Dataset of surface EMG recordings from 11 subjects performing single and combination gestures, from "**A Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition**" by Niklas Smedemark-Margulies, Yunus Bicer, Elifnur Sunger, Stephanie Naufel, Tales Imbiriba, Eugene Tunik, Deniz Erdogmus, and Mathew Yarossi.

For more details and example usage, see the following:

• Paper pdf - https://arxiv.org/pdf/2309.12217.pdf
• Experiment code - https://github.com/neu-spiral/multi-label-emg

Contents

Dataset of single and combination gestures from 11 subjects.
Subjects participated in 13 experimental blocks.
During each block, they followed visual prompts to perform gestures while also manipulating a joystick.
Surface EMG was recorded from 8 electrodes on the forearm; labels were recorded according to the current visual prompt and the current state of the joystick.

Experiments included the following blocks:

• 1 Calibration block
• 6 Simultaneous-Pulse Combination blocks (3 without feedback, 3 with feedback)
• 6 Hold-Pulse Combination blocks (3 without feedback, 3 with feedback)

The contents of each block type were as follows:

• In the Calibration block, subjects performed 8 repetitions of each of the 4 direction gestures, 2 modifier gestures, and a resting pose.
  Each Calibration trial provided 160 overlapping examples, for a total of: 8 repetitions x 7 gestures x 160 examples = 8960 examples.
• In Simultaneous-Pulse Combination blocks, subjects performed 8 trials of combination gestures, where both components were performed simultaneously.
  Each Simultaneous-Pulse trial provided 240 overlapping examples, for a total of: 8 trials x 240 examples = 1920 examples.
• In Hold-Pulse Combination blocks, subjects performed 28 trials of combination gestures, where 1 gesture component was held while the other was pulsed.
  Each Hold-Pulse trial provided 240 overlapping examples, for a total of: 28 trials x 240 examples = 6720 examples.

A single data example (from any block) corresponds a window 250ms of EMG recorded at 1926Hz (built-in 20–450 Hz bandpass filtering applied).
A 50ms step size was used between each window; note that neighboring data examples are therefore overlapping.

Feedback was provided as follows:

• In blocks with feedback, a model pre-trained on the Calibration data was used to give realtime visual feedback during the trial.
• In blocks without feedback, no model was used, and the visual prompt was the only source of information about the current gesture.

For more details, see the paper.

Labels

Two types of labels are provided:

• joystick labels were recorded based on the position of the joystick, and are treated as ground-truth.
• visual labels were also recorded based on what prompt was currently being shown to the subject.

For both joystick and visual labels, the following structure applies. Each gesture trial has a two-part label.

The first label component describes the direction gesture, and takes values in {0, 1, 2, 3, 4}, with the following meaning:

• 0 - "Up" (joystick pull)
• 1 - "Down" (joystick push)
• 2 - "Left" (joystick left)
• 3 - "Right" (joystick right)
• 4 - "NoDirection" (absence of a direction gesture; none of the above)

The second label component describes the modifier gesture, and takes values in {0, 1, 2}, with the following meaning:

• 0 - "Pinch" (joystick trigger button)
• 1 - "Thumb" (joystick thumb button)
• 2 - "NoModifier" (absence of a modifier gesture; none of the above)

Examples of Label Structure

Single gestures have labels like (0, 2) indicating ("Up", "NoModifier") or (4, 1) indicating ("NoDirection", "Thumb").

Combination gesture have labels like (0, 0) indicating ("Up", "Pinch") or (2, 1) indicating ("Left", "Thumb").

File layout

Data are provided in Numpy and MATLAB format. Descriptions below apply for both.

Each experimental block is provided in a separate folder.
Within one experimental block, the following files are provided:

• `data.npy` - Raw EMG data, with shape (items, channels, timesteps).
• `joystick_direction_labels.npy` - one-hot joystick direction labels, with shape (items, 5).
• `joystick_modifier_labels.npy` - one-hot joystick modifier labels, with shape (items, 3).
• `visual_direction_labels.npy` - one-hot visual direction labels, with shape (items, 5).
• `visual_modifier_labels.npy` - one-hot visual modifier labels, with shape (items, 3).

Loading data

For example code snippets for loading data, see the associated code repository.

The INRIA Aerial Image Labeling dataset is comprised of 360 RGB tiles of 5000×5000px with a spatial resolution of 30cm/px on 10 cities across the globe. Half of the cities are used for training and are associated to a public ground truth of building footprints. The rest of the dataset is used only for evaluation with a hidden ground truth. The dataset was constructed by combining public domain imagery and public domain official building footprints.

