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

Leaves from genetically unique Juglans regia plants were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA). Soil samples were collected in Fall of 2017 from the riparian oak forest located at the Russell Ranch Sustainable Agricultural Institute at the University of California Davis. The soil was sieved through a 2 mm mesh and was air dried before imaging. A single soil aggregate was scanned at 23 keV using the 10x objective lens with a pixel resolution of 650 nanometers on beamline 8.3.2 at the ALS. Additionally, a drought stressed almond flower bud (Prunus dulcis) from a plant housed at the University of California, Davis, was scanned using a 4x lens with a pixel resolution of 1.72 µm on beamline 8.3.2 at the ALS Raw tomographic image data was reconstructed using TomoPy. Reconstructions were converted to 8-bit tif or png format using ImageJ or the PIL package in Python before further processing. Images were annotated using Intel’s Computer Vision Annotation Tool (CVAT) and ImageJ. Both CVAT and ImageJ are free to use and open source. Leaf images were annotated in following Théroux-Rancourt et al. (2020). Specifically, Hand labeling was done directly in ImageJ by drawing around each tissue; with 5 images annotated per leaf. Care was taken to cover a range of anatomical variation to help improve the generalizability of the models to other leaves. All slices were labeled by Dr. Mina Momayyezi and Fiona Duong.To annotate the flower bud and soil aggregate, images were imported into CVAT. The exterior border of the bud (i.e. bud scales) and flower were annotated in CVAT and exported as masks. Similarly, the exterior of the soil aggregate and particulate organic matter identified by eye were annotated in CVAT and exported as masks. To annotate air spaces in both the bud and soil aggregate, images were imported into ImageJ. A gaussian blur was applied to the image to decrease noise and then the air space was segmented using thresholding. After applying the threshold, the selected air space region was converted to a binary image with white representing the air space and black representing everything else. This binary image was overlaid upon the original image and the air space within the flower bud and aggregate was selected using the “free hand” tool. Air space outside of the region of interest for both image sets was eliminated. The quality of the air space annotation was then visually inspected for accuracy against the underlying original image; incomplete annotations were corrected using the brush or pencil tool to paint missing air space white and incorrectly identified air space black. Once the annotation was satisfactorily corrected, the binary image of the air space was saved. Finally, the annotations of the bud and flower or aggregate and organic matter were opened in ImageJ and the associated air space mask was overlaid on top of them forming a three-layer mask suitable for training the fully convolutional network. All labeling of the soil aggregate and soil aggregate images was done by Dr. Devin Rippner. These images and annotations are for training deep learning models to identify different constituents in leaves, almond buds, and soil aggregates Limitations: For the walnut leaves, some tissues (stomata, etc.) are not labeled and only represent a small portion of a full leaf. Similarly, both the almond bud and the aggregate represent just one single sample of each. The bud tissues are only divided up into buds scales, flower, and air space. Many other tissues remain unlabeled. For the soil aggregate annotated labels are done by eye with no actual chemical information. Therefore particulate organic matter identification may be incorrect. Resources in this dataset:Resource Title: Annotated X-ray CT images and masks of a Forest Soil Aggregate. File Name: forest_soil_images_masks_for_testing_training.zipResource Description: This aggregate was collected from the riparian oak forest at the Russell Ranch Sustainable Agricultural Facility. The aggreagate was scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 0,0,0; pores spaces have a value of 250,250, 250; mineral solids have a value= 128,0,0; and particulate organic matter has a value of = 000,128,000. These files were used for training a model to segment the forest soil aggregate and for testing the accuracy, precision, recall, and f1 score of the model.Resource Title: Annotated X-ray CT images and masks of an Almond bud (P. Dulcis). File Name: Almond_bud_tube_D_P6_training_testing_images_and_masks.zipResource Description: Drought stressed almond flower bud (Prunis dulcis) from a plant housed at the University of California, Davis, was scanned by X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 4x lens with a pixel resolution of 1.72 µm using. For masks, the background has a value of 0,0,0; air spaces have a value of 255,255, 255; bud scales have a value= 128,0,0; and flower tissues have a value of = 000,128,000. These files were used for training a model to segment the almond bud and for testing the accuracy, precision, recall, and f1 score of the model.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads Resource Title: Annotated X-ray CT images and masks of Walnut leaves (J. Regia) . File Name: 6_leaf_training_testing_images_and_masks_for_paper.zipResource Description: Stems were collected from genetically unique J. regia accessions at the 117 USDA-ARS-NCGR in Wolfskill Experimental Orchard, Winters, California USA to use as scion, and were grafted by Sierra Gold Nursery onto a commonly used commercial rootstock, RX1 (J. microcarpa × J. regia). We used a common rootstock to eliminate any own-root effects and to simulate conditions for a commercial walnut orchard setting, where rootstocks are commonly used. The grafted saplings were repotted and transferred to the Armstrong lathe house facility at the University of California, Davis in June 2019, and kept under natural light and temperature. Leaves from each accession and treatment were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 170,170,170; Epidermis value= 85,85,85; Mesophyll value= 0,0,0; Bundle Sheath Extension value= 152,152,152; Vein value= 220,220,220; Air value = 255,255,255.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads

