AI Assisted Annotation Tools Market Outlook

In 2023, the global AI assisted annotation tools market size was valued at approximately USD 600 million. Propelled by increasing demand for labeled data in machine learning and AI-driven applications, the market is expected to grow at a CAGR of 25% from 2024 to 2032, reaching an estimated market size of USD 3.3 billion by 2032. Factors such as advancements in AI technologies, an upsurge in data generation, and the need for accurate data labeling are fueling this growth.

The rapid proliferation of AI and machine learning (ML) has necessitated the development of robust data annotation tools. One of the key growth factors is the increasing reliance on AI for commercial and industrial applications, which require vast amounts of accurately labeled data to train AI models. Industries such as healthcare, automotive, and retail are heavily investing in AI technologies to enhance operational efficiencies, improve customer experience, and foster innovation. Consequently, the demand for AI-assisted annotation tools is expected to soar, driving market expansion.

Another significant growth factor is the growing complexity and volume of data generated across various sectors. With the exponential increase in data, the manual annotation process becomes impractical, necessitating automated or semi-automated tools to handle large datasets efficiently. AI-assisted annotation tools offer a solution by improving the speed and accuracy of data labeling, thereby enabling businesses to leverage AI capabilities more effectively. This trend is particularly pronounced in sectors like IT and telecommunications, where data volumes are immense.

Furthermore, the rise of personalized and precision medicine in healthcare is boosting the demand for AI-assisted annotation tools. Accurate data labeling is crucial for developing advanced diagnostic tools, treatment planning systems, and patient management solutions. AI-assisted annotation tools help in labeling complex medical data sets, such as MRI scans and histopathological images, ensuring high accuracy and consistency. This demand is further amplified by regulatory requirements for data accuracy and reliability in medical applications, thereby driving market growth.

The evolution of the Image Annotation Tool has been pivotal in addressing the challenges posed by the increasing complexity of data. These tools have transformed the way industries handle data, enabling more efficient and accurate labeling processes. By automating the annotation of images, these tools reduce the time and effort required to prepare data for AI models, particularly in fields like healthcare and automotive, where precision is paramount. The integration of AI technologies within these tools allows for continuous learning and improvement, ensuring that they can adapt to the ever-changing demands of data annotation. As a result, businesses can focus on leveraging AI capabilities to drive innovation and enhance operational efficiencies.

From a regional perspective, North America remains the dominant player in the AI-assisted annotation tools market, primarily due to the early adoption of AI technologies and significant investments in AI research and development. The presence of major technology companies and a robust infrastructure for AI implementation further bolster this dominance. However, the Asia Pacific region is expected to witness the highest CAGR during the forecast period, driven by increasing digital transformation initiatives, growing investments in AI, and expanding IT infrastructure.

Component Analysis

The AI-assisted annotation tools market is segmented into software and services based on components. The software segment holds a significant share of the market, primarily due to the extensive deployment of annotation software across various industries. These software solutions are designed to handle diverse data types, including text, image, audio, and video, providing a comprehensive suite of tools for data labeling. The continuous advancements in AI algorithms and machine learning models are driving the development of more sophisticated annotation software, further enhancing their accuracy and efficiency.

Within the software segment, there is a growing trend towards the integration of AI and machine learning capabilities to automate the annotation process. This integration reduces the dependency on manual efforts, significantly improving the speed and s

The premium annotation tools market, valued at $1169.4 million in 2025, is projected to experience robust growth, driven by the increasing demand for high-quality training data in artificial intelligence (AI) and machine learning (ML) applications. The market's Compound Annual Growth Rate (CAGR) of 8.1% from 2025 to 2033 signifies a substantial expansion, fueled by several key factors. The rise of sophisticated AI models necessitates meticulously annotated datasets for optimal performance. This is particularly crucial in sectors like autonomous vehicles, medical image analysis, and natural language processing, where accuracy is paramount. The shift towards cloud-based and web-based annotation tools simplifies data management, collaboration, and scalability, further boosting market adoption. Segment-wise, the student and worker application segments are expected to see significant growth due to the increasing accessibility and affordability of these tools, while cloud-based solutions are poised to dominate owing to their flexibility and scalability advantages. Geographic expansion, particularly in regions with burgeoning tech industries like Asia Pacific and North America, will also contribute to the overall market growth. Competitive pressures among established players and emerging startups are driving innovation and affordability, making premium annotation tools more accessible to a wider range of users. Despite the positive outlook, the market faces certain challenges. The high cost of premium tools and the need for skilled annotators can be entry barriers for smaller businesses and individuals. Additionally, data privacy and security concerns related to sensitive datasets used in annotation remain a critical factor influencing market growth. However, the continuous advancements in automation and AI-powered annotation techniques are likely to mitigate these concerns. The ongoing evolution of annotation techniques, such as incorporating active learning and transfer learning, promises to further increase efficiency and reduce annotation costs, fostering wider adoption across various industries and accelerating market expansion.

