100+ datasets found
  1. R

    Convert Jason File To Yolo Format Dataset

    • universe.roboflow.com
    zip
    Updated Aug 16, 2023
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    computer vision learning (2023). Convert Jason File To Yolo Format Dataset [Dataset]. https://universe.roboflow.com/computer-vision-learning/convert-jason-file-to-yolo-format
    Explore at:
    zipAvailable download formats
    Dataset updated
    Aug 16, 2023
    Dataset authored and provided by
    computer vision learning
    License

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

    Variables measured
    Doors Polygons
    Description

    Convert Jason File To Yolo Format

    ## Overview
    
    Convert Jason File To Yolo Format is a dataset for instance segmentation tasks - it contains Doors annotations for 316 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. Gun Dataset YOLO v8

    • kaggle.com
    Updated Oct 3, 2024
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    Abuzar Khan (2024). Gun Dataset YOLO v8 [Dataset]. https://www.kaggle.com/datasets/abuzarkhaaan/helmetandguntesting
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 3, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Abuzar Khan
    License

    https://www.reddit.com/wiki/apihttps://www.reddit.com/wiki/api

    Description

    This dataset contains labeled data for gun detection collected from various videos on YouTube. The dataset has been specifically curated and labeled by me to aid in training machine learning models, particularly for real-time gun detection tasks. It is formatted for easy use with YOLO (You Only Look Once), one of the most popular object detection models.

    Key Features: Source: The videos were sourced from YouTube and feature diverse environments, including indoor and outdoor settings, with varying lighting conditions and backgrounds. Annotations: The dataset is fully labeled with bounding boxes around guns, following the YOLO format (.txt files for annotations). Each annotation provides the class (gun) and the coordinates of the bounding box. YOLO-Compatible: The dataset is ready to be used with any YOLO model (YOLOv3, YOLOv4, YOLOv5, etc.), ensuring seamless integration for object detection training. Realistic Scenarios: The dataset includes footage of guns from various perspectives and angles, making it useful for training models that can generalize to real-world detection tasks. This dataset is ideal for researchers and developers working on gun detection systems, security applications, or surveillance systems that require fast and accurate detection of firearms.

  3. yolo format dataset

    • kaggle.com
    Updated Oct 16, 2023
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    meer atif magsi (2023). yolo format dataset [Dataset]. https://www.kaggle.com/datasets/meeratif/yolo-format-data/data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 16, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    meer atif magsi
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Dataset Highlights:

    1. Diverse Image Collection: Our dataset encompasses a wide range of general images covering various categories such as objects, scenes, people, and more. The images are carefully curated to offer a rich source of visual data.

    2. Sindhi Language Titles: One of the distinctive features of our dataset is the inclusion of Sindhi language titles for each image.

    3. Annotations in YOLO Format: To facilitate your object detection tasks, we have meticulously annotated the images in YOLO format, making it compatible with the YOLOv3 or YOLOv4 models. This ensures that you can jump right into training your model without the hassle of converting annotations.

    4. Comprehensive Metadata: Each image in the dataset is accompanied by a YAML file providing additional metadata, including information about the image source, date of capture, and any relevant context that may be useful for your research.

    By publishing this YOLO-style dataset with Sindhi language titles, we aim to contribute to the machine learning and computer vision community, fostering innovation and inclusivity in the field. We encourage you to explore, experiment, and create cutting-edge models using this dataset, and we look forward to seeing the incredible projects that emerge from it.

  4. R

    Dataset Format Conversion (yolo Txt To Createml Json) Dataset

    • universe.roboflow.com
    zip
    Updated Nov 20, 2021
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    Alvaro Sosa (2021). Dataset Format Conversion (yolo Txt To Createml Json) Dataset [Dataset]. https://universe.roboflow.com/alvaro-sosa/dataset-format-conversion--yolo-txt-to-createml-json
    Explore at:
    zipAvailable download formats
    Dataset updated
    Nov 20, 2021
    Dataset authored and provided by
    Alvaro Sosa
    License

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

    Variables measured
    Drones Bounding Boxes
    Description

    Dataset Format Conversion (yolo Txt To CreateML Json)

    ## Overview
    
    Dataset Format Conversion (yolo Txt To CreateML Json) is a dataset for object detection tasks - it contains Drones annotations for 1,339 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. g

    Glasses Detection Yolo Format

    • gts.ai
    json
    Updated Apr 21, 2024
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    GTS (2024). Glasses Detection Yolo Format [Dataset]. https://gts.ai/dataset-download/glasses-detection-yolo-format/
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Apr 21, 2024
    Dataset provided by
    GLOBOSE TECHNOLOGY SOLUTIONS PRIVATE LIMITED
    Authors
    GTS
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Leverage our dataset to enhance your computer vision models, improve accessory detection algorithms, or enrich your research in optical recognition technologies.