Here are a few use cases for this project:

1. Historical Weapon Classification: This computer vision model can be utilized by historians, archeologists, and museum curators to classify and catalog historical weapons and artifacts, including swords, arrows, guns, and knives, enabling them to better understand and contextualize the weapons' origins and usage throughout history.

2. Video Game Asset Management: Game developers can use the Data Annotate model to automatically tag and categorize in-game assets, such as weapons and visual effects, to streamline their development process and more easily manage game content.

3. Prop and Costume Design: The model can aid prop and costume designers in the film, theater, and cosplay industries by identifying and categorizing various weapons and related items, allowing them to find suitable props or inspirations for their designs more quickly.

4. Law Enforcement and Security: Data Annotate can be used by law enforcement agencies and security personnel to effectively detect weapons in surveillance footage or images, enabling them to respond more quickly to potential threats and uphold public safety.

5. Educational Applications: Teachers and educators can use the model to develop interactive and engaging learning materials in the fields of history, art, and technology. It can help students identify and understand the significance of various weapons and their roles in shaping human history and culture.

Car-Highway Data Annotation Project

Introduction

In this project, we aim to annotate car images captured on highways. The annotated data will be used to train machine learning models for various computer vision tasks, such as object detection and classification.

Project Goals

• Collect a diverse dataset of car images from highway scenes.
• Annotate the dataset to identify and label cars within each image.
• Organize and format the annotated data for machine learning model training.

Tools and Technologies

For this project, we will be using Roboflow, a powerful platform for data annotation and preprocessing. Roboflow simplifies the annotation process and provides tools for data augmentation and transformation.

Annotation Process

1. Upload the raw car images to the Roboflow platform.
2. Use the annotation tools in Roboflow to draw bounding boxes around each car in the images.
3. Label each bounding box with the corresponding class (e.g., car).
4. Review and validate the annotations for accuracy.

Data Augmentation

Roboflow offers data augmentation capabilities, such as rotation, flipping, and resizing. These augmentations can help improve the model's robustness.

Data Export

Once the data is annotated and augmented, Roboflow allows us to export the dataset in various formats suitable for training machine learning models, such as YOLO, COCO, or TensorFlow Record.

Milestones

1. Data Collection and Preprocessing
2. Annotation of Car Images
3. Data Augmentation
4. Data Export
5. Model Training

Conclusion

By completing this project, we will have a well-annotated dataset ready for training machine learning models. This dataset can be used for a wide range of applications in computer vision, including car detection and tracking on highways.

Discover the booming Data Annotation & Labeling Tool market! Explore a comprehensive analysis revealing a $2B market in 2025, projected to reach $10B by 2033, driven by AI and ML adoption. Learn about key trends, regional insights, and leading companies shaping this rapidly evolving landscape.