The manual data annotation tools market, valued at $949.7 million in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 13.6% from 2025 to 2033. This surge is driven by the escalating demand for high-quality training data across diverse sectors. The increasing adoption of artificial intelligence (AI) and machine learning (ML) models necessitates large volumes of meticulously annotated data for optimal performance. Industries like IT & Telecom, BFSI (Banking, Financial Services, and Insurance), Healthcare, and Automotive are leading the charge, investing significantly in data annotation to improve their AI-powered applications, from fraud detection and medical image analysis to autonomous vehicle development and personalized customer experiences. The market is segmented by data type (image, video, text, audio) and application sector, reflecting the diverse needs of various industries. The rise of cloud-based annotation platforms is streamlining workflows and enhancing accessibility, while the increasing complexity of AI models is pushing the demand for more sophisticated and specialized annotation techniques. The competitive landscape is characterized by a mix of established players and emerging startups. Companies like Appen, Amazon Web Services, Google, and IBM are leveraging their extensive resources and technological capabilities to dominate the market. However, smaller, specialized companies are also making significant strides, catering to niche needs and offering innovative solutions. Geographic expansion is another key trend, with North America currently holding a substantial market share due to its advanced technology adoption and significant investments in AI research. However, Asia-Pacific, especially India and China, is witnessing rapid growth fueled by expanding digitalization and increasing government initiatives promoting AI development. Despite the rapid growth, challenges remain, including the high cost and time-consuming nature of manual annotation, alongside concerns around data privacy and security. The market's future trajectory will depend on technological advancements, evolving industry needs, and the effective addressal of these challenges.

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

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Data Labeling And Annotation Tools Market Size 2025-2029

The data labeling and annotation tools market size is forecast to increase by USD 2.69 billion at a CAGR of 28% between 2024 and 2029.

The market is experiencing significant growth, driven by the explosive expansion of generative AI applications. As AI models become increasingly complex, there is a pressing need for specialized platforms to manage and label the vast amounts of data required for training. This trend is further fueled by the emergence of generative AI, which demands unique data pipelines for effective training. However, this market's growth trajectory is not without challenges. Maintaining data quality and managing escalating complexity pose significant obstacles. ML models are being applied across various sectors, from fraud detection and sales forecasting to speech recognition and image recognition.
Ensuring the accuracy and consistency of annotated data is crucial for AI model performance, necessitating robust quality control measures. Moreover, the growing complexity of AI systems requires advanced tools to handle intricate data structures and diverse data types. The market continues to evolve, driven by advancements in machine learning (ML), computer vision, and natural language processing. Companies seeking to capitalize on market opportunities must address these challenges effectively, investing in innovative solutions to streamline data labeling and annotation processes while maintaining high data quality.

What will be the Size of the Data Labeling And Annotation Tools Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market is experiencing significant activity and trends, with a focus on enhancing annotation efficiency, ensuring data privacy, and improving model performance. Annotation task delegation and remote workflows enable teams to collaborate effectively, while version control systems facilitate model deployment pipelines and error rate reduction. Label inter-annotator agreement and quality control checks are crucial for maintaining data consistency and accuracy. Data security and privacy remain paramount, with cloud computing and edge computing solutions offering secure alternatives. Data privacy concerns are addressed through secure data handling practices and access controls. Model retraining strategies and cost optimization techniques are essential for adapting to evolving datasets and budgets. Dataset bias mitigation and accuracy improvement methods are key to producing high-quality annotated data.

