Hand Labelled

## Overview

Hand Labelled is a dataset for object detection tasks - it contains Hand Labelled annotations for 393 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).

I have prepared the dataset for this tutorial which you can download from here. This dataset contains around 3000 handgun images with their bounding box labels distributed in 2 folders: images and labels

Please cite the following paper when using this dataset:

N. Thakur, V. Su, M. Shao, K. Patel, H. Jeong, V. Knieling, and A. Bian “A labelled dataset for sentiment analysis of videos on YouTube, TikTok, and other sources about the 2024 outbreak of measles,” Proceedings of the 26th International Conference on Human-Computer Interaction (HCII 2024), Washington, USA, 29 June - 4 July 2024. (Accepted as a Late Breaking Paper, Preprint Available at: https://doi.org/10.48550/arXiv.2406.07693)

Abstract

This dataset contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024. These websites primarily include YouTube and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The remainder of the websites include Instagram and Facebook as well as the websites of various global and local news organizations. For each of these videos, the URL of the video, title of the post, description of the post, and the date of publication of the video are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis (using VADER), subjectivity analysis (using TextBlob), and fine-grain sentiment analysis (using DistilRoBERTa-base) of the video titles and video descriptions were performed. This included classifying each video title and video description into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii) one of the subjectivity classes i.e. highly opinionated, neutral opinionated, or least opinionated, and (iii) one of the fine-grain sentiment classes i.e. fear, surprise, joy, sadness, anger, disgust, or neutral. These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for performing sentiment analysis or subjectivity analysis in this field as well as for other applications. The paper associated with this dataset (please see the above-mentioned citation) also presents a list of open research questions that may be investigated using this dataset.

Hariyo Chasma Labelled Dataset

## Overview

Hariyo Chasma Labelled Dataset is a dataset for object detection tasks - it contains Person annotations for 228 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).

librarian-bots/arxiv-cs-papers-with-datasets-labelled dataset hosted on Hugging Face and contributed by the HF Datasets community

It is composed of around 770k of color 256x256 RGB images extracted from the European Union Intellectual Property Office (EUIPO) open registry. Each of them is associated to multiple labels that classify the figurative and textual elements that appear in the images. These annotations have been classified by the EUIPO evaluators using the Vienna classification, a hierarchical classification of figurative marks.

We suggest it to be used for: 1. Unconditional trademark generation 2. Conditional trademark generation. 3. Multi-label logo classification (Vienna classification). 4. Optical Character Recognition. 5. Conditional trade 6. Image segmentation. 7. Image retrieval

Kieli labels audio speech, Image, Video & Text Data including semantic segmentation, named entity recognition (NER) and POS tagging. Kieli transforms unstructured data into high quality training data for the refinement of Artificial Intelligence and Machine Learning platforms. For over a decade, hundreds of organizations have relied on Kieli to deliver secure, high-quality training data and model validation for machine learning. At Kieli, we believe that accurate data is the most important factor in production learning models. We are committed to delivering the best quality data for the most enterprising organizations and helping you make strides in Artificial Intelligence. At Kieli, we're passionately dedicated to serving the Arabic, English and French markets. We work in all areas of industry: healthcare, technology and retail.

Over 4 million frames of motion capture data for 100 different styles of locomotion. Can be used for animation, human motion and sequence modelling research.

This version of the dataset includes the features extracted from the raw motion capture data. This includes local phases, foot contacts, joint positions, joint rotations, joint velocities, character trajectory etc.

Image Data Labeling Service Market Outlook

The global image data labeling service market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach around USD 6.1 billion by 2032, exhibiting a robust CAGR of 17.1% during the forecast period. The exponential growth of this market is driven by the increasing demand for high-quality labeled data for machine learning and artificial intelligence applications across various industries.

One of the primary growth factors of the image data labeling service market is the surge in the adoption of artificial intelligence (AI) and machine learning (ML) technologies across multiple sectors. Organizations are increasingly relying on AI and ML to enhance operational efficiency, improve customer experience, and gain competitive advantages. As a result, there is a rising need for accurately labeled data to train these AI and ML models, driving the demand for image data labeling services. Furthermore, advancements in computer vision technology have expanded the scope of image data labeling, making it essential for applications such as autonomous vehicles, facial recognition, and medical imaging.