Impact assessment is an evolving area of research that aims at measuring and predicting the potential effects of projects or programs. Measuring the impact of scientific research is a vibrant subdomain, closely intertwined with impact assessment. A recurring obstacle pertains to the absence of an efficient framework which can facilitate the analysis of lengthy reports and text labeling. To address this issue, we propose a framework for automatically assessing the impact of scientific research projects by identifying pertinent sections in project reports that indicate the potential impacts. We leverage a mixed-method approach, combining manual annotations with supervised machine learning, to extract these passages from project reports. This is a repository to save datasets and codes related to this project. Please read and cite the following paper if you would like to use the data: Becker M., Han K., Werthmann A., Rezapour R., Lee H., Diesner J., and Witt A. (2024). Detecting Impact Relevant Sections in Scientific Research. The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING). This folder contains the following files: evaluation_20220927.ods: Annotated German passages (Artificial Intelligence, Linguistics, and Music) - training data annotated_data.big_set.corrected.txt: Annotated German passages (Mobility) - training data incl_translation_all.csv: Annotated English passages (Artificial Intelligence, Linguistics, and Music) - training data incl_translation_mobility.csv: Annotated German passages (Mobility) - training data ttparagraph_addmob.txt: German corpus (unannotated passages) model_result_extraction.csv: Extracted impact-relevant passages from the German corpus based on the model we trained rf_model.joblib: The random forest model we trained to extract impact-relevant passages Data processing codes can be found at: https://github.com/khan1792/texttransfer

The Data Annotation and Labeling Tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in the burgeoning fields of artificial intelligence (AI) and machine learning (ML). The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $10 billion by 2033. This expansion is fueled by several key factors. The automotive industry leverages data annotation for autonomous driving systems development, while healthcare utilizes it for medical image analysis and diagnostics. Financial services increasingly adopt these tools for fraud detection and risk management, and retail benefits from enhanced product recommendations and customer experience personalization. The prevalence of both supervised and unsupervised learning techniques necessitates diverse data annotation solutions, fostering market segmentation across manual, semi-supervised, and automatic tools. Market restraints include the high cost of data annotation and the need for skilled professionals to manage the annotation process effectively. However, the ongoing advancements in automation and the decreasing cost of computing power are mitigating these challenges. The North American market currently holds a significant share, with strong growth also expected from Asia-Pacific regions driven by increasing AI adoption. Competition in the market is intense, with established players like Labelbox and Scale AI competing with emerging companies such as SuperAnnotate and Annotate.io. These companies offer a range of solutions catering to varying needs and budgets. The market's future growth hinges on continued technological innovation, including the development of more efficient and accurate annotation tools, integration with existing AI/ML platforms, and expansion into new industry verticals. The increasing adoption of edge AI and the growth of data-centric AI further enhance the market potential. Furthermore, the growing need for data privacy and security is likely to drive demand for tools that prioritize data protection, posing both a challenge and an opportunity for providers to offer specialized solutions. The market's success will depend on the ability of vendors to adapt to evolving needs and provide scalable, cost-effective, and reliable annotation solutions.

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.

Dataset Card for fgan-annotate-dataset

This dataset has been created with Argilla. As shown in the sections below, this dataset can be loaded into Argilla as explained in Load with Argilla, or used directly with the datasets library in Load with datasets.

  Dataset Summary

This dataset contains:

A dataset configuration file conforming to the Argilla dataset format named argilla.yaml. This configuration file will be used to configure the dataset when using the… See the full description on the dataset page: https://huggingface.co/datasets/aaronemmanuel/fgan-annotate-dataset.

Data Annotate

## Overview

Data Annotate is a dataset for object detection tasks - it contains Grocery annotations for 1,261 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 Video Data annotation services, including:
2. Video classification
3. Timestamps
4. Video tracking
5. Video detection ...
6. Our Capacity
7. 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/datasets/computervision?source=Datarade

