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 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.

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.

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

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!

The booming Data Annotation & Collection Services market is projected to reach $75 Billion by 2033, fueled by AI adoption in autonomous driving, healthcare, and finance. Explore market trends, key players (Appen, Amazon, Google), and regional growth in this comprehensive analysis.

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.

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

The global data annotation platform market is expected to reach a value of USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). This growth is primarily attributed to the increasing demand for high-quality annotated data for training machine learning and artificial intelligence (AI) models. Data annotation involves labeling and classifying data, making it easier for AI models to understand and interpret complex information. Key drivers of the market include the rapid adoption of AI and machine learning across various industries, the increasing availability of unstructured data, and government initiatives to promote data annotation and AI development. The market is segmented by type (image annotation, text annotation, voice annotation, video annotation, others) and application (autonomous driving, smart healthcare, smart security, financial risk control, social media, others). The image annotation segment is expected to hold a significant market share due to its wide application in industries such as manufacturing, healthcare, and retail. The autonomous driving application segment is projected to witness substantial growth due to the increasing adoption of self-driving vehicles. Key industry players include BasicFinder, Jingdong Weigong, Alibaba Cloud, Appen (MatrixGo), Baidu, Longmao Data, Magic Data, Toloka AI, iFlytek, MindFlow, Huawei Cloud, DataBaker, Shujiajia, Human Signal, among others. The market is expected to witness significant growth in regions such as North America, Europe, and Asia Pacific due to the presence of major technology companies and the increasing demand for AI and machine learning solutions in these regions.

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.

Explore the dynamic Image Data Labeling Service market, projected for significant growth driven by AI advancements in automotive, healthcare, and IT. Discover key drivers, restraints, and regional opportunities.

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.

AI in Data Annotation Market Outlook

As per our latest research, the global AI in Data Annotation market size reached USD 2.6 billion in 2024, reflecting the accelerating adoption of artificial intelligence and machine learning across industries. The market is projected to grow at a robust CAGR of 25.8% from 2025 to 2033, with the market value expected to reach approximately USD 18.3 billion by 2033. This remarkable growth is primarily driven by the increasing demand for high-quality labeled datasets to train sophisticated AI models, particularly in sectors such as healthcare, autonomous vehicles, and retail. As organizations continue to invest in automation and intelligent systems, the need for scalable, accurate, and efficient data annotation solutions is set to surge, underpinning the long-term expansion of this market.

One of the most significant growth factors for the AI in Data Annotation market is the rapid evolution and deployment of artificial intelligence and machine learning technologies across diverse industries. As AI algorithms become more advanced, the requirement for accurately labeled data grows exponentially. Industries such as healthcare rely on annotated medical images and records to enhance diagnostic accuracy and accelerate drug discovery, while the automotive sector depends on labeled video and image data for the development of autonomous driving systems. The expansion of AI-powered virtual assistants, chatbots, and recommendation engines in retail and BFSI further elevates the importance of robust data annotation, ensuring that algorithms can interpret and respond to human inputs with precision. The proliferation of big data, combined with the increasing complexity of AI applications, is making data annotation an indispensable part of the AI development lifecycle.

Technological advancements in annotation tools and the integration of automation are also fueling market growth. The emergence of AI-assisted annotation platforms, which leverage natural language processing and computer vision, has significantly improved the speed and accuracy of data labeling. These platforms can automatically pre-label large datasets, reducing the manual effort required and minimizing human error. Additionally, the adoption of cloud-based annotation solutions enables organizations to scale their data labeling operations efficiently, supporting remote collaboration and real-time quality control. As more enterprises recognize the value of well-annotated data in gaining a competitive edge, investments in advanced annotation software and services are expected to rise, further propelling market expansion.

Another critical driver is the increasing emphasis on data privacy and regulatory compliance, particularly in sectors handling sensitive information. Organizations are seeking annotation solutions that ensure data security, confidentiality, and compliance with global regulations such as GDPR and HIPAA. This has led to the development of secure, on-premises annotation platforms and privacy-preserving techniques, such as federated learning and differential privacy. As regulatory scrutiny intensifies and data breaches become more commonplace, the demand for compliant and secure data annotation services is anticipated to witness substantial growth. The focus on ethical AI development, transparency, and bias mitigation also underscores the need for high-quality, unbiased labeled data, further supporting the expansion of the AI in Data Annotation market.

Regionally, North America continues to dominate the AI in Data Annotation market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The presence of major AI technology companies, robust research and development infrastructure, and early adoption of advanced analytics solutions are key factors driving market growth in North America. Meanwhile, Asia Pacific is emerging as the fastest-growing region, fueled by increasing investments in AI, rapid digital transformation, and the expansion of the IT and telecom sector. Europe remains a significant market, supported by strong regulatory frameworks and a focus on ethical AI. Latin America and the Middle East & Africa are also witnessing steady growth, driven by government initiatives and the adoption of AI in various industries.

Component Analysis

The AI in Data Annotation market is segmented by component into Software and Services, each playing a pivotal role in supporting the diverse needs of organizations e

Image Annotation Tool Market Outlook

The global image annotation tool market size is projected to grow from approximately $700 million in 2023 to an estimated $2.5 billion by 2032, exhibiting a remarkable compound annual growth rate (CAGR) of 15.2% over the forecast period. The surging demand for machine learning and artificial intelligence applications is driving this robust market expansion. Image annotation tools are crucial for training AI models to recognize and interpret images, a necessity across diverse industries.