Another significant factor contributing to market growth is the proliferation of big data. The massive volume of data generated from various sources, including social media, surveillance cameras, and IoT devices, necessitates the need for effective data labeling solutions. Companies are leveraging image data labeling services to manage and analyze these vast datasets efficiently. Additionally, the growing focus on personalized customer experiences in sectors like retail and e-commerce is fueling the demand for labeled data, which helps in understanding customer preferences and behaviors.

Investment in research and development (R&D) activities by key players in the market is also a crucial growth driver. Companies are continuously innovating and developing new techniques to enhance the accuracy and efficiency of image data labeling processes. These advancements not only improve the quality of labeled data but also reduce the time and cost associated with manual labeling. The integration of AI and machine learning algorithms in the labeling process is further boosting the market growth by automating repetitive tasks and minimizing human errors.

From a regional perspective, North America holds the largest market share due to early adoption of advanced technologies and the presence of major AI and ML companies. The region is expected to maintain its dominance during the forecast period, driven by continuous technological advancements and substantial investments in AI research. Asia Pacific is anticipated to witness the highest growth rate due to the rising adoption of AI technologies in countries like China, Japan, and India. The increasing focus on digital transformation and government initiatives to promote AI adoption are significant factors contributing to the regional market growth.

Type Analysis

The image data labeling service market is segmented into three primary types: manual labeling, semi-automatic labeling, and automatic labeling. Manual labeling, which involves human annotators tagging images, is essential for ensuring high accuracy, especially in complex tasks. Despite being time-consuming and labor-intensive, manual labeling is widely used in applications where nuanced understanding and precision are paramount. This segment continues to hold a significant market share due to the reliability it offers. However, the cost and time constraints associated with manual labeling are driving the growth of more advanced labeling techniques.

Semi-automatic labeling combines human intervention with automated processes, providing a balance between accuracy and efficiency. In this approach, algorithms perform initial labeling, and human annotators refine and validate the results. This method significantly reduces the time required for data labeling while maintaining high accuracy levels. The semi-automatic labeling segment is gaining traction as it offers a scalable and cost-effective solution, particularly beneficial for industries dealing with large volumes of data, such as retail and IT.

Automatic labeling, driven by AI and machine learning algorithms, represents the most advanced segment of the market. This approach leverages sophisticated models to autonomously label image data with minimal human intervention. The continuous improvement in AI algorithms, along with the availability of large datasets for training, has enhanced the accuracy and reliability of automatic lab

The test set from "Titanic - Machine Learning from Disaster" doesn't include labels.

This is an augmented version of the test set with the correct labels, retrieved from the original Titanic dataset at: https://www.openml.org/search?type=data&sort=runs&id=40945&status=active

The accuracy of the labels was validated by getting a 1.0 score in the competition with them.

This dataset is provided for educational purposes, and is not intended to help people cheat in the competition. If the only reason you want to download this is so you can get a shiny 1.0 on the leaderboards, don't do it.

covering up to the 12th fret.

This dataset was created for the Paper From Group to Individual Labels using Deep Features , Kotzias et. al,. KDD 2015 Please cite the paper if you want to use it :) It contains sentences labelled with positive or negative sentiment. ### Format: sentence score ### Details: Score is either 1 (for positive) or 0 (for negative) The sentences come from three different websites/fields: imdb.com amazon.com yelp.com For each website, there exist 500 positive and 500 negative sentences. Those were selected randomly for larger datasets of reviews. We attempted to select sentences that have a clearly positive or negative connotaton, the goal was for no neutral sentences to be selected. ### Attribute Information: The attributes are text sentences, extracted from reviews of products, movies, and restaurants ### Relevant Papers: From Group to Individual Labels using Deep Features , Kotzias et. al,. KDD 2015

Dataset Card for "climate-tagging-labelled-datasets"

More Information needed

This is the part of training labelled Dataset-B for the publication "P/S separation of multi-component seismic data at land surface based on deep learning".

There are 500-shot data labels:

Train/, Val/ & Test/ are the separated file for Training, Validation & Testing

The labelled data size are nx*nz=1001*3001 using a IEEE float format

One can use the Seisic Unix command to plot for QC: ximage < shotx_mod_1.dat n1=3001 perc=99 &

In each directory, the files are named as follows:

The horizontal component:

shotx_mod_*.dat

The vertical component:

shotz_mod_*.dat

The P-wave label :

shotp_*.dat

The S-wave label :

shots_*.dat

The resume files have the extension .txt and the corresponding labels are in a file with the extension .lab.