  6. P

    Paimon Dataset YOLO Detection Dataset

    • paperswithcode.com
    • gts.ai
    Updated Dec 3, 2024
    + more versions
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    (2024). Paimon Dataset YOLO Detection Dataset [Dataset]. https://paperswithcode.com/dataset/paimon-dataset-yolo-detection
    Explore at:
    Dataset updated
    Dec 3, 2024
    Description

    Description:

    👉 Download the dataset here

    This dataset consists of a diverse collection of images featuring Paimon, a popular character from the game Genshin Impact. The images have been sourced from in-game gameplay footage and capture Paimon from various angles and in different sizes (scales), making the dataset suitable for training YOLO object detection models.

    The dataset provides a comprehensive view of Paimon in different lighting conditions, game environments, and positions, ensuring the model can generalize well to similar characters or object detection tasks. While most annotations are accurately labeled, a small number of annotations may include minor inaccuracies due to manual labeling errors. This is ideal for researchers and developers working on character recognition, object detection in gaming environments, or other AI vision tasks.

    Download Dataset

    Dataset Features:

    Image Format: .jpg files in 640×320 resolution.

    Annotation Format: .txt files in YOLO format, containing bounding box data with:

    class_id

    x_center

    y_center

    width

    height

    Use Cases:

    Character Detection in Games: Train YOLO models to detect and identify in-game characters or NPCs.

    Gaming Analytics: Improve recognition of specific game elements for AI-powered game analytics tools.

    Research: Contribute to academic research focused on object detection or computer vision in animated and gaming environments.

    Data Structure:

    Images: High-quality .jpg images captured from multiple perspectives, ensuring robust model training across various orientations and lighting scenarios.

    Annotations: Each image has an associated .txt file that follows the YOLO format. The annotations are structured to include class identification, object location (center coordinates), and

    bounding box dimensions.

    Key Advantages:

    Varied Angles and Scales: The dataset includes Paimon from multiple perspectives, aiding in creating more versatile and adaptable object detection models.

    Real-World Scenario: Extracted from actual gameplay footage, the dataset simulates real-world detection challenges such as varying backgrounds, motion blur, and changing character scales.

    Training Ready: Suitable for training YOLO models and other deep learning frameworks that require object detection capabilities.

    This dataset is sourced from Kaggle.

  7. i

    Indian Traffic Sign Detection Benchmark Dataset in YOLO Format

    • ieee-dataport.org
    Updated Feb 15, 2022
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    Muhammad KP (2022). Indian Traffic Sign Detection Benchmark Dataset in YOLO Format [Dataset]. https://ieee-dataport.org/documents/indian-traffic-sign-detection-benchmark-dataset-yolo-format
    Explore at:
    Dataset updated
    Feb 15, 2022
    Authors
    Muhammad KP
    License

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

    Description

    mosaic etc.. are also present. The dataset has images in 3 different types of traffic signs in India. Dataset is annotated only as one class-Traffic Sign.

  8. R

    Yolo Coco Data Format Dataset

    • universe.roboflow.com
    zip
    Updated Apr 29, 2025
    + more versions
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    Md Abdur Rob (2025). Yolo Coco Data Format Dataset [Dataset]. https://universe.roboflow.com/md-abdur-rob-x4zgr/yolo-coco-data-format/model/1
    Explore at:
    zipAvailable download formats
    Dataset updated
    Apr 29, 2025
    Dataset authored and provided by
    Md Abdur Rob
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Variables measured
    Objects Bounding Boxes
    Description

    YOLO Coco Data Format

    ## Overview
    
    YOLO Coco Data Format is a dataset for object detection tasks - it contains Objects annotations for 692 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).
    
  9. Traffic Road Object Detection Dataset using YOLO.

    • kaggle.com
    Updated Nov 8, 2023
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    ilyesBoukraa (2023). Traffic Road Object Detection Dataset using YOLO. [Dataset]. https://www.kaggle.com/datasets/boukraailyesali/traffic-road-object-detection-dataset-using-yolo
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    ilyesBoukraa
    License

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

    Description

    Dataset Description: Car Object Detection in Road Traffic

    Overview:

    This dataset is designed for car object detection in road traffic scenes (Images with shape 1080x1920x3). The dataset is derived from publicly available video content on YouTube, specifically from the video with the Creative Commons Attribution license, available here. https://youtu.be/MNn9qKG2UFI?si=uJz_WicTCl8zfrVl" alt="youtube video">

    Source:

    • Video Source: YouTube Video.
    • License: Creative Commons Attribution (reuse allowed) more details here.
    • Dataset Contents: The dataset consists of a collection of image frames extracted from the video. Each image frame captures various scenes from road traffic. Car objects within these frames are annotated with bounding boxes.

