Roboflow Annotate Hackitall

## Overview

Roboflow Annotate Hackitall is a dataset for object detection tasks - it contains Products annotations for 421 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).

The dataset consists of drone images that were obtained for agricultural field monitoring to detect weeds and crops through computer vision and machine learning approaches. The images were obtained through high-resolution UAVs and annotated using the LabelImg and Roboflow tool. Each image has a corresponding YOLO annotation file that contains bounding box information and class IDs for detected objects. The dataset includes:

Original images in .jpg format with a resolution of 585 × 438 pixels.

Annotation files (.txt) corresponding to each image, following the YOLO format: class_id x_center y_center width height.

A classes.txt file listing the object categories used in labeling (e.g., Weed, Crop).

The dataset is intended for use in machine learning model development, particularly for precision agriculture, weed detection, and plant health monitoring. It can be directly used for training YOLOv7 and other object detection models.

3D Mapping Annotation

## Overview

3D Mapping Annotation is a dataset for object detection tasks - it contains Ramps Steps annotations for 806 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).

Annotate

## Overview

Annotate is a dataset for object detection tasks - it contains Lemon annotations for 3,318 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

This dataset was created by karim fathy

Contents

Kaggle Annotation

## Overview

Kaggle Annotation is a dataset for object detection tasks - it contains Objects annotations for 965 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).

Vehicle Detection Dataset

This dataset is designed for vehicle detection tasks, featuring a comprehensive collection of images annotated for object detection. This dataset, originally sourced from Roboflow (https://universe.roboflow.com/object-detection-sn8ac/ai-traffic-system), was exported on May 29, 2025, at 4:59 PM GMT and is now publicly available on Kaggle under the CC BY 4.0 license.

Overview

• Purpose: The dataset supports the development of computer vision models for detecting various types of vehicles in traffic scenarios.
• Classes: The dataset includes annotations for 7 vehicle types:
  • Bicycle
  • Bus
  • Car
  • Motorbike
  • Rickshaw
  • Truck
  • Van
• Number of Images: The dataset contains 9,440 images, split into training, validation, and test sets:
  • Training: Images located in ../train/images
  • Validation: Images located in ../valid/images
  • Test: Images located in ../test/images
• Annotation Format: Images are annotated in YOLOv11 format, suitable for training state-of-the-art object detection models.
• Pre-processing: Each image has been resized to 640x640 pixels (stretched). No additional image augmentation techniques were applied.

Source and Creation

This dataset was created and exported via Roboflow, an end-to-end computer vision platform that facilitates collaboration, image collection, annotation, dataset creation, model training, and deployment. The dataset is part of the ai-traffic-system project (version 1) under the workspace object-detection-sn8ac. For more details, visit: https://universe.roboflow.com/object-detection-sn8ac/ai-traffic-system/dataset/1.

Usage

This dataset is ideal for researchers, data scientists, and developers working on vehicle detection and traffic monitoring systems. It can be used to: - Train and evaluate deep learning models for object detection, particularly using the YOLOv11 framework. - Develop AI-powered traffic management systems, autonomous driving applications, or urban mobility solutions. - Explore computer vision techniques for real-world traffic scenarios.

For advanced training notebooks compatible with this dataset, check out: https://github.com/roboflow/notebooks. To explore additional datasets and pre-trained models, visit: https://universe.roboflow.com.

License

The dataset is licensed under CC BY 4.0, allowing for flexible use, sharing, and adaptation, provided appropriate credit is given to the original source.

This dataset is a valuable resource for building robust vehicle detection models and advancing computer vision applications in traffic systems.

Traffic Annotate

## Overview

Traffic Annotate is a dataset for object detection tasks - it contains 20km annotations for 6,086 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).

Annotate Image

## Overview

Annotate Image is a dataset for object detection tasks - it contains Lump Or Lumps annotations for 289 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 images of traffic signs along with their corresponding bounding box annotations and class labels. The dataset has been preprocessed and visualized for traffic sign recognition tasks, and it was sourced from Roboflow. The dataset is well-suited for training deep learning models in object detection and classification tasks. It has been preprocessed to ensure uniform image sizes and normalized pixel values.

4,764 workers died on the job in 2020 (3.4 per 100,000 full-time equivalent workers). Workers in transportation and material moving occupations and construction and extraction occupations accounted for nearly half of all fatal occupational injuries (47.4 percent), representing 1,282 and 976 workplace deaths, respectively. Occupational Safety and Health Administration (US Department of Labour)

Introduction

There have been many accidents in construction sites due to lack of safety measures. A major reason for this has been workers not wearing Personal Protective Equipments (PPE) for their safety. Detecting PPEs become very crucial for the continuous monitoring of worker safety.

Dataset collection

This dataset is provided as a collection in Roboflow, please check this link: Construction Site Safety Image Dataset under the CC BY 4.0 License https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2163725%2F0e46d95b350ee8bc9c683595ccf5ecb6%2Fconstruction-safety.jpg?generation=1677172246224555&alt=media" alt=""> This dataset is a great collection of images, since the labels are in the following format: 'Hardhat', 'Mask', 'NO-Hardhat', 'NO-Mask', 'NO-Safety Vest', 'Person', 'Safety Cone', 'Safety Vest', 'machinery', 'vehicle'. It is very important in tracking and monitoring applications whether a person is wearing Hardhat or NO-Hardhat. Most of the datasets are not annotated in this particular way, making this dataset very useful.

