Object Detection Data Labeling

## Overview

Object Detection Data Labeling is a dataset for object detection tasks - it contains Objects annotations for 285 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).

1. Overview We provide various types of Annotated Imagery Data annotation services, including:
2. Bounding box
3. Polygon
4. Segmentation
5. Polyline
6. Key points
7. Image classification
8. Image description ...
9. Our Capacity
10. Platform: Our platform supports human-machine interaction and semi-automatic labeling, increasing labeling efficiency by more than 30% per annotator.It has successfully been applied to nearly 5,000 projects.

• Annotation Tools: Nexdata's platform integrates 30 sets of annotation templates, covering audio, image, video, point cloud and text.

-Secure Implementation: NDA is signed to gurantee secure implementation and Annotated Imagery Data is destroyed upon delivery.

-Quality: Multiple rounds of quality inspections ensures high quality data output, certified with ISO9001

1. About Nexdata Nexdata has global data processing centers and more than 20,000 professional annotators, supporting on-demand data annotation services, such as speech, image, video, point cloud and Natural Language Processing (NLP) Data, etc. Please visit us at https://www.nexdata.ai/computerVisionTraining?source=Datarade

Shanghai Tech Object Labelling

## Overview

Shanghai Tech Object Labelling is a dataset for object detection tasks - it contains Humans annotations for 300 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).

Object Detection Auto Label

## Overview

Object Detection  Auto Label is a dataset for object detection tasks - it contains Object annotations for 478 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).

Nexdata provides high-quality Annotated Imagery Data annotation for bounding box, polygon,segmentation,polyline, key points,image classification and image description. We have handled tons of data for autonomous driving, internet entertainment, retail, surveillance and security and etc.

Dataset

This dataset was created by Shivani Rana

Contents

Product Label

## Overview

Product Label is a dataset for object detection tasks - it contains Products LjCv annotations for 211 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).

Object Labeling

## Overview

Object Labeling is a dataset for object detection tasks - it contains Objectdetect annotations for 1,152 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.

The image annotation software market is booming, projected to reach $10 billion by 2033 with a 25% CAGR. Learn about key drivers, trends, and leading companies shaping this rapidly evolving sector fueled by AI and machine learning advancements. Discover market size, segmentation, and regional analysis in this comprehensive report.

PLEASE UPVOTE IF YOU FOUND THIS DATASET USEFUL

The Open Images Dataset is a vast collection of annotated images designed for computer vision research. It contains millions of images labeled with thousands of object categories, bounding boxes, and relationship annotations, making it a valuable resource for training and evaluating machine learning models in object detection, image segmentation, and scene understanding.

Provenance:
- Source: The dataset was initially released by Google Research and is now maintained for public access.
- Methodology: Images were sourced from various locations across the web and annotated using a combination of machine learning models and human verification. The dataset follows a structured labeling pipeline to ensure high-quality annotations.

For more information and dataset access, visit: https://storage.googleapis.com/openimages/web/index.html.

The dataset was created for bee object detection based on images. Videos were taken at the entrance of 25 beehives in three apiaries in San Jose, Cupertino, and Gilroy in CA, USA. The videos were taken above the landing pad of different beehives. The camera was placed at a distinct angle to provide a clear view of the hive entrance.

The images were saved one frame per second from videos. The annotation platform Label Studio was selected to annotate bees in each image due to the friendly user interface and high quality. The below criteria was followed in the labeling process. First, at least 50% of the bee's body must be visible. Second, the image cannot be too blurry. After tagging each bee with a rectangle box in the annotation tool, output label files with Yolo labeling format were generated for each image. The output label files contained one set of bounding-box (BBox) coordinates for each bee in the image. If there were multiple objects in the image, there would be one line for one object in the label file. It recorded the object ID, X-axis center, Y-axis center, BBox width, and height with normalized image size from 0 to 1.

Please cite the paper if you used the data in your research: Liang, A. (2024). Developing a multimodal system for bee object detection and health assessment. IEEE Access, 12, 158703 - 15871. https://doi.org/10.1109/ACCESS.2024.3464559.

The development of independent sitting is associated with language development, but the learning experiences underlying this relationship are not well understood. Additionally, it is unknown how these processes play out in infants with motor impairments and delays in sitting development. We examined the real-time associations between sitting and caregiver speech input in 28 5–7-month-old infants with typical development and 22 7–16-month-old infants with cerebral palsy who were at a similar stage of early sitting development. We hypothesized that object labels would be more likely to co-occur with moments of optimal attention to the labeled object while sitting than while in other positions. Infants were video recorded in five minutes of free play with a caregiver. Coders transcribed caregivers’ speech, identified instances of object labeling, and coded infants’ and caregivers’ attentional states during object labeling episodes. We found that caregivers labeled more objects while infants were sitting than while they were in other positions. However, object labels were not more likely to co-occur with infant attention, infant multimodal attention, or coordinated visual attention to the labeled object during sitting. Infants with cerebral palsy were exposed to fewer labels and were less likely to be attending to objects as they were labeled than infants with typical development. Our findings shed light on a possible pathway connecting sitting and language in typical and atypical development.