    Annotation Details:

    • Bounding Boxes: Each image frame contains annotated bounding boxes around car objects, marking their locations in the scene.
    • Classes: The dataset is focused on car object detection, and car objects are labeled as the target class (aka one class only).
    • Data Format: Images are provided in JPEG format.
    • Annotation files are provided in YOLO text format.
    • We used labelImg GUI to label this dataset in YOLO format, more details are in this GitHub repo.

    Use Cases:

    • Object Detection: This dataset can be used to train and evaluate object detection models, with an emphasis on detecting cars in road traffic scenarios.

    Acknowledgments: We acknowledge and thank the creator of the original video for making it available under a Creative Commons Attribution license. Their contribution enables the development of datasets and research in the field of computer vision and object detection.

    Disclaimer: This dataset is provided for educational and research purposes and should be used in compliance with YouTube's terms of service and the Creative Commons Attribution license.

  10. h

    shelf-segmentation-train

    • huggingface.co
    Updated Jul 15, 2025
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    The citation is currently not available for this dataset.
    Explore at:
    Dataset updated
    Jul 15, 2025
    Authors
    cheese
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Custom YOLO Dataset

    This dataset is formatted for YOLO-based instance segmentation. It includes images and annotations for training, validation, and testing.

      Dataset structure
    

    train/images, valid/images, test/images: JPEG image files train/labels, valid/labels, test/labels: YOLO-format .txt annotations data.yaml: defines class names and split locations

  11. g

    Facial Expression Image Data AFFECTNET YOLO Format

    • gts.ai
    json
    Updated Mar 20, 2024
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    GTS (2024). Facial Expression Image Data AFFECTNET YOLO Format [Dataset]. https://gts.ai/dataset-download/facial-expression-image-data-affectnet-yolo-format/
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Mar 20, 2024
    Dataset provided by
    GLOBOSE TECHNOLOGY SOLUTIONS PRIVATE LIMITED
    Authors
    GTS
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    This dataset AFFECTNET YOLO Format is aimed to be used in facial expression detection for a YOLO project...

  12. I

    dataset_yolo

    • app.ikomia.ai
    Updated Jun 28, 2023
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    Ikomia (2023). dataset_yolo [Dataset]. https://app.ikomia.ai/hub/algorithms/dataset_yolo/
    Explore at:
    Dataset updated
    Jun 28, 2023
    Dataset authored and provided by
    Ikomia
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Load YOLO dataset Load YOLO dataset. This plugin converts a given dataset in YOLO format to Ikomia format. Once loaded, all images can be visualized with their respective annotations. Then, any training algorithms from the Ikomia marketplace can be connected to this converter....

  13. R

    Format Yolo (no Osci) Dataset

    • universe.roboflow.com
    zip
    Updated Nov 26, 2022
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    Universitas Surabaya (2022). Format Yolo (no Osci) Dataset [Dataset]. https://universe.roboflow.com/universitas-surabaya/format-yolo-no-osci
    Explore at:
    zipAvailable download formats
    Dataset updated
    Nov 26, 2022
    Dataset authored and provided by
    Universitas Surabaya
    License

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

    Variables measured
    Algae Bounding Boxes
    Description

    Format Yolo (no Osci)

    ## Overview
    
    Format Yolo (no Osci) is a dataset for object detection tasks - it contains Algae annotations for 269 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).
    
  14. h

    tinyperson-yolo-detection-format

    • huggingface.co
    Updated Apr 26, 2025
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    Niyuta Liu (2025). tinyperson-yolo-detection-format [Dataset]. https://huggingface.co/datasets/Niyuta/tinyperson-yolo-detection-format
    Explore at:
    Dataset updated
    Apr 26, 2025
    Authors
    Niyuta Liu
    License

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

    Description

    Niyuta/tinyperson-yolo-detection-format dataset hosted on Hugging Face and contributed by the HF Datasets community

  15. R

    Yolo V3 Person Dataset

    • universe.roboflow.com
    zip
    Updated Jul 15, 2024
    + more versions
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    yolo v3 (2024). Yolo V3 Person Dataset [Dataset]. https://universe.roboflow.com/yolo-v3-dyfia/yolo-v3-person/dataset/1
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jul 15, 2024
    Dataset authored and provided by
    yolo v3
    License

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

    Variables measured
    S Bounding Boxes
    Description

    Yolo V3 Person

    ## Overview
    
    Yolo V3 Person is a dataset for object detection tasks - it contains S annotations for 4,544 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).
    