Training data preparation involves data preprocessing steps and annotation guidelines creation, while human-in-the-loop systems allow for real-time feedback and model fine-tuning. Data validation techniques and team collaboration tools are essential for maintaining data integrity and reducing errors. Scalable annotation processes and annotation project management tools streamline workflows and ensure a consistent output. Model performance evaluation and annotation tool comparison are ongoing efforts to optimize processes and select the best tools for specific use cases. Data security measures and dataset bias mitigation strategies are essential for maintaining trust and reliability in annotated data.

How is this Data Labeling And Annotation Tools Industry segmented?

The data labeling and annotation tools industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Text
  Video
  Image
  Audio


Technique

  Manual labeling
  Semi-supervised labeling
  Automatic labeling


Deployment

  Cloud-based
  On-premises


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    Spain
    UK


  APAC

    China


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The Text segment is estimated to witness significant growth during the forecast period. The data labeling market is witnessing significant growth and advancements, primarily driven by the increasing adoption of generative artificial intelligence and large language models (LLMs). This segment encompasses various annotation techniques, including text annotation, which involves adding structured metadata to unstructured text. Text annotation is crucial for machine learning models to understand and learn from raw data. Core text annotation tasks range from fundamental natural language processing (NLP) techniques, such as Named Entity Recognition (NER), where entities like persons, organizations, and locations are identified and tagged, to complex requirements of modern AI.

Moreover,

The size of the Data Annotation Tool Market market was valued at USD 3.9 USD billion in 2023 and is projected to reach USD 6.64 USD billion by 2032, with an expected CAGR of 7.9% during the forecast period. A Data Annotation Tool is defined as the software that can be employed to make annotations to data hence helping a learning computer model learn patterns. These tools provide a way of segregating the data types to include images, texts, and audio, as well as videos. Some of the subcategories of annotation include images such as bounding boxes, segmentation, text such as entity recognition, sentiment analysis, audio such as transcription, sound labeling, and video such as object tracking. Other common features depend on the case but they commonly consist of interfaces, cooperation with others, suggestion of labels, and quality assurance. It can be used in the automotive industry (object detection for self-driving cars), text processing (classification of text), healthcare (medical imaging), and retail (recommendation). These tools get applied in training good quality, accurately labeled data sets for the engineering of efficient AI systems. Key drivers for this market are: Increasing Adoption of Cloud-based Managed Services to Drive Market Growth. Potential restraints include: Adverse Health Effect May Hamper Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

The data annotation outsourcing market is experiencing robust growth, driven by the increasing demand for high-quality training data to fuel the advancements in artificial intelligence (AI) and machine learning (ML) technologies. The market's expansion is fueled by several key factors, including the proliferation of AI-powered applications across various industries – from autonomous vehicles and healthcare to finance and retail – each requiring vast amounts of accurately annotated data for optimal performance. This surge in demand is pushing organizations to outsource data annotation tasks to specialized providers, leveraging their expertise and cost-effective solutions. The market is segmented based on various annotation types (image, text, video, audio), application domains, and geographic regions. While North America currently holds a significant market share due to the high concentration of AI companies and robust technological infrastructure, regions like Asia-Pacific are exhibiting rapid growth, driven by increasing digitalization and government initiatives promoting AI development. Competition is intensifying among established players and emerging startups, leading to innovations in annotation techniques, automation tools, and quality control measures. The forecast period (2025-2033) anticipates continued strong growth, propelled by the ongoing advancements in AI and ML algorithms, which require ever-larger and more complex datasets. Challenges such as data security, maintaining data quality consistency across different annotation providers, and addressing ethical concerns surrounding data sourcing and usage will continue to influence market dynamics. Nevertheless, the overall outlook remains positive, with the market poised for substantial expansion, driven by the increasing reliance on AI across various industries and the growing availability of sophisticated annotation tools and techniques. Key players are focusing on strategic partnerships, acquisitions, and technological innovations to enhance their market position and cater to the evolving needs of their clients. The market’s overall value is projected to exceed expectations, outpacing initial estimations based on the observed acceleration in AI adoption.

