up to a 160% improvement in person-detection mAP50 without manual annotation or hazardous photography sessions.

### Source excerpt

Learn how to build a synthetic data augmentation pipeline on Amazon SageMaker AI and Amazon Rekognition that generates photo-realistic, auto-labeled training images for industrial safety AI. This approach improved person detection by up to 160% without manual annotation or hazardous data collection near heavy machinery.

## Introducing TextImage Augmentation for Document Images

DevFeed: [Introducing TextImage Augmentation for Document Images](<https://devfeed.tech/articles/introducing-textimage-augmentation-for-document-images-7172.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/doc_aug_hf_alb>)

Author: Dana Aubakirova; Pablo Montalvo; Vladimir Iglovikov

Published: 2024-08-06T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [data augmentation](<https://devfeed.tech/topics/data-augmentation.md>), [albumentations](<https://devfeed.tech/topics/albumentations.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [albumentations](<https://devfeed.tech/tags/albumentations.md>), [data-augmentation](<https://devfeed.tech/tags/data-augmentation.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [document-ai](<https://devfeed.tech/tags/document-ai.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [research](<https://devfeed.tech/tags/research.md>), [synthetic-data](<https://devfeed.tech/tags/synthetic-data.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [training](<https://devfeed.tech/tags/training.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlms](<https://devfeed.tech/tags/vlms.md>)

### AI overview

The article introduces a multimodal data augmentation pipeline for document images used in Vision Language Model fine-tuning. Developed with Albumentations AI, it modifies document images and their text annotations together while aiming to preserve text quality. The methods include text insertion, deletion, swapping, and stopword replacement, followed by image masking and inpainting.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Hello Deep Learning: Dropout, data augmentation, weight decay and quantisation

DevFeed: [Hello Deep Learning: Dropout, data augmentation, weight decay and quantisation](<https://devfeed.tech/articles/hello-deep-learning-dropout-data-augmentation-weight-decay-and-quantisation-36368.md>)

Original publisher: [Read original article](<https://berthub.eu/articles/posts/dropout-data-augmentation-weight-decay/>)

Published: 2023-03-30T10:00:07Z

Content type: tutorial

Language: en

Sources: [Bert Hubert's writings](<https://devfeed.tech/sources/bert-hubert-s-writings.md>)

Topics: [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [data augmentation](<https://devfeed.tech/topics/data-augmentation.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Image](<https://devfeed.tech/topics/image.md>)

Tags: [data-augmentation](<https://devfeed.tech/tags/data-augmentation.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [image](<https://devfeed.tech/tags/image.md>), [performance](<https://devfeed.tech/tags/performance.md>), [training](<https://devfeed.tech/tags/training.md>), [training-data](<https://devfeed.tech/tags/training-data.md>), [validation](<https://devfeed.tech/tags/validation.md>)

### AI overview

This Hello Deep Learning tutorial examines how data augmentation and related techniques can improve the robustness of a convolutional handwritten-digit recognition network. It explains that exposing the network to shifted, corrupted, or otherwise modified inputs helps it handle real-world variation and can enlarge a limited training set.

### Source excerpt

This page is part of the Hello Deep Learning series of blog posts. You are very welcome to improve this page via GitHub! In the previous chapter we found ways to speed up our character recognition learning by a factor of 20 by using a better optimizer, and a further factor of four by cleverly using threads using a 'shared nothing architecture'. We also learned how we can observe the development of parameters.