Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## 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).
https://www.reddit.com/wiki/apihttps://www.reddit.com/wiki/api
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.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
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.
Sindhi Language Titles: One of the distinctive features of our dataset is the inclusion of Sindhi language titles for each image.
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.
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## 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).
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Leverage our dataset to enhance your computer vision models, improve accessory detection algorithms, or enrich your research in optical recognition technologies.
Description:
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
## 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).
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
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:
Annotation Details:
Use Cases:
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.
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
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
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
This dataset AFFECTNET YOLO Format is aimed to be used in facial expression detection for a YOLO project...
MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
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....
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## 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).
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
Niyuta/tinyperson-yolo-detection-format dataset hosted on Hugging Face and contributed by the HF Datasets community
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## 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).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
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.
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.
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.
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## 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).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
1734 images for training
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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:
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.
(x, y, width, height)
in YOLO format.0 = Normal
, 1 = Reversal
, and 2 = Corrected
.If you are using this synthetic dataset or the original Dyslexia Handwriting Dataset, please cite the following papers:
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
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.
The original RAR file was password-protected with the password: WanAsy321. This synthetic dataset, however, is provided openly for streamlined usage.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## 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).