AI-Powered Medical Imaging Annotation Market Outlook

According to our latest research, the AI-powered medical imaging annotation market size reached USD 1.85 billion globally in 2024. The market is experiencing robust expansion, driven by technological advancements and the rising adoption of artificial intelligence in healthcare. The market is projected to grow at a CAGR of 27.8% from 2025 to 2033, reaching a forecasted value of USD 15.69 billion by 2033. The primary growth factor fueling this trajectory is the increasing demand for accurate, scalable, and rapid annotation solutions to support AI-driven diagnostics and decision-making in clinical settings.

The growth of the AI-powered medical imaging annotation market is propelled by the exponential rise in medical imaging data generated by advanced diagnostic modalities. As healthcare providers continue to digitize patient records and imaging workflows, there is a pressing need for sophisticated annotation tools that can efficiently label vast volumes of images for training and validating AI algorithms. This trend is further amplified by the integration of machine learning and deep learning techniques, which require large, well-annotated datasets to achieve high accuracy in disease detection and classification. Consequently, hospitals, research institutes, and diagnostic centers are increasingly investing in AI-powered annotation platforms to streamline their operations and enhance clinical outcomes.

Another significant driver for the market is the growing prevalence of chronic diseases and the subsequent surge in diagnostic imaging procedures. Conditions such as cancer, cardiovascular diseases, and neurological disorders necessitate frequent imaging for early detection, monitoring, and treatment planning. The complexity and volume of these images make manual annotation labor-intensive and prone to variability. AI-powered annotation solutions address these challenges by automating the labeling process, ensuring consistency, and significantly reducing turnaround times. This not only improves the efficiency of radiologists and clinicians but also accelerates the deployment of AI-based diagnostic tools in routine clinical practice.

The evolution of regulatory frameworks and the increasing emphasis on data quality and patient safety are also shaping the growth of the AI-powered medical imaging annotation market. Regulatory agencies worldwide are encouraging the adoption of AI in healthcare, provided that the underlying data used for algorithm development is accurately annotated and validated. This has led to the emergence of specialized service providers offering compliant annotation solutions tailored to the stringent requirements of medical device approvals and clinical trials. As a result, the market is witnessing heightened collaboration between healthcare providers, technology vendors, and regulatory bodies to establish best practices and standards for medical image annotation.

Regionally, North America continues to dominate the AI-powered medical imaging annotation market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, benefits from a mature healthcare IT infrastructure, strong research funding, and a high concentration of leading AI technology companies. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by rapid healthcare digitization, increasing investments in AI research, and expanding patient populations. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a slower pace, as healthcare systems modernize and adopt advanced imaging technologies.

Component Analysis

The component segment of the AI-powered medical imaging annotation market is bifurcated into software and services, both of which play pivotal roles in the overall ecosystem. Software solutions encompass annotation platforms, data management tools, and integration modules that enable seamless image labeling, workflow automation, and interoperability with existing hospital information systems. These platforms leverage advanced algorithms for image segmentation, object detection, and feature extraction, significantly enhancing the speed and accuracy of annotation tasks. The increasing sophistication of annotation software, including support for multi-modality images and customizable labeling protocols, is driving widespread adoption among health

DESCRIPTION
For this task, we use a subset of the MIRFLICKR (http://mirflickr.liacs.nl) collection. The entire collection contains 1 million images from the social photo sharing website Flickr and was formed by downloading up to a thousand photos per day that were deemed to be the most interesting according to Flickr. All photos in this collection were released by their users under a Creative Commons license, allowing them to be freely used for research purposes. Of the entire collection, 25 thousand images were manually annotated with a limited number of concepts and many of these annotations have been further refined and expanded over the lifetime of the ImageCLEF photo annotation task. This year we used crowd sourcing to annotate all of these 25 thousand images with the concepts.

On this page we provide you with more information about the textual features, visual features and concept features we supply with each image in the collection we use for this year's task.

TEXTUAL FEATURES
All images are accompanied by the following textual features:

- Flickr user tags
These are the tags that the users assigned to the photos their uploaded to Flickr. The 'raw' tags are the original tags, while the 'clean' tags are those collapsed to lowercase and condensed to removed spaces.