  16. c

    Annotated Fire -Smoke Image Dataset for fire detection Using YOLO.

    • acquire.cqu.edu.au
    zip
    Updated Apr 14, 2025
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    Shouthiri Partheepan (2025). Annotated Fire -Smoke Image Dataset for fire detection Using YOLO. [Dataset]. http://doi.org/10.25946/28747046.v1
    Explore at:
    zipAvailable download formats
    Dataset updated
    Apr 14, 2025
    Dataset provided by
    CQUniversity
    Authors
    Shouthiri Partheepan
    License

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

    Description

    This dataset contains 11027 labeled images for the detection of fire and smoke instances in diverse real-world scenarios. The annotations are provided in YOLO format with bounding boxes and class labels for two classes: fire and smoke. The dataset is divided into an 80% training set with 10,090 fire instances and 9724 smoke instances, a 10% Validation set with 1,255 fire and 1,241 smoke instances, and a 10% Test set with 1,255 fire and 1,241 smoke instances. This dataset is suitable for training and evaluating fire and smoke detection models, such as YOLOv8, YOLOv9, and similar deep learning-based frameworks in the context of emergency response, wildfire monitoring, and smart surveillance.

  17. League of Legends Champion Mini-Map Dataset

    • kaggle.com
    Updated May 8, 2020
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    Yadola (2020). League of Legends Champion Mini-Map Dataset [Dataset]. http://doi.org/10.34740/kaggle/dsv/1140364
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 8, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Yadola
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    League of Legends is a MOBA (Multiplayer Online Battle Arena) where 2 teams (blue and red) face off. There are 3 lanes, a jungle, and 5 roles. The goal is to take down the enemy Nexus to win the game.

    Content

    This dataset contains plain images and noised images with bounding boxes drawn to identify the champions from within the mini-map throughout the course of a game of League of Legends.

    The Champions currently available are (the numbers show the class number for the relevant champion): 0 - Veigar 1 - Diana 2 - Vladimir 3 - Ryze 4 - Ekko 5 - Irelia 6 - Master Yi 7 - Nocturne 8 - Pantheon 9 - Yorick

    A YOLOv3 weights file is also added where it has been trained to identify the above mentioned champions.

    Acknowledgements

    I would like to thank Riot Games for developing and supporting League of Legends and Make Sense AI for enabling the creation of this dataset.

    Inspiration

    This dataset is available to help encourage and improve the information captured from the mini-map during the course of a League of Legends Game by developers.

  18. R

    Annotation Format Dataset

    • universe.roboflow.com
    zip
    Updated Nov 1, 2023
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    prakhar (2023). Annotation Format Dataset [Dataset]. https://universe.roboflow.com/prakhar-jzjmf/annotation-format/dataset/3
    Explore at:
    zipAvailable download formats
    Dataset updated
    Nov 1, 2023
    Dataset authored and provided by
    prakhar
    License

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

    Variables measured
    Vehicles Bounding Boxes
    Description

    Annotation Format

    ## Overview
    
    Annotation Format is a dataset for object detection tasks - it contains Vehicles annotations for 739 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).
    
  19. i

    Ecuadorian Traffic Officer Detection for Autonomous Vehicles in YOLOv8...

    • ieee-dataport.org
    Updated Mar 6, 2025
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    Juan Ortiz (2025). Ecuadorian Traffic Officer Detection for Autonomous Vehicles in YOLOv8 format [Dataset]. https://ieee-dataport.org/documents/ecuadorian-traffic-officer-detection-autonomous-vehicles-yolov8-format
    Explore at:
    Dataset updated
    Mar 6, 2025
    Authors
    Juan Ortiz
    License

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

    Description

    1734 images for training

  20. Synthetic Dyslexia Handwriting Dataset (YOLO-Format)

    • zenodo.org
    zip
    Updated Feb 11, 2025
    + more versions
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    Nora Fink; Nora Fink (2025). Synthetic Dyslexia Handwriting Dataset (YOLO-Format) [Dataset]. http://doi.org/10.5281/zenodo.14852659
    Explore at:
    zipAvailable download formats
    Dataset updated
    Feb 11, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Nora Fink; Nora Fink
    License

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

    Description

    Description
    This synthetic dataset has been generated to facilitate object detection (in YOLO format) for research on dyslexia-related handwriting patterns. It builds upon an original corpus of uppercase and lowercase letters obtained from multiple sources: the NIST Special Database 19 111, the Kaggle dataset “A-Z Handwritten Alphabets in .csv format” 222, as well as handwriting samples from dyslexic primary school children of Seberang Jaya, Penang (Malaysia).

