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.

Soup Can Object Detection Dataset Sample

Duality.ai just released a 1000 image dataset used to train a YOLOv8 model for 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.

  Dataset Overview

This dataset consists of high-quality images of soup cans… See the full description on the dataset page: https://huggingface.co/datasets/duality-robotics/YOLOv8-Object-Detection-02-Dataset.

Wood Train

## Overview

Wood Train is a dataset for instance segmentation tasks - it contains Fiber And Vessel annotations for 1,026 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).

WD

## Overview

WD is a dataset for object detection tasks - it contains Weapons annotations for 2,451 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 prepared annotated and prepared to be used with yolov8, the dataset consists of three classes train, person, and line (yellow safety line)

Train : 25 images Test: 173 images Validation: 90 images

Multi Instance Object Detection Dataset Sample

Duality.ai just released a 1000 image dataset used to train a YOLOv8 model for 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.

  Dataset Overview

This dataset consists of high-quality images of soup… See the full description on the dataset page: https://huggingface.co/datasets/duality-robotics/YOLOv8-Multi-Instance-Object-Detection-Dataset.

Train Data 10000_2

## Overview

Train Data 10000_2 is a dataset for instance segmentation tasks - it contains 1 annotations for 3,753 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).

Cricket Ball Detection for YOLOv8: Train Like a Pro! 🏏

Sharpen your Cricket AI: Unleash the power of YOLOv8 for precise cricket ball detection in images and videos with this comprehensive dataset.

Fuel Your Custom Training: Build a robust cricket ball detection model tailored to your specific needs. This dataset, featuring 1778 meticulously annotated images in YOLOv8 format, serves as the perfect launchpad.

Dive into Diversity:

In-Action Balls: Train your model to identify cricket balls in motion, capturing deliveries, fielding plays, and various gameplay scenarios.

Lighting Variations: Adapt to diverse lighting conditions (day, night, indoor) with a range of images showcasing balls under different illumination.

Background Complexity: Prepare your model for real-world environments. The dataset includes images featuring stadiums, practice nets, and various background clutter.

Ball States: Train effectively with images of new and used cricket balls, encompassing varying degrees of wear and tear.

Unlock Potential Applications:

Real-time Cricket Analysis: Power applications for in-depth player analysis, ball trajectory tracking, and automated umpiring systems.

Enhanced Broadcasting Experiences: Integrate seamless ball tracking, on-screen overlays, and real-time highlights into cricket broadcasts.

Automated Summarization: Streamline cricket video processing for automated highlight reels, focusing on key ball-related moments.

Who Should Use This Dataset:

• Computer vision researchers and developers seeking to leverage YOLOv8 for object detection in sports applications.
• Cricket enthusiasts and data scientists passionate about building AI-powered cricket analytics tools.
• Anyone venturing into custom object detection models for cricket analysis or sports technology projects.

Train 200

## Overview

Train 200 is a dataset for instance segmentation tasks - it contains Weed FKzS annotations for 200 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).

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 includes 640×640 front-seat photos of myself wearing a seatbelt. It was created to train and test YOLOv8 object detection models.

Sar Seg Train

## Overview

Sar Seg Train is a dataset for instance segmentation tasks - it contains Sarimage annotations for 507 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).

Scripts for generating domain-randomised synthetic images with Omniverse Replicator and for training/evaluating a YOLOv8 detector in robotic manipulation tasks, reproducing all experiments reported in the paper.

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 Types 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 broad "Marine Type" classes.

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 trainign dataset format.

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.

Train Dataset Part1

## Overview

Train Dataset Part1 is a dataset for object detection tasks - it contains Happy Sad Neutral Confused annotations for 376 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).

Supplementary Data Protocol

This supplementary dataset includes all files necessary to reproduce and evaluate the training and validation of YOLOv8 and CNN models for detecting GUS-stained and haustoria-containing cells with the BluVision Haustoria software.

1. gus_training_set_yolo/
- Contains the complete YOLOv8-compatible training dataset for GUS classification.
- Format: PyTorch YOLOv5/8 structure from Roboflow export.
- Subfolders:
- train/, test/, val/: Image sets and corresponding label files.
- data.yaml: Configuration file specifying dataset structure and classes.

2. haustoria_training_set_yolo/
- Contains the complete YOLOv8-compatible training dataset for haustoria detection.
- Format identical to gus_training_set_yolo/.

3. haustoria_training_set_cnn/
- Dataset formatted for CNN-based classification.
- Structure:
- gus/: Images of cells without haustoria.
- hau/: Images of cells with haustoria.
- Suitable for binary classification pipelines (e.g., Keras, PyTorch).

4. yolo_models/
- Directory containing the final trained YOLOv8 model weights.
- Includes:
- gus.pt: YOLOv8 model trained on GUS data.
- haustoria.pt: YOLOv8 model trained on haustoria data.

The dataset comprised two main components, totaling 1113 images and 5628 orange samples (He, 2024). The primary component consisted of 338 images depicting orange from the 'Chunjian' variety. These images were captured under varying conditions of light, distance, occlusion, number of targets, and maturity in Ziyan Township, Nanchong City, Sichuan Province, China. This variety is extensively cultivated in southern regions of China and represents one of the primary cultivars of citrus in the country. The image was captured using a Canon EOS 90D camera with an 18-55mm focal length lens, under natural outdoor lighting conditions. The camera was set to automatic exposure mode, and the processed image was exported with a resolution of 2992×2992 pixels. The secondary component of the dataset comprised 775 images sourced from publicly available orange picture on the internet, totaling 1216 samples. These additional images aim to enhance the model's generalization performance to other orange varieties. The images have been partitioned into training set (70%, 781 images used for model training), validation set (20%, 221 images used for hyperparameter tuning and performance evaluation during training), and test set (10%, 111 images used for assessing the model's generalization ability, accuracy, and reliability).

Dataset

This dataset was created by Arun Nithyaanandam

Contents

Explore our dataset of 621 solar panel images with detailed bounding box annotations in YOLO format, perfect for training YOLOv8 models.