- EXIF metadata
If available, the EXIF metadata contains information about the camera that took the photo and the parameters used. The 'raw' exif is the original camera data, while the 'clean' exif reduces the verbosity.

- User information and Creative Commons license information
This contains information about the user that took the photo and the license associated with it.

VISUAL FEATURES
Over the previous years of the photo annotation task we noticed that often the same types of visual features are used by the participants, in particular features based on interest points and bag-of-words are popular. To assist you we have extracted several features for you that you may want to use, so you can focus on the concept detection instead. We additionally give you some pointers to easy to use toolkits that will help you extract other features or the same features but with different default settings.

- SIFT, C-SIFT, RGB-SIFT, OPPONENT-SIFT
We used the ISIS Color Descriptors (http://www.colordescriptors.com) toolkit to extract these descriptors. This package provides you with many different types of features based on interest points, mostly using SIFT. It furthermore assists you with building codebooks for bag-of-words. The toolkit is available for Windows, Linux and Mac OS X.

- SURF
We used the OpenSURF (http://www.chrisevansdev.com/computer-vision-opensurf.html) toolkit to extract this descriptor. The open source code is available in C++, C#, Java and many more languages.

- TOP-SURF
We used the TOP-SURF (http://press.liacs.nl/researchdownloads/topsurf) toolkit to extract this descriptor, which represents images with SURF-based bag-of-words. The website provides codebooks of several different sizes that were created using a combination of images from the MIR-FLICKR collection and from the internet. The toolkit also offers the ability to create custom codebooks from your own image collection. The code is open source, written in C++ and available for Windows, Linux and Mac OS X.

- GIST
We used the LabelMe (http://labelme.csail.mit.edu) toolkit to extract this descriptor. The MATLAB-based library offers a comprehensive set of tools for annotating images.

For the interest point-based features above we used a Fast Hessian-based technique to detect the interest points in each image. This detector is built into the OpenSURF library. In comparison with the Hessian-Laplace technique built into the ColorDescriptors toolkit it detects fewer points, resulting in a considerably reduced memory footprint. We therefore also provide you with the interest point locations in each image that the Fast Hessian-based technique detected, so when you would like to recalculate some features you can use them as a starting point for the extraction. The ColorDescriptors toolkit for instance accepts these locations as a separate parameter. Please go to http://www.imageclef.org/2012/photo-flickr/descriptors for more information on the file format of the visual features and how you can extract them yourself if you want to change the default settings.

CONCEPT FEATURES
We have solicited the help of workers on the Amazon Mechanical Turk platform to perform the concept annotation for us. To ensure a high standard of annotation we used the CrowdFlower platform that acts as a quality control layer by removing the judgments of workers that fail to annotate properly. We reused several concepts of last year's task and for most of these we annotated the remaining photos of the MIRFLICKR-25K collection that had not yet been used before in the previous task; for some concepts we reannotated all 25,000 images to boost their quality. For the new concepts we naturally had to annotate all of the images.

- Concepts
For each concept we indicate in which images it is present. The 'raw' concepts contain the judgments of all annotators for each image, where a '1' means an annotator indicated the concept was present whereas a '0' means the concept was not present, while the 'clean' concepts only contain the images for which the majority of annotators indicated the concept was present. Some images in the raw data for which we reused last year's annotations only have one judgment for a concept, whereas the other images have between three and five judgments; the single judgment does not mean only one annotator looked at it, as it is the result of a majority vote amongst last year's annotators.

- Annotations
For each image we indicate which concepts are present, so this is the reverse version of the data above. The 'raw' annotations contain the average agreement of the annotators on the presence of each concept, while the 'clean' annotations only include those for which there was a majority agreement amongst the annotators.

You will notice that the annotations are not perfect. Especially when the concepts are more subjective or abstract, the annotators tend to disagree more with each other. The raw versions of the concept annotations should help you get an understanding of the exact judgments given by the annotators.

Image Tagging and Annotation Services Market Outlook

The global image tagging and annotation services market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach around USD 4.8 billion by 2032, growing at a compound annual growth rate (CAGR) of about 14%. This robust growth is driven by the exponential rise in demand for machine learning and artificial intelligence applications, which heavily rely on annotated datasets to train algorithms effectively. The surge in digital content creation and the increasing need for organized data for analytical purposes are also significant contributors to the market expansion.