This dataset contains the 16 bit of the semi-automatically generated ground truth labels for the nuclei that were used both in training (Labelled as "Original") or inference (Labelled as "Biological" or "Technical) for the MRCNN and FPN2-WS networks

This dataset contains the 16 bit of the manually annotated ground truth labels for the nuclei that were used both in training (Labelled as "Original") or inference (Labelled as "Biological" or "Technical) for the MRCNN and FPN2-WS networks

The Inria Aerial Image Labeling addresses a core topic in remote sensing: the automatic pixelwise labeling of aerial imagery. Dataset features: Coverage of 810 km² (405 km² for training and 405 km² for testing) Aerial orthorectified color imagery with a spatial resolution of 0.3 m Ground truth data for two semantic classes: building and not building (publicly disclosed only for the training subset) The images cover dissimilar urban settlements, ranging from densely populated areas (e.g., San Francisco’s financial district) to alpine towns (e.g,. Lienz in Austrian Tyrol). Instead of splitting adjacent portions of the same images into the training and test subsets, different cities are included in each of the subsets. For example, images over Chicago are included in the training set (and not on the test set) and images over San Francisco are included on the test set (and not on the training set). The ultimate goal of this dataset is to assess the generalization power of the techniqu

Summary This submission is a supplementary material to the article [Coban 2020b]. As part of the manuscript, we release three simulated parallel-beam tomographic datasets of 94 apples with internal defects, the ground truth reconstructions and two defect label files.

Description This Zenodo upload contains the ground truth reconstructed slices for each apple. In total, there are 72192 reconstructed slices, which have been divided into 6 separate submissions:

ground_truths_1.zip (1 of 6): 10.5281/zenodo.4550729

ground_truths_2.zip (2 of 6): 10.5281/zenodo.4575904

ground_truths_3.zip (3 of 6): 10.5281/zenodo.4576078

ground_truths_4.zip (4 of 6): 10.5281/zenodo.4576122

ground_truths_5.zip (5 of 6): 10.5281/zenodo.4576202

ground_truths_6.zip (6 of 6): 10.5281/zenodo.4576260 (this upload)

The simulated parallel-beam datasets and defect label files are also available through this project, via a separate Zenodo upload: 10.5281/zenodo.4212301.

Apparatus The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban 2020a].

Ground Truth Generation

We reconstructed the raw tomographic data, which was captured at sample resolution of 54.2µm over a 360 degrees in circular and continuous motion in a cone-beam setup. A total of 1200 projections were collected, which were distributed evenly over the full circle. The raw tomographic data is available upon request.

The ground truth reconstructed slices were generated based on Conjugate Gradient Least Squares (CGLS) reconstruction of each apple. The voxel grid in the reconstruction was 972px x 972px x 768px. The resolution in the ground truth reconstructions remained unchanged.

All ground truth reconstructed slices are in .tif format. Each file is named "appleNo_sliceNo.tif".

List of Contents The contents of the submission is given below.

ground_truths_6: This folder contains reconstructed slices of 16 apples

Additional Links These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's GitHub page.

Contact Details For more information or guidance in using these dataset, please get in touch with

s.b.coban [at] cwi.nl

vladyslav.andriiashen [at] cwi.nl

poulami.ganguly [at] cwi.nl

Acknowledgments We acknowledge GREEFA for supplying the apples and further discussions.

Description

This data is the ground truth for the "evaluation dataset" for the DCASE 2020 Challenge Task 2 "Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring" [task description].

In the task, three datasets have been released: "development dataset", "additional training dataset", and "evaluation dataset". The evaluation dataset was the last of the three released and includes around 400 samples for each Machine Type and Machine ID used in the evaluation dataset, none of which have any condition label (i.e., normal or anomaly). This ground truth data contains the condition labels.

Data format

The ground truth data is a CSV file like the following:

fan id_01_00000000.wav,normal_id_01_00000098.wav,0 id_01_00000001.wav,anomaly_id_01_00000064.wav,1 ...

id_05_00000456.wav,anomaly_id_05_00000033.wav,1 id_05_00000457.wav,normal_id_05_00000049.wav,0 pump id_01_00000000.wav,anomaly_id_01_00000049.wav,1 id_01_00000001.wav,anomaly_id_01_00000039.wav,1 ...

id_05_00000346.wav,anomaly_id_05_00000052.wav,1 id_05_00000347.wav,anomaly_id_05_00000080.wav,1 slider id_01_00000000.wav,anomaly_id_01_00000035.wav,1 id_01_00000001.wav,anomaly_id_01_00000176.wav,1 ...