The automated data annotation tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in artificial intelligence (AI) and machine learning (ML) applications. The market, valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033. This significant expansion is fueled by several key factors. The proliferation of AI-powered applications across various industries, including healthcare, automotive, and finance, necessitates vast amounts of accurately annotated data. Furthermore, the ongoing advancements in deep learning algorithms and the emergence of sophisticated annotation tools are streamlining the data annotation process, making it more efficient and cost-effective. The market is segmented by tool type (text, image, and others) and application (commercial and personal use), with the commercial segment currently dominating due to the substantial investment by enterprises in AI initiatives. Geographic distribution shows a strong concentration in North America and Europe, reflecting the high adoption rate of AI technologies in these regions; however, Asia-Pacific is expected to show significant growth in the coming years due to increasing technological advancements and investments in AI development. The competitive landscape is characterized by a mix of established technology giants and specialized data annotation providers. Companies like Amazon Web Services, Google, and IBM offer integrated annotation solutions within their broader cloud platforms, competing with smaller, more agile companies focusing on niche applications or specific annotation types. The market is witnessing a trend toward automation within the annotation process itself, with AI-assisted tools increasingly employed to reduce manual effort and improve accuracy. This trend is expected to drive further market growth, even as challenges such as data security and privacy concerns, as well as the need for skilled annotators, persist. However, the overall market outlook remains positive, indicating continued strong growth potential through 2033. The increasing demand for AI and ML, coupled with technological advancements in annotation tools, is expected to overcome existing challenges and drive the market towards even greater heights.

Data Annotation Tools Market size was valued at USD 0.03 Billion in 2024 and is projected to reach USD 4.04 Billion by 2032, growing at a CAGR of 25.5% during the forecasted period 2026 to 2032.Global Data Annotation Tools Market DriversThe market drivers for the Data Annotation Tools Market can be influenced by various factors. These may include:Rapid Growth in AI and Machine Learning: The demand for data annotation tools to label massive datasets for training and validation purposes is driven by the rapid growth of AI and machine learning applications across a variety of industries, including healthcare, automotive, retail, and finance.Increasing Data Complexity: As data kinds like photos, videos, text, and sensor data become more complex, more sophisticated annotation tools are needed to handle a variety of data formats, annotations, and labeling needs. This will spur market adoption and innovation.Quality and Accuracy Requirements: Training accurate and dependable AI models requires high-quality annotated data. Organizations can attain enhanced annotation accuracy and consistency by utilizing data annotation technologies that come with sophisticated annotation algorithms, quality control measures, and human-in-the-loop capabilities.Applications Specific to Industries: The development of specialized annotation tools for particular industries, like autonomous vehicles, medical imaging, satellite imagery analysis, and natural language processing, is prompted by their distinct regulatory standards and data annotation requirements.

This is a dataset containing audio tags for a number of 3930 audio files of the TAU Urban Acoustic Scenes 2019 development dataset (airport, public square, and park). The files were annotated using a web-based tool, with multiple annotators providing labels for each file.

The dataset contains annotations for 3930 files, annotated with the following tags:

announcement jingle

announcement speech

adults talking

birds singing

children voices

dog barking

footsteps

music

siren

traffic noise

The annotation procedure and processing is presented in the paper:

Irene Martin-Morato, Annamaria Mesaros. What is the ground truth? Reliability of multi-annotator data for audio tagging, 29th European Signal Processing Conference, EUSIPCO 2021

The dataset contains the following:

raw annotations provided by 133 annotators, multiple opinions per audio file

  MATS_labels_full_annotations.yaml 


  content formatted as: 


      - filename: file1.wav 
       annotations:
       - annotator_id: ann_1
        tags:
        - tag1
        - tag2
       - annotator_id: ann_3
        tags:
        - tag1
      - filename: file3.wav 
      ...

processed annotations using different methods, as presented in the accompanying paper

    MATS_labels_majority_vote.csv
    MATS_labels_union.csv
    MATS_labels_mace100.csv  
    MATS_labels_mace100_competence60


    content formatted as: 


    filename [tab] tag1,tag2,tag3

The audio files can be downloaded from https://zenodo.org/record/2589280 and are covered by their own license.

Discover the booming Data Labeling Tools market: Explore key trends, growth drivers, and leading companies shaping the future of AI. This in-depth analysis projects significant expansion through 2033, revealing opportunities and challenges in this vital sector for machine learning. Learn more now!

Our dataset contains 2 weeks of approx. 8-9 hours of acceleration data per day from 11 participants wearing a Bangle.js Version 1 smartwatch with our firmware installed.

The dataset contains annotations from 4 different commonly used annotation methods utilized in user studies that focus on in-the-wild data. These methods can be grouped in user-driven, in situ annotations - which are performed before or during the activity is recorded - and recall methods - where participants annotate their data in hindsight at the end of the day.