One of the primary growth factors for the image tagging and annotation services market is the increasing adoption of AI and machine learning technologies across various industries. These technologies require large volumes of accurately labeled data to function optimally, making image tagging and annotation services crucial. Specifically, sectors such as healthcare, automotive, and retail are investing in AI-driven solutions that necessitate high-quality annotated images to enhance machine learning models' efficiency. For example, in healthcare, annotated medical images are essential for developing tools that can aid in diagnostics and treatment decisions. Similarly, in the automotive industry, annotated images are pivotal for the development of autonomous vehicles.

Another significant driver is the growing emphasis on improving customer experience through personalized solutions. Companies are leveraging image tagging and annotation services to better understand consumer behavior and preferences by analyzing visual content. In retail, for instance, businesses analyze customer-generated images to tailor marketing strategies and improve product offerings. Additionally, the integration of augmented reality (AR) and virtual reality (VR) in various applications has escalated the need for precise image tagging and annotation, as these technologies rely on accurately labeled datasets to deliver immersive experiences.

Data Collection and Labeling are foundational components in the realm of image tagging and annotation services. The process of collecting and labeling data involves gathering vast amounts of raw data and meticulously annotating it to create structured datasets. These datasets are crucial for training machine learning models, enabling them to recognize patterns and make informed decisions. The accuracy of data labeling directly impacts the performance of AI systems, making it a critical step in the development of reliable AI applications. As industries increasingly rely on AI-driven solutions, the demand for high-quality data collection and labeling services continues to rise, underscoring their importance in the broader market landscape.

The rising trend of digital transformation across industries has also significantly bolstered the demand for image tagging and annotation services. Organizations are increasingly investing in digital tools that can automate processes and enhance productivity. Image annotation plays a critical role in enabling technologies such as computer vision, which is instrumental in automating tasks ranging from quality control to inventory management. Moreover, the proliferation of smart devices and the Internet of Things (IoT) has led to an unprecedented amount of image data generation, further fueling the need for efficient image tagging and annotation services to make sense of the vast data deluge.

From a regional perspective, North America is currently the largest market for image tagging and annotation services, attributed to the early adoption of advanced technologies and the presence of numerous tech giants investing in AI and machine learning. The region is expected to maintain its dominance due to ongoing technological advancements and the growing demand for AI solutions across various sectors. Meanwhile, the Asia Pacific region is anticipated to experience the fastest growth during the forecast period, driven by rapid industrialization, increasing internet penetration, and the rising adoption of AI technologies in countries like China, India, and Japan. The European market is also witnessing steady growth, supported by government initiatives promoting digital innovation and the use of AI-driven applications.

Service Type Analysis

The service type segment in the image tagging and annotation services market is bifurcated into manual annotation and automa

This dataset contains 4,599 high-quality, annotated images of 25 commonly used chemistry lab apparatuses. The images, each containing structures in real-world settings, have been captured from different angles, backgrounds, and distances, while also undergoing variations in lighting to aid in the robustness of object detection models. Every image has been labeled using bounding box annotation in YOLO and COCO format, alongside the class IDs and normalized bounding box coordinates making object detection more precise. The annotations and bounding boxes have been built using the Roboflow platform.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 6,960 labeled instances per 25 apparatus categories including beakers, conical flasks, measuring cylinders, test tubes, among others. The dataset can be utilized for the development of automation systems, real-time monitoring and tracking systems, tools for safety monitoring, alongside AI educational tools.

Data Annotation Market Outlook

According to our latest research, the global data annotation market size reached USD 2.15 billion in 2024, fueled by the rapid proliferation of artificial intelligence and machine learning applications across industries. The market is witnessing a robust growth trajectory, registering a CAGR of 26.3% during the forecast period from 2025 to 2033. By 2033, the data annotation market is projected to attain a valuation of USD 19.14 billion. This growth is primarily driven by the increasing demand for high-quality annotated datasets to train sophisticated AI models, the expansion of automation in various sectors, and the escalating adoption of advanced technologies in emerging economies.

