Yolov8 Hand Training

## Overview

Yolov8 Hand Training is a dataset for object detection tasks - it contains 3 annotations for 1,705 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).

Computer Vision YOLOv8 Training

## Overview

Computer Vision YOLOv8 Training is a dataset for object detection tasks - it contains COTS Fish Coral annotations for 637 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).

Yolov8 Training

## Overview

Yolov8 Training is a dataset for object detection tasks - it contains Objects annotations for 637 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).

This extensive dataset is tailored for ship detection tasks utilizing the YOLOv8 object detection framework. It comprises over 80,000 high-resolution images containing various maritime scenes, captured under diverse environmental conditions and viewpoints. Each image is meticulously annotated with bounding boxes encompassing ships of different sizes, orientations, and contexts, ensuring comprehensive coverage of real-world scenarios.

The dataset is partitioned into sizable training and testing subsets, each exceeding 1 GB in size, to facilitate robust model training and evaluation. With its vast collection of annotated samples and compatibility with YOLOv8 architecture, this dataset serves as an invaluable resource for researchers, practitioners, and enthusiasts in the field of maritime object detection.

Deteksi Helm YOLOv8 Training

## Overview

Deteksi Helm YOLOv8 Training is a dataset for object detection tasks - it contains Objects annotations for 3,606 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).

DATASET SAMPLE

Duality.ai just released a 1000 image dataset used to train a YOLOv8 model in multiclass object detection -- and it's 100% free! Just create an EDU account here. This HuggingFace dataset is a 20 image and label sample, but you can get the rest at no cost by creating a FalconCloud account. Once you verify your email, the link will redirect you to the dataset page. What makes this dataset unique, useful, and capable of bridging the Sim2Real gap?

The digital twins are… See the full description on the dataset page: https://huggingface.co/datasets/duality-robotics/YOLOv8-Multiclass-Object-Detection-Dataset.

Great dataset to practice object detection algorithms. Data structured in YOLOv8 format with train, validation and test datasets along with labels. Also includes the yaml file. There is also a separate test video loaded so if you want to evaluate your model using the test video, you can also use that. Evaluation can be done using the test images as well as the test video. Data is from Roboflow website.

This dataset contains annotated images for object detection for containers and hands in a first-person view (egocentric view) during drinking activities. Both YOLOV8 format and COCO format are provided.Please refer to our paper for more details.Purpose: Training and testing the object detection model.Content: Videos from Session 1 of Subjects 1-20.Images: Extracted from the videos of Subjects 1-20 Session 1.Additional Images:~500 hand/container images from Roboflow Open Source data.~1500 null (background) images from VOC Dataset and MIT Indoor Scene Recognition Dataset:1000 indoor scenes from 'MIT Indoor Scene Recognition'400 other unrelated objects from VOC DatasetData Augmentation:Horizontal flipping±15% brightness change±10° rotationFormats Provided:COCO formatPyTorch YOLOV8 formatImage Size: 416x416 pixelsTotal Images: 16,834Training: 13,862Validation: 1,975Testing: 997Instance Numbers:Containers: Over 10,000Hands: Over 8,000

Cellphone Yolov8 Training

## Overview

Cellphone Yolov8 Training is a dataset for object detection tasks - it contains Cellphone annotations for 294 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).

This dataset is designed for training and evaluating object detection models, specifically for detecting plastic bottles and classifying them based on the presence or absence of a label. It is structured to work seamlessly with YOLOv8 and follows the standard YOLO format.

🔍 Classes: 0: Bottle with Label

1: Bottle without Label

📁 Folder Structure: images/: Contains all image files

labels/: Corresponding YOLO-format annotation files

data.yaml: Configuration file for training with YOLOv8

🛠 Use Case: This dataset is ideal for real-time detection systems, quality control applications, recycling automation, and projects focused on object classification in cluttered or real-world environments.

Explore the Player Detection and Tracking in Sports Videos Dataset, designed for training YOLOv8 models. Featuring diverse sports images and detailed annotations, this dataset supports robust development of player detection and tracking models, enhancing sports analytics and AI-driven analysis tools.

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.