The participants had the task to label their activities using (1) a button located on the smartwatch, (2) the activity tracking app Strava, (3) a (hand)written diary and (4) a tool to visually inspect and label activity data, called MAD-GUI. Methods (1)-(3) are used in both weeks, however method (4) is introduced in the beginning of the second study week.

The accelerometer data is recorded with 25 Hz, a sensitivity of ±8g and is stored in a csv format. Labels and raw data are not yet combined. You can either write your own script to label the data or follow the instructions in our corresponding Github repository.

The following unique classes are included in our dataset:

laying, sitting, walking, running, cycling, bus_driving, car_driving, vacuum_cleaning, laundry, cooking, eating, shopping, showering, yoga, sport, playing_games, desk_work, guitar_playing, gardening, table_tennis, badminton, horse_riding.

However, many activities are very participant specific and therefore only performed by one of the participants.

The labels are also stored as a .csv file and have the following columns:

week_day, start, stop, activity, layer

Example:

week2_day2,10:30:00,11:00:00,vacuum_cleaning,d

The layer columns specifies which annotation method was used to set this label.

The following identifiers can be found in the column:

b: in situ button

a: in situ app

d: self-recall diary

g: time-series recall labelled with a the MAD-GUI

The corresponding publication is currently under review.

Data Annotation And Labeling Market Size And Forecast

Data Annotation And Labeling Market size was valued to be USD 1080.8 Million in the year 2023 and it is expected to reach USD 8851.05 Million in 2031, growing at a CAGR of 35.10% from 2024 to 2031.

Data Annotation And Labeling Market Drivers

Increased Adoption of Artificial Intelligence (AI) and Machine Learning (ML): The demand for large volumes of high-quality labeled data to effectively train these systems is being driven by the widespread adoption of AI and ML technologies across various industries, thereby fueling the growth of the Data Annotation And Labeling Market.

Advancements in Computer Vision and Natural Language Processing: A need for annotated and labeled data to develop and enhance AI models capable of understanding and interpreting visual and textual data accurately is created by the rapid progress in fields such as computer vision and natural language processing.

Growth of Cloud Computing and Big Data: The adoption of AI and ML solutions has been facilitated by the rise of cloud computing and the availability of massive amounts of data, leading to an increased demand for data annotation and labeling services to organize and prepare this data for analysis and model training.

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

The Automated Data Annotation Tools market is experiencing rapid growth, driven by the increasing demand for high-quality training data in artificial intelligence (AI) and machine learning (ML) applications. The market, valued at $311.8 million in 2025, is projected to expand significantly over the forecast period (2025-2033), fueled by a robust Compound Annual Growth Rate (CAGR) of 19.7%. This expansion is primarily attributed to the rising adoption of AI across diverse sectors, including autonomous vehicles, healthcare, and finance, all requiring large volumes of accurately annotated data. Furthermore, the increasing complexity of AI models necessitates more sophisticated annotation techniques, further boosting market demand. The market is segmented by tool type (e.g., image annotation, text annotation, video annotation), deployment mode (cloud-based, on-premises), and industry vertical (e.g., automotive, healthcare, retail). Key players are strategically investing in R&D to enhance their offerings and expand their market share. Competition is intense, with both established tech giants and specialized startups vying for dominance. Challenges include the need for skilled annotators, data security concerns, and the high cost of annotation, particularly for complex datasets. The continued growth trajectory of the Automated Data Annotation Tools market is underpinned by several factors. Advancements in deep learning and the proliferation of AI applications in various sectors will continuously drive demand for precise and efficient annotation solutions. The emergence of innovative annotation techniques, such as automated labeling and active learning, will further streamline workflows and improve accuracy. However, maintaining data privacy and security remains a crucial aspect, requiring robust measures throughout the annotation process. Companies are focusing on developing scalable and cost-effective solutions to address these challenges, ultimately contributing to the market's sustained expansion. The competitive landscape is dynamic, with companies strategically employing mergers and acquisitions, partnerships, and product innovations to strengthen their position within this lucrative and rapidly evolving market.

The AI-assisted annotation tools market is booming, projected to reach $617 million by 2025 and grow at a CAGR of 9.2% through 2033. Learn about key drivers, trends, and leading companies shaping this rapidly expanding sector. Discover how AI is revolutionizing data annotation for machine learning.