To cite this :

Jiechieu, K.F.F., Tsopze, N. Skills prediction based on multi-label resume classification using CNN with model predictions explanation. Neural Comput & Applic (2020).

This dataset is a multi-labelled SMILES odor dataset with 138 odor descriptors. This dataset was created for replicating the paper: A principal odor map unifies diverse tasks in olfactory perception.

The complete replication of the paper (dataset curation + model) can be found in the OpenPOM GitHub repository.

The dataset contains 4983 molecules, each described by multiple odor labels (e.g. creamy, grassy), was made by combining the GoodScents and Leffingwell PMP 2001 datasets each containing odorant molecules and corresponding odor descriptors.

Supervised machine learning methods for image analysis require large amounts of labelled training data to solve computer vision problems. The recent rise of deep learning algorithms for recognising image content has led to the emergence of many ad-hoc labelling tools. With this survey, we capture and systematise the commonalities as well as the distinctions between existing image labelling software. We perform a structured literature review to compile the underlying concepts and features of image labelling software such as annotation expressiveness and degree of automation. We structure the manual labelling task by its organisation of work, user interface design options, and user support techniques to derive a systematisation schema for this survey. Applying it to available software and the body of literature, enabled us to uncover several application archetypes and key domains such as image retrieval or instance identification in healthcare or television.

Hand Labelled Expierment DAta

## Overview

Hand Labelled Expierment DAta is a dataset for object detection tasks - it contains Mountains annotations for 659 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).

Being an Image labeling expert, we have immense experience in various types of data annotation services. We Annotate data quickly and effectively with our patented Automated Data Labelling tool along with our in-house, full-time, and highly trained annotators.

We can label the data with the following features:

1. Image classification
2. Object detection
3. Semantic segmentation
4. Image tagging
5. Text annotation
6. Point cloud annotation
7. Key-Point annotation
8. Custom user-defined labeling

Data Services we provide:

1. Data collection & sourcing
2. Data cleaning
3. Data mining
4. Data labeling
5. Data management​

We have an AI-enabled training data platform "ADVIT", the most advanced Deep Learning (DL) platform to create, manage high-quality training data and DL models all in one place.

Coast Train is a library of images of coastal environments, annotations, and corresponding thematic label masks (or ‘label images’) collated for the purposes of training and evaluating machine learning (ML), deep learning, and other models for image segmentation. It includes image sets from both geospatial satellite, aerial, and UAV imagery and orthomosaics, as well as non-geospatial oblique and nadir imagery. Images include a diverse range of coastal environments from the U.S. Pacific, Gulf of Mexico, Atlantic, and Great Lakes coastlines, consisting of time-series of high-resolution (≤1m) orthomosaics and satellite image tiles (10–30m). Each image, image annotation, and labelled image is available as a single NPZ zipped file. NPZ files follow the following naming convention: {datasource}{numberofclasses}{threedigitdatasetversion}.zip, where {datasource} is the source of the original images (for example, NAIP, Landsat 8, Sentinel 2), {numberofclasses} is the number of classes used to annotate the images, and {threedigitdatasetversion} is the three-digit code corresponding to the dataset version (in other words, 001 is version 1). Each zipped folder contains a collection of NPZ format files, each of which corresponds to an individual image. An individual NPZ file is named after the image that it represents and contains (1) a CSV file with detail information for every image in the zip folder and (2) a collection of the following NPY files: orig_image.npy (original input image unedited), image.npy (original input image after color balancing and normalization), classes.npy (list of classes annotated and present in the labelled image), doodles.npy (integer image of all image annotations), color_doodles.npy (color image of doodles.npy), label.npy (labelled image created from the classes present in the annotations), and settings.npy (annotation and machine learning settings used to generate the labelled image from annotations). All NPZ files can be extracted using the utilities available in Doodler (Buscombe, 2022). A merged CSV file containing detail information on the complete imagery collection is available at the top level of this data release, details of which are available in the Entity and Attribute section of this metadata file.