Face Detection - Object Detection & Face Recognition Dataset

The dataset is created on the basis of Selfies and ID Dataset

The dataset is a collection of images (selfies) of people and bounding box labeling for their faces. It has been specifically curated for face detection and face recognition tasks. The dataset encompasses diverse demographics, age, ethnicities, and genders.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F01348572e2ae2836f10bc2f2da381009%2FFrame%2050%20(1).png?generation=1699439342545305&alt=media" alt="">

The dataset is a valuable resource for researchers, developers, and organizations working on age prediction and face recognition to train, evaluate, and fine-tune AI models for real-world applications. It can be applied in various domains like psychology, market research, and personalized advertising.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 95,000+ human images & videos - Full dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• true_gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_1_extension, photo_2_extension, …, photo_15_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, …, photo_15_resolution - photo resolution in the dataset

OTHER BIOMETRIC DATASETS:

• Anti Spoofing Real Dataset
• Antispoofing Replay Dataset
• Selfies, ID Images dataset (5591 sets of 15 files)
• Selfies and video dataset (4 052 sets)
• Dataset of bald people, 5000 images

🧩 This is just an example of the data. Leave a request here to learn more

Dataset structure

• images - contains of original images of people
• labels - includes visualized labeling for the original images
• annotations.xml - contains coordinates of the bbox, created for the original photo

Data Format

Each image from images folder is accompanied by an XML-annotation in the annotations.xml file indicating the coordinates of the polygons and labels . For each point, the x and y coordinates are provided.

Example of XML file structure

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F19e61b2d0780e9db80afe4a0ce879c4b%2Fcarbon.png?generation=1699440100527867&alt=media" alt="">

🚀 You can learn more about our high-quality unique datasets here

keywords: biometric system, biometric system attacks, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, object detection dataset, deep learning datasets, computer vision datset, human images dataset, human faces dataset

The global market for Image Tagging & Annotation Services is poised for significant expansion, projected to reach a market size of approximately $5,500 million in 2025. This growth is fueled by an impressive Compound Annual Growth Rate (CAGR) of 22% during the forecast period of 2025-2033. The burgeoning demand for AI and machine learning applications across various sectors is the primary catalyst, driving the need for meticulously tagged and annotated datasets to train these sophisticated models. Industries such as Automotive, particularly with the rise of autonomous driving and advanced driver-assistance systems (ADAS), are heavily investing in image annotation for object recognition and scene understanding. Similarly, Retail & Commerce leverages these services for personalized customer experiences, inventory management, and visual search functionalities. The Government & Security sector utilizes image annotation for surveillance, threat detection, and forensic analysis, while Healthcare benefits from its application in medical imaging analysis, diagnosis, and drug discovery. Further bolstering this growth are key trends like the increasing adoption of cloud-based annotation platforms, which offer scalability and enhanced collaboration, and the growing sophistication of annotation tools, including AI-assisted annotation that streamlines the process and improves accuracy. The demand for diverse annotation types, such as image classification, object recognition, and boundary recognition, is expanding as AI models become more complex and capable. While the market is robust, potential restraints include the high cost of skilled annotation labor and the need for stringent data privacy and security measures, especially in sensitive sectors like healthcare and government. However, the inherent value derived from accurate and comprehensive data annotation in driving AI innovation and operational efficiency across a multitude of industries ensures a dynamic and upward trajectory for this market. Here's a unique report description for Image Tagging & Annotation Services, incorporating your specific requirements:

This report offers an in-depth analysis of the global Image Tagging & Annotation Services market, a critical component for the advancement of Artificial Intelligence and Machine Learning. Valued at over $500 million in the base year of 2025, the market is projected to witness robust growth, reaching an estimated $2.5 billion by 2033. The study encompasses the historical period from 2019-2024, the base year of 2025, and a comprehensive forecast period spanning from 2025-2033, providing a dynamic outlook on market evolution.

Content

The dataset contains labeled images of transport vehicles and number plates using LabelImg in YOLOv5 format.

I first collected some 1000 training images of traffic, vehicles and number plates, and CCTV footage videos. Then I extracted frames from videos using OpenCV. Drew a box around each object that we want the detector to see and label each box with the object class that we would like the detector to predict.

There are many labeling tools available online, the one used by us was LabelImg. It is a free, open-source tool for graphically labeling images. It’s written in Python and uses QT for its graphical interface.

The images were labeled under 7 classes – Car, Number Plate, Blur Number Plate, Two Wheeler, Auto, Bus, and Truck in YOLOv5 format

Inspiration

Use the given dataset in classification problems Use CNN and YOLOv5 model to detect the objects labeled in the given dataset

Fruit Object Detection is a dataset for an object detection task. Possible applications of the dataset could be in the food industry. The dataset consists of 4474 images with 22576 labeled objects belonging to 11 different classes including pear, apple, grape, and other: pineapple, durian, korean melon, watermelon, tangerine, lemon, cantaloupe, and dragon fruit

SecondSight Data Labeling

## Overview

SecondSight Data Labeling is a dataset for object detection tasks - it contains Objects annotations for 388 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).

Plastic Labelling

## Overview

Plastic Labelling is a dataset for object detection tasks - it contains Plastic 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).

Example dataset described in ICMLA2019 Paper 'Lean Training Data Generation for Planar Object Detection Models in Unsteady Logistics Contexts' (Dörr, Brandt, Meyer, Pouls).