    In the original dataset, uppercase letters originated from NIST Special Database 19, while lowercase letters came from the Kaggle dataset curated by S. Patel. Additional images (categorized as Normal, Reversal, and Corrected) were collected and labeled based on handwriting samples of dyslexic and non-dyslexic students, resulting in:

    • 78,275 images labeled as Normal
    • 52,196 images labeled as Reversal
    • 8,029 images labeled as Corrected

    Building upon this foundation, the Synthetic Dyslexia Handwriting Dataset presented here was programmatically generated to produce labeled examples suitable for training and validating object detection models. Each synthetic image arranges multiple letters of various classes (Normal, Reversal, Corrected) in a “text line” style on a black background, providing YOLO-compatible .txt annotations that specify bounding boxes for each letter.

    Key Points of the Synthetic Generation Process

    1. Letter-Level Source Data
      Individual characters were sampled from the original image sets.
    2. Randomized Layout
      Letters are randomly assembled into words and lines, ensuring a wide variety of visual arrangements.
    3. Bounding Box Labels
      Each character is assigned a bounding box with (x, y, width, height) in YOLO format.
    4. Class Annotations
      Classes include 0 = Normal, 1 = Reversal, and 2 = Corrected.
    5. Preservation of Visual Characteristics
      Letters retain their key dyslexia-relevant features (e.g., reversals).

    Historical References & Credits

    If you are using this synthetic dataset or the original Dyslexia Handwriting Dataset, please cite the following papers:

    • M. S. A. B. Rosli, I. S. Isa, S. A. Ramlan, S. N. Sulaiman and M. I. F. Maruzuki, "Development of CNN Transfer Learning for Dyslexia Handwriting Recognition," 2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2021, pp. 194–199, doi: 10.1109/ICCSCE52189.2021.9530971.
    • N. S. L. Seman, I. S. Isa, S. A. Ramlan, W. Li-Chih and M. I. F. Maruzuki, "Notice of Removal: Classification of Handwriting Impairment Using CNN for Potential Dyslexia Symptom," 2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2021, pp. 188–193, doi: 10.1109/ICCSCE52189.2021.9530989.
    • Isa, Iza Sazanita. CNN Comparisons Models On Dyslexia Handwriting Classification / Iza Sazanita Isa … [et Al.]. Universiti Teknologi MARA Cawangan Pulau Pinang, 2021.
    • Isa, I. S., Rahimi, W. N. S., Ramlan, S. A., & Sulaiman, S. N. (2019). Automated detection of dyslexia symptom based on handwriting image for primary school children. Procedia Computer Science, 163, 440–449.

    References to Original Data Sources

    111 P. J. Grother, “NIST Special Database 19,” NIST, 2016. [Online]. Available:
    https://www.nist.gov/srd/nist-special-database-19

    222 S. Patel, “A-Z Handwritten Alphabets in .csv format,” Kaggle, 2017. [Online]. Available:
    https://www.kaggle.com/sachinpatel21/az-handwritten-alphabets-in-csv-format

    Usage & Citation

    Researchers and practitioners are encouraged to integrate this synthetic dataset into their computer vision pipelines for tasks such as dyslexia pattern analysis, character recognition, and educational technology development. Please cite the original authors and publications if you utilize this synthetic dataset in your work.

    Password Note (Original Data)

    The original RAR file was password-protected with the password: WanAsy321. This synthetic dataset, however, is provided openly for streamlined usage.

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computer vision learning (2023). Convert Jason File To Yolo Format Dataset [Dataset]. https://universe.roboflow.com/computer-vision-learning/convert-jason-file-to-yolo-format

Convert Jason File To Yolo Format Dataset

convert-jason-file-to-yolo-format

convert-jason-file-to-yolo-format-dataset

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zipAvailable download formats
Dataset updated
Aug 16, 2023
Dataset authored and provided by
computer vision learning
License

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

Variables measured
Doors Polygons
Description

Convert Jason File To Yolo Format

## Overview

Convert Jason File To Yolo Format is a dataset for instance segmentation tasks - it contains Doors annotations for 316 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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