"Fan", "pump", "slider", etc mean "Machine Type" names. The lines following a Machine Type correspond to pairs of a wave file in the Machine Type and a condition label. The first column shows the name of a wave file. The second column shows the original name of the wave file, but this can be ignored by users. The third column shows the condition label (i.e., 0: normal or 1: anomaly).

How to use

A system for calculating AUC and pAUC scores for the "evaluation dataset" is available on the Github repository [URL]. The ground truth data is used by this system. For more information, please see the Github repository.

Conditions of use

This dataset was created jointly by NTT Corporation and Hitachi, Ltd. and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

Publication

If you use this dataset, please cite all the following three papers:

Yuma Koizumi, Shoichiro Saito, Noboru Harada, Hisashi Uematsu, and Keisuke Imoto, "ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection," in Proc. of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2019. [pdf]

Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” in Proc. 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019. [pdf]

Yuma Koizumi, Yohei Kawaguchi, Keisuke Imoto, Toshiki Nakamura, Yuki Nikaido, Ryo Tanabe, Harsh Purohit, Kaori Suefusa, Takashi Endo, Masahiro Yasuda, and Noboru Harada, "Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring," in Proc. 5th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2020. [pdf]

Feedback

If there is any problem, please contact us:

Yuma Koizumi, koizumi.yuma@ieee.org

Yohei Kawaguchi, yohei.kawaguchi.xk@hitachi.com

Keisuke Imoto, keisuke.imoto@ieee.org

Click to add a brief description of the dataset (Markdown and LaTeX enabled).

Provide:

a high-level explanation of the dataset characteristics explain motivations and summary of its content potential use cases of the dataset

Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew’s correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.

LabelMe database is a large collection of images with ground truth labels for object detection and recognition. The annotations come from two different sources, including the LabelMe online annotation tool.

Recent progress in computer vision has been driven by high-capacity models trained on large datasets. Unfortunately, creating large datasets with pixel-level labels has been extremely costly due to the amount of human effort required. In this paper, we present an approach to rapidly creating pixel-accurate semantic label maps for images extracted from modern computer games. Although the source code and the internal operation of commercial games are inaccessible, we show that associations between image patches can be reconstructed from the communication between the game and the graphics hardware. This enables rapid propagation of semantic labels within and across images synthesized by the game, with no access to the source code or the content. We validate the presented approach by producing dense pixel-level semantic annotations for 25 thousand images synthesized by a photorealistic open-world computer game. Experiments on semantic segmentation datasets show that using the acquired data to supplement real-world images significantly increases accuracy and that the acquired data enables reducing the amount of hand-labeled real-world data: models trained with game data and just 1/3 of the CamVid training set outperform models trained on the complete CamVid training set.

This folder contains four Image Annotation Datasets (ESPGame, IAPR-TC12, ImageCLEF 2011, ImagCLEF 2012). Each dataset has sub-folders of training images, testing images, ground truth, labels.

Moreover, labels are the limited number of labels the dataset could assign to an image. While the ground is the correct labeling for each image.

Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew’s correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.

Original images are from http://www.robots.ox.ac.uk/~vgg/software/cell_detection/. This software is associated with the publication "Learning to Detect Cells Using Non-overlapping Extremal Regions", MICCAI 2012. (DOI: 10.1007/978-3-642-33415-3_43)

Here, we provide the ground truth labels of: cell centers and segmentation, which are used in the publications:

• "Learning to Segment: Training Hierarchical Segmentation under a Topological Loss", MICCAI 2015. (DOI: 10.1007/978-3-319-24574-4_32)
• "Cell Detection and Segmentation Using Correlation Clustering", MICCAI 2014. (DOI: 10.1007/978-3-319-10404-1_2)

Summary
This submission is a supplementary material to the article [Coban 2020b]. As part of the manuscript, we release three simulated parallel-beam tomographic datasets of 94 apples with internal defects, the ground truth reconstructions and two defect label files.

Description
This Zenodo upload contains the ground truth reconstructed slices for each apple. In total, there are 72192 reconstructed slices, which have been divided into 6 separate submissions:

• ground_truths_1.zip (1 of 6): 10.5281/zenodo.4550729
• ground_truths_2.zip (2 of 6): 10.5281/zenodo.4575904
• ground_truths_3.zip (3 of 6): 10.5281/zenodo.4576078 (this upload)
• ground_truths_4.zip (4 of 6): 10.5281/zenodo.4576122
• ground_truths_5.zip (5 of 6): 10.5281/zenodo.4576202
• ground_truths_6.zip (6 of 6): 10.5281/zenodo.4576260

The simulated parallel-beam datasets and defect label files are also available through this project, via a separate Zenodo upload: 10.5281/zenodo.4212301.

Apparatus
The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban 2020a].

