100+ datasets found
  1. R

    Hand Labelled Dataset

    • universe.roboflow.com
    zip
    Updated Aug 22, 2022
    + more versions
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    Hand labelled (2022). Hand Labelled Dataset [Dataset]. https://universe.roboflow.com/hand-labelled/hand-labelled
    Explore at:
    zipAvailable download formats
    Dataset updated
    Aug 22, 2022
    Dataset authored and provided by
    Hand labelled
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Hand Labelled Bounding Boxes
    Description

    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).
    
  2. i

    gun dataset labelled

    • ieee-dataport.org
    Updated Feb 17, 2020
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    pranav nerurkar (2020). gun dataset labelled [Dataset]. https://ieee-dataport.org/documents/gun-dataset-labelled
    Explore at:
    Dataset updated
    Feb 17, 2020
    Authors
    pranav nerurkar
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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

  3. Z

    A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and...

    • data.niaid.nih.gov
    • zenodo.org
    • +2more
    Updated Jul 20, 2024
    + more versions
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    Shao, Mingchen (2024). A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and other sources about the 2024 outbreak of Measles [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_11711229
    Explore at:
    Dataset updated
    Jul 20, 2024
    Dataset provided by
    Thakur, Nirmalya
    Shao, Mingchen
    Bian, Andrew
    Su, Vanessa
    Patel, Kesha A.
    Jeong, Hongseok
    Knieling, Victoria
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    YouTube
    Description

    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.

  4. R

    Hariyo Chasma Labelled Dataset Dataset

    • universe.roboflow.com
    zip
    Updated May 9, 2024
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    Hariyo Chasma (2024). Hariyo Chasma Labelled Dataset Dataset [Dataset]. https://universe.roboflow.com/hariyo-chasma/hariyo-chasma-labelled-dataset
    Explore at:
    zipAvailable download formats
    Dataset updated
    May 9, 2024
    Dataset authored and provided by
    Hariyo Chasma
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Person Bounding Boxes
    Description

    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).
    
  5. h

    arxiv-cs-papers-with-datasets-labelled

    • huggingface.co
    Updated Oct 15, 2015
    + more versions
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    Librarian Bots (2015). arxiv-cs-papers-with-datasets-labelled [Dataset]. https://huggingface.co/datasets/librarian-bots/arxiv-cs-papers-with-datasets-labelled
    Explore at:
    Dataset updated
    Oct 15, 2015
    Dataset authored and provided by
    Librarian Bots
    Description

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

  6. P

    Large Labelled Logo Dataset (L3D) Dataset

    • paperswithcode.com
    Updated Dec 9, 2021
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    Asier Gutiérrez-Fandiño; David Pérez-Fernández; Jordi Armengol-Estapé (2021). Large Labelled Logo Dataset (L3D) Dataset [Dataset]. https://paperswithcode.com/dataset/large-labelled-logo-dataset-l3d
    Explore at:
    Dataset updated
    Dec 9, 2021
    Authors
    Asier Gutiérrez-Fandiño; David Pérez-Fernández; Jordi Armengol-Estapé
    Description

    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

  7. d

    Kieli NLP Data - Fully-labelled Audio & Text Dataset for Machine Learning &...

    • datarade.ai
    Updated Mar 20, 2021
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    Kieli (2021). Kieli NLP Data - Fully-labelled Audio & Text Dataset for Machine Learning & AI platforms [Dataset]. https://datarade.ai/data-products/a-fully-labelled-dataset-for-machine-learning-and-ai-platforms-kieli
    Explore at:
    .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset updated
    Mar 20, 2021
    Dataset authored and provided by
    Kieli
    Area covered
    Djibouti, Denmark, Antigua and Barbuda, Ethiopia, Uruguay, Tajikistan, Mauritius, Fiji, Venezuela (Bolivarian Republic of), Anguilla
    Description

    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.

  8. P

    100STLYE-Labelled Dataset

    • paperswithcode.com
    + more versions
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    Ian Mason; Sebastian Starke; Taku Komura, 100STLYE-Labelled Dataset [Dataset]. https://paperswithcode.com/dataset/100stlye-labelled
    Explore at:
    Authors
    Ian Mason; Sebastian Starke; Taku Komura
    Description

    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.

  9. h

    mmlu-auxiliary-train-auto-labelled

    • huggingface.co
    Updated Feb 24, 2024
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    Kaizhao Liang (2024). mmlu-auxiliary-train-auto-labelled [Dataset]. https://huggingface.co/datasets/kz919/mmlu-auxiliary-train-auto-labelled
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 24, 2024
    Authors
    Kaizhao Liang
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    Dataset Card for MMLU Auxiliary Trained Set Labelled by e5-mistral-7b-instruct

      Dataset Description
    
    
    
    
    
      Dataset Summary
    

    This dataset, named "MMLU Auxiliary Trained Set Labelled by e5-mistral-7b-instruct," consists of 99,842 examples spanning various subjects. Each instance includes a question, multiple choice options, a subject category, and an answer. The unique aspect of this dataset is the task label for each question, generated by a zero-shot classifier… See the full description on the dataset page: https://huggingface.co/datasets/kz919/mmlu-auxiliary-train-auto-labelled.