One of the key growth factors fueling the image annotation tool market is the rapid adoption of AI and machine learning technologies across various sectors. Organizations in healthcare, automotive, retail, and many other industries are increasingly leveraging AI to enhance operational efficiency, improve customer experiences, and drive innovation. Accurate image annotation is essential for developing sophisticated AI models, thereby boosting the demand for these tools. Additionally, the proliferation of big data analytics and the growing necessity to manage large volumes of unstructured data have amplified the need for efficient image annotation solutions.

Another significant driver is the increasing use of autonomous systems and applications. In the automotive industry, for instance, the development of autonomous vehicles relies heavily on annotated images to train algorithms for object detection, lane discipline, and navigation. Similarly, in the healthcare sector, annotated medical images are indispensable for developing diagnostic tools and treatment planning systems powered by AI. This widespread application of image annotation tools in the development of autonomous systems is a critical factor propelling market growth.

The rise of e-commerce and the digital retail landscape has also spurred demand for image annotation tools. Retailers are using these tools to optimize visual search features, personalize shopping experiences, and enhance inventory management through automated recognition of products and categories. Furthermore, advancements in computer vision technology have expanded the capabilities of image annotation tools, making them more accurate and efficient, which in turn encourages their adoption across various industries.

Data Annotation Software plays a pivotal role in the image annotation tool market by providing the necessary infrastructure for labeling and categorizing images efficiently. These software solutions are designed to handle various annotation tasks, from simple bounding boxes to complex semantic segmentation, enabling organizations to generate high-quality training datasets for AI models. The continuous advancements in data annotation software, including the integration of machine learning algorithms for automated labeling, have significantly enhanced the accuracy and speed of the annotation process. As the demand for AI-driven applications grows, the reliance on robust data annotation software becomes increasingly critical, supporting the development of sophisticated models across industries.

Regionally, North America holds the largest share of the image annotation tool market, driven by significant investments in AI and machine learning technologies and the presence of leading technology companies. Europe follows, with strong growth supported by government initiatives promoting AI research and development. The Asia Pacific region presents substantial growth opportunities due to the rapid digital transformation in emerging economies and increasing investments in technology infrastructure. Latin America and the Middle East & Africa are also expected to witness steady growth, albeit at a slower pace, due to the gradual adoption of advanced technologies.

Component Analysis

The image annotation tool market by component is segmented into software and services. The software segment dominates the market, encompassing a variety of tools designed for different annotation tasks, from simple image labeling to complex polygonal, semantic, or instance segmentation. The continuous evolution of software platforms, integrating advanced features such as automated annotation and machine learning algorithms, has significantly enhanced the accuracy and efficiency of image annotations. Furthermore, the availability of open-source annotation tools has lowered the entry barrier, allowing more organizations to adopt these technologies.

Services associated with image ann

Data Annotation Outsourcing Market size was valued at USD 0.8 Billion in 2023 and is projected to reach USD 3.6 Billion by 2031, growing at a CAGR of 33.2%during the forecasted period 2024 to 2031.

Global Data Annotation Outsourcing Market Drivers

The market drivers for the Data Annotation Outsourcing Market can be influenced by various factors. These may include:

Fast Growth in AI and Machine Learning Applications: The need for data annotation services has increased as a result of the need for huge amounts of labeled data for training AI and machine learning models. Companies can focus on their core skills by outsourcing these processes and yet receive high-quality annotated data.

Growing Need for High-Quality Labeled Data: The efficacy of AI models depends on precise data labeling. In order to achieve accurate and reliable data labeling, businesses are outsourcing their annotation responsibilities to specialist service providers, which is propelling market expansion.

Global Data Annotation Outsourcing Market Restraints

Several factors can act as restraints or challenges for the Data Annotation Outsourcing Market. These may include:

Data Privacy and Security Issues: It can be difficult to guarantee data privacy and security. Strict rules and guidelines must be followed by businesses in order to protect sensitive data, which can be expensive and complicated.

Problems with Quality Control: It can be difficult to maintain consistent and high-quality data annotation when working with numerous vendors. The effectiveness of AI and machine learning models might be impacted by inconsistent or inaccurate data annotations.

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.

The global data annotation tools market is anticipated to grow significantly over the forecast period, reaching a projected value of 1,639.44 million by 2033. This growth is attributed to the rising demand for data annotation in the fields of artificial intelligence (AI), machine learning (ML), and data science. The increase in the volume and complexity of data being generated is also contributing to the market growth. Key drivers of the market include the increasing adoption of AI and ML across various industries, the need for accurate data annotation for training machine learning models, and the growing demand for data annotation services for applications such as object detection, image segmentation, and natural language processing. Some of the major players in the market include IBM, Google, Microsoft, Amazon Web Services (AWS), and Hive. Key drivers for this market are: AI and ML advancementsExpansion of autonomous vehiclesGrowth of smart citiesProliferation of IoT devicesRise of cloud computing. Potential restraints include: Growing adoption of AI and MLIncreasing demand for high-quality annotated dataRise of data-intensive applicationsEmergence of cloud-based annotation toolsGrowing need for data governance and compliance.

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.