The primary growth factor propelling the data annotation market is the surging adoption of artificial intelligence and machine learning across diverse sectors such as healthcare, automotive, retail, and IT & telecommunications. Organizations are increasingly leveraging AI-driven solutions for predictive analytics, automation, and enhanced decision-making, all of which require meticulously labeled datasets for optimal performance. The proliferation of computer vision, natural language processing, and speech recognition technologies has further intensified the need for accurate data annotation, as these applications rely heavily on annotated images, videos, text, and audio to function effectively. As businesses strive for digital transformation and increased operational efficiency, the demand for comprehensive data annotation services and software continues to escalate, thereby driving market expansion.

Another significant driver for the data annotation market is the growing complexity and diversity of data types being utilized in AI projects. Modern AI systems require vast amounts of annotated data spanning multiple formats, including text, images, videos, and audio. This complexity has led to the emergence of specialized data annotation tools and services capable of handling intricate annotation tasks, such as semantic segmentation, entity recognition, and sentiment analysis. Moreover, the integration of data annotation platforms with cloud-based solutions and workflow automation tools has streamlined the annotation process, enabling organizations to scale their AI initiatives efficiently. As a result, both large enterprises and small-to-medium businesses are increasingly investing in advanced annotation solutions to maintain a competitive edge in their respective industries.

Furthermore, the rise of data-centric AI development methodologies has placed greater emphasis on the quality and diversity of training datasets, further fueling the demand for professional data annotation services. Companies are recognizing that the success of AI models is heavily dependent on the accuracy and representativeness of the annotated data used during training. This realization has spurred investments in annotation technologies that offer features such as quality control, real-time collaboration, and integration with machine learning pipelines. Additionally, the growing trend of outsourcing annotation tasks to specialized service providers in regions with cost-effective labor markets has contributed to the market's rapid growth. As AI continues to permeate new domains, the need for scalable, high-quality data annotation solutions is expected to remain a key growth driver for the foreseeable future.

From a regional perspective, North America currently dominates the data annotation market, accounting for the largest share due to the presence of major technology companies, robust research and development activities, and early adoption of AI technologies. However, the Asia Pacific region is expected to exhibit the fastest growth over the forecast period, driven by increasing investments in AI infrastructure, the expansion of IT and telecommunication networks, and the availability of a large, skilled workforce for annotation tasks. Europe also represents a significant market, characterized by stringent data privacy regulations and growing demand for AI-driven automation in industries such as automotive and healthcare. As global enterprises continue to prioritize AI initiatives, the data annotation market is poised for substantial growth across all major regions.

Endoscope Semantic Segmentation

Introduction

Computer-assisted surgery has been developed to enhance surgery correctness and safety. However, the researchers and engineers suffer from limited annotated data to develop and train better algorithms. Consequently, the development of fundamental algorithms such as Simultaneous Localization and Mapping (SLAM) are limited. This article elaborates the efforts of preparing the dataset for semantic segmentation, which is the foundation of many computer-assisted surgery mechanisms. Based on the Cholec80 dataset [1], we extracted 8,080 laparoscopic cholecystectomy image frames from 17 video clips in Cholec80, annotated the images, and released them to the public. The dataset is named CholecSeg8K. Each of these images are annotated at pixel-level for thirteen classes, which are commonly founded in laparoscopic cholecystectomy surgery. CholecSeg8k is released under the license CC BY-NC-SA 4.0.

Data Collection

CholecSeg8K dataset uses the endoscopic images from Cholec80 [1], which is provided by Research Group CAMMA (Computational Analysis and Modeling of Medical Activities), as the base. The research group cooperated with the University Hospital of Strasbourg, IHU Strasbourg, and IRCAD to construct the dataset. Cholec80 contains 80 videos of cholecystectomy surgeries performed by 13 surgeons. Each video in Cholec80 captured the procedure at 25 fps and annotated tools presence and operation phases. Our work selected a subset of the videos provided by Cholec80 and created semantic segmentation masks at extracted frames in the selected videos.