  10. Titanic - Labelled Test Set

    • kaggle.com
    Updated May 30, 2023
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    Wesley Howe (2023). Titanic - Labelled Test Set [Dataset]. https://www.kaggle.com/datasets/wesleyhowe/titanic-labelled-test-set
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 30, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Wesley Howe
    Description

    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.

  11. Image Data Labeling Service Market Report | Global Forecast From 2025 To...

    • dataintelo.com
    csv, pdf, pptx
    Updated Oct 16, 2024
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    Dataintelo (2024). Image Data Labeling Service Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/image-data-labeling-service-market
    Explore at:
    csv, pdf, pptxAvailable download formats
    Dataset updated
    Oct 16, 2024
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    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

  12. i

    TapToTab: A Pitch-Labelled Guitar Dataset for Note Recognition

    • ieee-dataport.org
    Updated Sep 4, 2024
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    Hanan Hindy (2024). TapToTab: A Pitch-Labelled Guitar Dataset for Note Recognition [Dataset]. https://ieee-dataport.org/documents/taptotab-pitch-labelled-guitar-dataset-note-recognition
    Explore at:
    Dataset updated
    Sep 4, 2024
    Authors
    Hanan Hindy
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    covering up to the 12th fret.

  13. a

    Sentiment Labelled Sentences Data Set

    • academictorrents.com
    bittorrent
    Updated Aug 26, 2016
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    None (2016). Sentiment Labelled Sentences Data Set [Dataset]. https://academictorrents.com/details/07e05fc1229555e124df72160a01b2540d04cebf
    Explore at:
    bittorrent(512208)Available download formats
    Dataset updated
    Aug 26, 2016
    Authors
    None
    License

    https://academictorrents.com/nolicensespecifiedhttps://academictorrents.com/nolicensespecified

    Description

    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

  14. h

    climate-tagging-labelled-datasets

    • huggingface.co
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    maayansharon, climate-tagging-labelled-datasets [Dataset]. https://huggingface.co/datasets/maayansharon/climate-tagging-labelled-datasets
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Authors
    maayansharon
    Description

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

    More Information needed

  15. h

    reddit-labelled

    • huggingface.co
    Updated Jul 26, 2023
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    Alosh Denny (2023). reddit-labelled [Dataset]. https://huggingface.co/datasets/aoxo/reddit-labelled
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 26, 2023
    Authors
    Alosh Denny
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    aoxo/reddit-labelled dataset hosted on Hugging Face and contributed by the HF Datasets community

  16. Labelled data for P/S separation with CNN

    • zenodo.org
    application/gzip, bin
    Updated Dec 22, 2022
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    He Huang; Tengfei Wang; Jiubing Cheng; He Huang; Tengfei Wang; Jiubing Cheng (2022). Labelled data for P/S separation with CNN [Dataset]. http://doi.org/10.5281/zenodo.7471043
    Explore at:
    application/gzip, binAvailable download formats
    Dataset updated
    Dec 22, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    He Huang; Tengfei Wang; Jiubing Cheng; He Huang; Tengfei Wang; Jiubing Cheng
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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

  17. GitHub florex resume corpus

    • kaggle.com
    Updated Apr 27, 2023
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    Amr Abdelaal (2023). GitHub florex resume corpus [Dataset]. https://www.kaggle.com/datasets/amrwael/github-florex-resume-corpus/data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 27, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Amr Abdelaal
    Description

    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).

  18. P

    Multi-Labelled SMILES Odors dataset Dataset

    • paperswithcode.com
    Updated Aug 31, 2023
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    (2023). Multi-Labelled SMILES Odors dataset Dataset [Dataset]. https://paperswithcode.com/dataset/multi-labelled-smiles-odors-dataset
    Explore at:
    Dataset updated
    Aug 31, 2023
    Description

    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.

  19. f

    Data from: A survey of image labelling for computer vision applications

    • tandf.figshare.com
    docx
    Updated May 31, 2023
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    Christoph Sager; Christian Janiesch; Patrick Zschech (2023). A survey of image labelling for computer vision applications [Dataset]. http://doi.org/10.6084/m9.figshare.14445354.v1
    Explore at:
    docxAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    Taylor & Francis
    Authors
    Christoph Sager; Christian Janiesch; Patrick Zschech
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  20. R

    Hand Labelled Expierment Data Dataset

    • universe.roboflow.com
    zip
    Updated Jul 31, 2022
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    Hand labelled (2022). Hand Labelled Expierment Data Dataset [Dataset]. https://universe.roboflow.com/hand-labelled/hand-labelled-expierment-data
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jul 31, 2022
    Dataset authored and provided by
    Hand labelled
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Mountains Bounding Boxes
    Description

    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).
    
Share
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Close
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Hand labelled (2022). Hand Labelled Dataset [Dataset]. https://universe.roboflow.com/hand-labelled/hand-labelled

Hand Labelled Dataset

hand-labelled

hand-labelled-dataset

Explore at:
zipAvailable download formats
Dataset updated
Aug 22, 2022
Dataset authored and provided by
Hand labelled
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Variables measured
Hand Labelled Bounding Boxes
Description

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).
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