Ground Truth Generation

We reconstructed the raw tomographic data, which was captured at sample resolution of 54.2µm over a 360 degrees in circular and continuous motion in a cone-beam setup. A total of 1200 projections were collected, which were distributed evenly over the full circle. The raw tomographic data is available upon request.

The ground truth reconstructed slices were generated based on Conjugate Gradient Least Squares (CGLS) reconstruction of each apple. The voxel grid in the reconstruction was 972px x 972px x 768px. The resolution in the ground truth reconstructions remained unchanged.

All ground truth reconstructed slices are in .tif format. Each file is named "appleNo_sliceNo.tif".

List of Contents
The contents of the submission is given below.

• ground_truths_3: This folder contains reconstructed slices of 16 apples
  

Additional Links
These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's GitHub page.

Contact Details
For more information or guidance in using these dataset, please get in touch with

• s.b.coban [at] cwi.nl
• vladyslav.andriiashen [at] cwi.nl
• poulami.ganguly [at] cwi.nl

Acknowledgments
We acknowledge GREEFA for supplying the apples and further discussions.

This file contains the MATLAB source code for developing Ground Truth Dataset, Semantic Segmentation, and Evaluation for Lumbar Spine MRI Dataset. It has the file structure necessary for the execution of the code. Please download the MRI Dataset, and the Label Image Ground Truth Data for Lumbar Spine MRI Dataset separately and unzip them inside the same installation folder. Links to their repository are provided in the Related Links section below.

Please refer to instruction.docx file included in the package on how to install and use this source code.

You can download and read the research papers detailing our methodology on boundary delineation for lumbar spinal stenosis detection using the URLs provided in the Related Links at the end of this page. You can also check out other dataset related to this program from that section.

We kindly request you to cite our papers when using our data or program in your research.

IIIT5K-Words

The IIIT5K Words Dataset is a comprehensive collection of labeled word images, curated by the International Institute of Information Technology, Hyderabad (IIIT-H). It is designed to facilitate research and development in optical character recognition (OCR), word recognition, and related fields.

The dataset contains a diverse set of 5,000 word images, covering various fonts, styles, and sizes. Each word image represents a single English word and is accompanied by its corresponding ground truth label, providing accurate transcription for training and evaluation purposes.

Please refer: IIIT5K-Words official site

Note: In order to view mat files use this code

install requirements

!pip install shutil pymatreader

unzip the zip file

import shutil

shutil.unpack_archive('IIIT5K-Word_V3.0.tar.gz', 'data')

view mat files

from pymatreader import read_mat

testdata_mat = read_mat('testdata.mat')

testCharBound_mat = read_mat('testCharBound.mat')

testdata_mat

Key Features: - Size: The dataset comprises 5,000 word images, making it suitable for training and evaluating OCR algorithms. - Diversity: The dataset encompasses a wide range of fonts, styles, and sizes to ensure the inclusion of various challenges encountered in real-world scenarios. - Ground Truth Labels: Each word image is paired with its ground truth label, enabling supervised learning approaches and facilitating evaluation metrics calculation. - Quality Annotation: The dataset has been carefully curated by experts at IIIT-H, ensuring high-quality annotations and accurate transcription of the word images. - Research Applications: The dataset serves as a valuable resource for OCR, word recognition, text detection, and related research areas.

Potential Use Cases: - Optical Character Recognition (OCR) Systems: The dataset can be employed to train and benchmark OCR models, improving their accuracy and robustness. - Word Recognition Algorithms: Researchers can utilize the dataset to develop and evaluate word recognition algorithms, including deep learning-based approaches. - Text Detection: The dataset can aid in the development and evaluation of algorithms for text detection in natural scenes. - Font and Style Analysis: Researchers can leverage the dataset to study font and style variations, character segmentation, and other related analyses.

Citation:

@InProceedings{MishraBMVC12, author = "Mishra, A. and Alahari, K. and Jawahar, C.~V.", title = "Scene Text Recognition using Higher Order Language Priors", booktitle = "BMVC", year = "2012", }

Dataset Card for predicted_labels

These photos are used in the FiftyOne getting started webinar. The images have a prediction label where were generated by self-supervised classification through a OpenClip Model. https://github.com/thesteve0/fiftyone-getting-started/blob/main/5_generating_labels.py They were then manually cleaned to produce the ground truth label. https://github.com/thesteve0/fiftyone-getting-started/blob/main/6_clean_labels.md They are 300 public domain photos… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/getting-started-labeled-photos.

The 95% confidence intervals of accuracy and MCC of the supervised learning models for the main dataset.

The 95% confidence intervals of accuracy and MCC of the supervised learning models for the external dataset.