Quick Summary

• Number of classes: 10
• Label Annotation: YOLO format (.txt)
• Metadata: metadata.csv and count.csv provides information about the dataset and train-val-test count information.
• PPE Class Map: {0: 'Hardhat', 1: 'Mask', 2: 'NO-Hardhat', 3: 'NO-Mask', 4: 'NO-Safety Vest', 5: 'Person', 6: 'Safety Cone', 7: 'Safety Vest', 8: 'machinery', 9: 'vehicle'}
• Difficulty: This is a beginner-friendly dataset on multi-class classification, object detection, and tracking. Annotations are in YoloV8 format. The splits are given in the dataset folder itself with metadata, so anyone can use this data to run models and produce results.

Citation

Please cite the project from Roboflow, if you use this dataset in a research paper. python @misc{ construction-site-safety_dataset, title = { Construction Site Safety Dataset }, type = { Open Source Dataset }, author = { Roboflow Universe Projects }, howpublished = { \url{ https://universe.roboflow.com/roboflow-universe-projects/construction-site-safety } }, url = { https://universe.roboflow.com/roboflow-universe-projects/construction-site-safety }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2023 }, month = { feb }, note = { visited on 2023-02-23 }, }

Introduction

This dataset consists of volleyball court images along with their key point annotations. The dataset has been annotated precisely to train a yolov8x-pose model to regress the key points on volleyball courts. The regressed key points will be used to carry out a homography and perspective transformation to produce a radar view of the court. This dataset is part of three datasets (the other two for volleyball players and referee object detection and volleyball ball object detection) used to train yolov8x models for my project.

Versions

This dataset has four versions, two of which have eight key points on the court (in RGB and grayscale) and the other two have four key points on the court. The version uploaded on my Kaggle has four distinct key points in grayscale. For other versions and formats of this dataset visit my Roboflow account. The best yolov8x-pose model (linked above) was trained on the version of this dataset uploaded on my Kaggle.

Images Source

The images used in this dataset have been extracted from a short 22-second clip of a volleyball match uploaded on YouTube.

Annotation Platform

The images were annotated on Roboflow Workspace.

Pre-Processing and Augmentations

The images were preprocessed to 640 pixels in width and height, and two versions of this dataset were subjected to grayscale.

The VegQual dataset contains 4,736 high-quality, annotated images of 14 commonly used vegetables, captured under real-world conditions. The images include variations in angles, backgrounds, distances, and lighting, providing a diverse and challenging resource for training and evaluating deep learning–based object detection models. Each image has been carefully annotated using bounding boxes in TXT (YOLO) format, incorporating both class IDs and normalized bounding box coordinates. These annotations enable precise object localization and are fully compatible with major deep learning frameworks. All annotations were created using the Roboflow platform, ensuring consistency, accuracy, and high-quality labeling standards.To achieve a better learning procedure, the dataset has been split into three sub-datasets: training, validation, and testing. The training dataset constitutes 70% of the entire dataset, with validation and testing at 20% and 10% respectively. In addition, all images undergo scaling to a standard of 640x640 pixels while being auto-oriented to rectify rotation discrepancies brought about by the EXIF metadata. The dataset is structured in three main folders - train, valid, and test, and each contains images/ and labels/ subfolders. Every image contains a label file containing class and bounding box data corresponding to each detected object. The whole dataset features 1,1407 labeled instances per 14 categories. The dataset provides a valuable benchmark for research in computer vision, deep learning, agricultural automation, and food quality assessment. It supports advancements in real-time classification and defect detection of vegetables, contributing to innovation in sustainable food production and intelligent agricultural systems.

Food To Annotate

## Overview

Food To Annotate is a dataset for object detection tasks - it contains Food WPtH annotations for 1,239 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).

Ksa 2

## Overview

Ksa 2 is a dataset for object detection tasks - it contains Characters annotations for 688 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).

Box Annotate

## Overview

Box Annotate is a dataset for object detection tasks - it contains Box Defects annotations for 432 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).

🌳 91 images of trees, annotated with bounding boxes in YOLOv8 format.

🪞 Mirrored from https://universe.roboflow.com/project-s402o/tree-counting-qiw3h/dataset/1.

Gate Annotate

## Overview

Gate Annotate is a dataset for object detection tasks - it contains Gate annotations for 243 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).

Give Machines the Power to See People.

This isn’t just a dataset — it’s a foundation for building the future of human-aware technology. Carefully crafted and annotated with precision, the People Detection dataset enables AI systems to recognize and understand human presence in dynamic, real-world environments.

Whether you’re building smart surveillance, autonomous vehicles, crowd analytics, or next-gen robotics, this dataset gives your model the eyes it needs.

What Makes This Dataset Different?

• Real-World Images – Diverse environments, realistic lighting, and real human motion
• High-Quality Annotations – Every person labeled with clean YOLO-format bounding boxes
• Plug-and-Play – Comes with pre-split training, validation, and test sets — no extra prep needed
• Speed-Optimized – Perfect for real-time object detection applications

Built for Visionaries

• Detect people instantly — in cities, offices, or crowds
• Build systems that respond to human presence
• Train intelligent agents to navigate human spaces safely and smartly

Created using Roboflow. Optimized for clarity, performance, and scale. Source Dataset on Roboflow →

This is more than a dataset. It’s a step toward a smarter world — One where machines can understand people.

Annotate Faces

## Overview

Annotate Faces is a dataset for object detection tasks - it contains Face annotations for 1,520 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).