augmented non-deterministic dataset through MCMC and the auxiliary SWAP model

The data consists of MRI images. The data has four classes of images both in training as well as a testing set:

1. Mild Demented
2. Moderate Demented
3. Non Demented
4. Very Mild Demented

The data contains two folders. One of them is augmented ones and the other one is originals. Originals could be used for validation or test dataset...

Data is augmented from an existing dataset. Original images can be seen in Data Explorer. https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images

My purpose of the publish this dataset is to the usage of augmented images as well as originals. The importance of augmentation is can be a little underrated.

Augmented Dataset For Training

## Overview

Augmented Dataset For Training is a dataset for object detection tasks - it contains Ambulance annotations for 3,906 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

ml6team/augmented-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset Card for "kaggle-mbti-cleaned-augmented"

This dataset is built upon Shunian/kaggle-mbti-cleaned to address the sample imbalance problem. Thanks to the Parrot Paraphraser and NLP AUG, some of the skewness issue are addressed in the training data, make it grows from 328,660 samples to 478,389 samples in total. View GitHub for more information

Training Data (add Augmented)

## Overview

Training Data (add Augmented) is a dataset for object detection tasks - it contains Plate annotations for 825 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

A collection of 16 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

Collection description

DATA Augmented

## Overview

DATA Augmented is a dataset for object detection tasks - it contains UML Composant1 annotations for 455 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Augmented Olivetti Faces Dataset

• Total number of images: 2000 (original 400)
• Total labels of images: 2000 (50 per person)
• Description: The Augmented Olivetti Faces Dataset is a collection of facial images that have undergone various image augmentations to enhance diversity and variability. This dataset is derived from the original Olivetti Faces dataset, which consists of grayscale images capturing different facial expressions under controlled lighting conditions. The augmented version includes images that have been subject to transformations such as horizontal flips, rotations, cropping, and resizing, as well as the addition of controlled noise.
• The dataset contains a total of 2000 samples, each represented as a 2D matrix of pixel values. The original facial images are included, along with their horizontally flipped counterparts, images rotated by a small angle, cropped and resized versions, and images with added controlled noise. This augmented collection aims to provide a broader range of training data for machine learning models, especially those focused on facial recognition, image inpainting, and other computer vision tasks.

Researchers and developers can utilize the Augmented Olivetti Faces Dataset to evaluate the robustness and generalization capabilities of their algorithms in the presence of diverse facial variations. Additionally, the dataset can serve as a valuable resource for exploring the impact of augmentations on model performance, and for experimenting with image processing techniques that enhance the reliability and effectiveness of facial recognition systems.

Datasource: http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html Credit to AT&T Laboratories Cambridge for images

Na0s/sft-ready-Text-Generation-Augmented-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

Augmented Dataset For Training 2

## Overview

Augmented Dataset For Training 2 is a dataset for object detection tasks - it contains Ambulance annotations for 3,906 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

LeRobot Augmented Dataset

This dataset is an augmented version of the original LeRobot dataset. The augmentation expands the dataset by creating 4 versions of each original episode:

Original data - preserved as-is Horizontally flipped images - original action/state vectors Shoulder pan negated - original images with shoulder pan values negated in action/state vectors Both flipped and negated - horizontally flipped images with negated shoulder pan values

  Augmentation… See the full description on the dataset page: https://huggingface.co/datasets/twarner/lerobot-augmented.

The original dataset can be found in the following link (https://www.kaggle.com/datasets/hamdallak/the-iqothnccd-lung-cancer-dataset/data)

The goal for this dataset is to enhance the usability of the original dataset by augmenting the data to generate more CT images. The augmented dataset has more than 10 times the number of images compared to the original. Data, and specifically, image augmentation is a popular technique used in Data Engineering to enlarge the existing dataset in order to make the model more robust and more precise. Medical images are very hard to come by, so sometimes Data Augmentation is a necessity when it comes to these kinds of datasets.

For the purpose of augmenting the existing images, I created a notebook which can be found in the following link (https://www.kaggle.com/code/aleksandarcvetanov/elastic-transformation-of-ct-images). The notebook uses the OpenCV library and its methods to achieve elastic transformation of the images. Elastic transformation (deformation) is a well-known technique in image augmentation, cited in numerous science papers and articles. Elastic transformation of images is the base technique used in the original development of the U-Net, a popular neural network developed for the purposes of classifying and segmenting medical images using Convolutional Neural Networks.

More information about the original dataset can be found in the text file attached with this dataset.

Sample Augmented Dataset

## Overview

Sample Augmented Dataset is a dataset for object detection tasks - it contains Lays Small AiKc annotations for 738 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

🩺 Dataset Description

This dataset is an augmented version of an ECG image dataset created to balance and enrich the original classes for deep learning–based cardiovascular disease classification.

The original dataset consisted of unbalanced image counts per class in the training set: - ABH: 233 images - MI: 239 images - HMI: 172 images - NORM: 284 images

To improve class balance and model generalization, each class in the training set was expanded to 500 images using a combination of morphological, noise-based, and geometric data augmentation techniques. Additionally, the test set includes 112 images per class.

⚖️ Final Dataset Composition

• Training set: 4 classes × 500 images each → 2,000 images total
• Test set: 4 classes × 112 images each → 448 images total

🔬 Data Augmentation Techniques

1. Morphological Alterations - Erosion - Dilation - None (original preserved)

2. Noise Introduction - augment_noise_black_rain — simulates black streaks - augment_noise_pixel_dropout_black — random black pixel dropout - augment_noise_white_rain — simulates white streaks - augment_noise_pixel_dropout_white — random white pixel dropout

3. Geometric Transformations - Shift — small translations in all directions - Scale — random zoom-in/zoom-out between 0.9× and 1.1× - Rotate — small random rotation between -5° and +5°

These transformations were applied with balanced proportions to ensure diversity and realism while preserving diagnostic features of ECG signals.

💡 Intended Use

This dataset is designed for: - Training and evaluating deep learning models (CNNs, ViTs) for ECG image classification - Research in medical image augmentation, imbalanced data learning, and cardiovascular disease prediction

📘 License

This dataset is released under the CC0 1.0 License, allowing free use and distribution for research and educational purposes.

Result of 10-Fold cross-validation on augmented dataset.

DENTAL AUGMENTED

## Overview

DENTAL AUGMENTED is a dataset for object detection tasks - it contains  Wydf annotations for 1,281 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Misinformation about climate change poses a significant threat to societal well-being, prompting the urgent need for effective mitigation strategies. However, the rapid proliferation of online misinformation on social media platforms outpaces the ability of fact-checkers to debunk false claims. Automated detection of climate change misinformation offers a promising solution. In this study, we address this gap by developing a two-step hierarchical model—the Augmented CARDS model—specifically designed for detecting contrarian climate claims on Twitter. Furthermore, we apply the Augmented CARDS model to five million climate-themed tweets over a six-month period in 2022. We find that over half of contrarian climate claims on Twitter involve attacks on climate actors or conspiracy theories. Spikes in climate contrarianism coincide with one of four stimuli: political events, natural events, contrarian influencers, or convinced influencers. Implications for automated responses to climate misinformation are discussed.

Orange Augmented

## Overview

Orange Augmented is a dataset for object detection tasks - it contains Disease annotations for 1,598 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Augmented Dataset 5

## Overview

Augmented Dataset 5 is a dataset for object detection tasks - it contains Pothole KH6s annotations for 5,835 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).