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).

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).

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.

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.

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).

Train Dataset Complete

## Overview

Train Dataset Complete is a dataset for instance segmentation tasks - it contains Damage 6VC0 annotations for 1,077 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 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

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.

The dataset is in YOLOv8 format. The dataset is divided into train, validation and test. Data replication processes were also applied. Download Dataset.

YOLOv8 Image segmentation dataset: PELLET Casimir Marius

This dataset includes 100 images from the PELLET Casimir Marius story on Europeana. It is available in YOLOv8 format, to train a model to segment text lines and illustrations from page images. The ground truth was generated using Teklia's open-source annotation interface Callico. This work is marked with CC0 1.0. To view a copy of this license, visit https://creativecommons.org/publicdomain/zero/1.0/.

• Original dataset: https://github.com/ayoolaolafenwa/PixelLib/releases/tag/1.0.0
• This dataset contains annotations in YOLO format.
• One can train different segmentation models on this dataset.
• Some of the segmentation models are: YOLOv8, Pointrend, MaskRCNN

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.

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.

An MDCFVit-YOLO model based on the YOLOv8 algorithm is proposed to address issues in nighttime infrared object detection such as low visibility, high interference, and low precision in detecting small objects. The backbone network uses the lightweight Repvit model, improving detection performance and reducing model weight through transfer learning. The proposed MPA module integrates multi-scale contextual information, capturing complex dependencies between spatial and channel dimensions, thereby enhancing the representation capability of the neural network. The CSM module dynamically adjusts the weights of feature maps, enhancing the model of sensitivity to small targets. The dynamic automated detection head DAIH improves the accuracy of infrared target detection by dynamically adjusting regression feature maps. Additionally, three innovative loss functions—focalerDIoU, focalerGIOU and focalerShapeIoU are proposed to reduce losses during the training process. Experimental results show that the detection accuracy of 78% for small infrared nighttime targets, with a recall rate of 58.6%, an mAP value of 67%. and a parameter count of 20.9M for the MDCFVit-YOLO model. Compared to the baseline model YOLOv8, the mAP increased by 6.4%, with accuracy and recall rates improved by 4.5% and 5.7%, respectively. This research provides new ideas and methods for infrared target detection, enhancing the detection accuracy and real-time performance.

📄 Description

Overview This dataset contains annotated images of 7 types of kitchen utensils — fork, butter knife, kitchen knife, peeler, spoon, tongs, and wooden spoon — organized into train/ and val/ sets. Each split includes subfolders images/ (JPEG/PNG files) and labels/ (YOLO-format .txt files), along with a classes.txt listing the class names mapped to indices 0–6.

Dataset Contents

• train/images/ & val/images/: Raw utensil photos
• train/labels/ & val/labels/: YOLO-format .txt annotations (one line per object: class_id x_center y_center width height, all normalized)

• classes.txt:

  fork
  butter knife
  kitchen knife
  peeler
  spoon
  tongs
  wooden spoon
  

Use Cases

• Train or fine-tune object detection models (e.g., YOLOv8, YOLOv5) on kitchen utensil recognition
• Benchmark multi‑class detection performance in indoor/home environments
• Serve as a starting point for kitchen inventory automation, robotics, and smart cooking applications

Structure and Labeling Standards

• 2, XXX images total — pre-split into train/validation
• Each image’s annotation file shares its base name and contains bounding boxes in YOLO format
• Class indices align with entries in classes.txt, ensuring compatibility with common detection frameworks

Getting Started

1. Clone or download this dataset

2. Reference the folder paths in your data.yaml:

   train: train/images
   val:  val/images
   nc: 7
   names:
    0: fork
    1: butter knife
    2: kitchen knife
    3: peeler
    4: spoon
    5: tongs
    6: wooden spoon
   

3. Train a YOLOv8 model:

   model.train(data='data.yaml', epochs=50, imgsz=640)
   

Recommended Citation / Acknowledgment If you publish research using this dataset, please mention:

“Kitchen utensil detection dataset uploaded via Kaggle by Raunak gola.”

Future Extensions

• Expand with more utensil types or larger image sets
• Support segmentation annotations
• Add real-world kitchen scene backgrounds or occluded images

Train E

## Overview

Train E is a dataset for instance segmentation tasks - it contains Yirtik YXfG annotations for 531 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).

An MDCFVit-YOLO model based on the YOLOv8 algorithm is proposed to address issues in nighttime infrared object detection such as low visibility, high interference, and low precision in detecting small objects. The backbone network uses the lightweight Repvit model, improving detection performance and reducing model weight through transfer learning. The proposed MPA module integrates multi-scale contextual information, capturing complex dependencies between spatial and channel dimensions, thereby enhancing the representation capability of the neural network. The CSM module dynamically adjusts the weights of feature maps, enhancing the model of sensitivity to small targets. The dynamic automated detection head DAIH improves the accuracy of infrared target detection by dynamically adjusting regression feature maps. Additionally, three innovative loss functions—focalerDIoU, focalerGIOU and focalerShapeIoU are proposed to reduce losses during the training process. Experimental results show that the detection accuracy of 78% for small infrared nighttime targets, with a recall rate of 58.6%, an mAP value of 67%. and a parameter count of 20.9M for the MDCFVit-YOLO model. Compared to the baseline model YOLOv8, the mAP increased by 6.4%, with accuracy and recall rates improved by 4.5% and 5.7%, respectively. This research provides new ideas and methods for infrared target detection, enhancing the detection accuracy and real-time performance.

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 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.