Dataset

This dataset was created by ILVANREZKY

Released under Apache 2.0

Contents

Moon Detection Dataset for YOLOv8

This dataset was developed as part of the CubeRT-02 project, a CubeSat mission aimed at testing AI-powered vision systems in aerospace contexts. It consists of 7500+ annotated images for object detection of the Moon, optimized for use with the YOLOv8 architecture.

📸 Dataset Collection & Annotation

• Initial set: ~400 web-sourced images used for theoretical model exploration.
• Main dataset: 7500+ images captured over 6 months using smartphones and amateur camera devices, reflecting real-world scales, perspectives, and conditions.
• Cleaning: Manual filtering of blurry, low-quality, or irrelevant images.
• Annotation: Performed via Roboflow platform with bounding boxes for YOLOv8.
• Augmentation: Basic augmentations applied during preparation; none used during final training due to negative effects on performance.

📁 Dataset Structure

This dataset follows the YOLOv8 format.

A Python script and a YAML configuration file are included to help you easily train or test the dataset using Ultralytics' YOLOv8 implementation.

Authors

• Luis Adrian Cabrera | https://github.com/LuisAdrian5519
• Jesse Banda Chaidez | https://github.com/Jessebnda

Contributors

• Erick Blanco Nakashima
• José Alejandro Padilla Pérez
• Juan Pablo Riojas Pesqueira
• Guillermo Villegas
• Juan Adrian Astorga
• Julio Castañeda
• Juan Pablo Aboytes
• Maximo Millán Cabrera

Dataset Card for YOLOv8-TO_Data

This dataset contains the training and testing sets for the YOLOv8-TO paper.

  Dataset Description

Created by: Thomas Rochefort-Beaudoin License: MIT Datasets MMC Dataset Description: The MMC (Minimum Compliance) dataset is derived using the MMC method as the basis for the training dataset, where the segmentation labels are generated from black-and-white density projections obtained via a Heaviside projection. Split: 80% training, 10%… See the full description on the dataset page: https://huggingface.co/datasets/tomrb/yolov8to_data.

Yolov8 Weapon Train

## Overview

Yolov8 Weapon Train is a dataset for object detection tasks - it contains Gun annotations for 3,118 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).

South American Flags Dataset (YOLOv8 Format)

Created by Ishan Chauhan and Miilee Sharma

  Dataset Overview

This dataset contains labeled images of South American country flags intended for training object detection models using the YOLOv8 format. The annotations are structured for seamless integration with Ultralytics' YOLOv8 training pipeline.

  Contents

images/train/ – Training images
images/val/ – Validation images
images/test/ – Test images
labels/ –… See the full description on the dataset page: https://huggingface.co/datasets/7mgppp/south-american-flags.

Discover a structured dataset with 505 training, 112 validation, and 107 test annotated images, complete with a data.

The SmartBay Observatory in Galway Bay is an important contribution by Ireland to the growing global network of real-time data capture systems deployed within the ocean – technology giving us new insights into the ocean which we have not had before.

The observatory was installed on the seafloor 1.5km off the coast of Spiddal, County Galway, Ireland . The observatory uses cameras, probes and sensors to permit continuous and remote live underwater monitoring. This observatory equipment allows ocean researchers unique real-time access to monitor ongoing changes in the marine environment. Data relating to the marine environment at the site is transferred in real-time from the SmartBay Observatory through a fibre optic telecommunications cable to the Marine Institute headquarters and onwards onto the internet. The data includes a live video stream, the depth of the observatory node, the sea temperature and salinity, and estimates of the chlorophyll and turbidity levels in the water which give an indication of the volume of phytoplankton and other particles, such as sediment, in the water.

The Smartbay Marine Species Object Detection training Dataset is an initial Bounding Box Annotated image dataset used in attempting to Train a YOLOv8 Object Detection Model to classify the Marine Fauna observed in the Smartbay Observatory Video footage using species names.

The imagery used in this training dataset consists of image frame captures from the Smartbay video Archive files, CC-BY imagery from the www.minka-sdg.org website and images taken by Eva Cullen in the "Galway Atlantaquaria" Aquarium in Galway, Ireland.

The imagery were annotated using CVAT, collated on Roboflow and exported in YOLOv8 training dataset format.

This dataset contains images with annotations in yolo format. This dataset can be used for training yolo models (yolov8 also). This is created for learning purpose.

Classes: 0 - player 1 - football