Data Details

Data in CholecSeg8K Dataset are grouped into a directory tree for better organization and accessibility. Each directory on the first level tree represents the video clips extract from Cholec80 and is named by the filename of the video clips. Each directory on the secondary level tree store the raw image data, annotation, and color masks for an 80 frame video clip. The directory is named according to the filename of original video clips and the index of the starting frame of the extracted video clip.

The frames are extracted and placed into directories, each containing 80 consecutive frames of the video with a resolution of 854x480 and the annotated semantic segmentation masks. There are a total of 101 directories and the total number of frames is 8080. The total number of classes of different objects is 13, including black background, abdominal wall, liver, gastrointestinal tract, fat, grasper, connective tissue, blood, cystic duct, L hook electrocautery (Instrument), gallbladder, hepatic vein, and liver ligament. In this dataset, not all 13 would appear in the same frame at the same time.

In the dataset, each frame comes with three masks, one color mask, one mask used by the annotation tool, and one watershed mask. The color mask is mainly used for visualization. The watershed mask contains objects with simpler pixel readings, i.e., the same value for three channels, for easier processing. The values are the IDs of the class in the annotation tool. The annotation tool used the other mask, which is the hand-drawn one during annotation, to generate the color mask and the watershed mask. The label is both presented in color masks and watershed masks. The watershed masks store the ID of the classes as pixel values for all three channels. The color mask paints the classes in different colors. The IDs and the colors are defined in the annotation tool.

Examples of Labeled Data

Table I shows the corresponding class names of the class numbers in Figure 1, 2, 3 and the RGB hex code in the watershed masks

Table I: Class numbers and their corresponding classnames

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5861215%2Faa3ba9cd043a72841e5bb9244543ac5f%2FGOOD.png?generation=1608259335102559&alt=media" alt="">

Figure 1: Example of Semantic Segmentation Label of Endoscope Image 1

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5850324%2F25bc29882ce088789376afa0c472...

Project Title Object Detection Model for Identification of Poisonous Plants in the Chesapeake Bay Watershed by Shameer Rao

Model Overview Time outside is crucial for our health, but there are risks in the great outdoors. One of the most significant issues we may encounter is poisonous plants. There isn't a singular rule to recognize them; they are not all bright red, nor do they all have three leaves. An encounter with a harmful plant could cause rashes, itching, and swelling. This object detection model will identify the four most common poisonous plants in the Chesapeake Bay Watershed, with the intended audience being American states within the Chesapeake Bay area (Delaware, Maryland, New York, Pennsylvania, Virginia, and West Virginia—and the District of Columbia). The model will have five classes; Giant Hogweed (Heracleum mantegazzianum), Poison Hemlock (Conium maculatum), Spotted Water Hemlock (Cicuta maculata), Mayapple (Podophyllum peltatum), and a null class.

Model Structure Roboflow will be used to create the model, with five classes; Giant Hogweed (Heracleum mantegazzianum), Poison Hemlock (Conium maculatum), Spotted Water Hemlock (Cicuta maculata), Mayapple (Podophyllum peltatum), and a null class to store nonessential results. I have chosen Roboflow for its in-depth analytics and various optimization tools. The smart annotation tool is one of its best features and increases workflow when annotating. Additionally, having more familiarity with Roboflow compared to Google’s Teachable Machine is also an advantage.

Data Collection Plan Each class will be trained with 100 images taken during the daytime, containing as little background noise as possible, and focused on most parts of the plants. All photos must be in JPEG or PNG format. Image size will not be an eliminating factor, but all images will be backed-up on Google Drive in case of cropping or editing the pictures. With these parameters set, I hope that the rules either eliminate or reduce bias within this model. Additionally, the images will be collected from the iNaturalist, CDC, NPS, and MD DNR websites.

Minimal Viable Product The object detection model should reach a 40.0% mAP, 50.0% precision, and 40.0% recall to be considered a success, with each class at a 50% accuracy rate as well. Although my initial benchmark is low, I aim to reach this threshold in the first or second iteration of the model. Upon reaching this threshold, the final milestone should increase up to 65.0% mAP, 75.0% precision, and 60.0% recall. These milestones should be feasible as I reached 67.2% mAP, 76.0% precision, and 61.1% recall on my second iteration of the Shark Tooth Model. I expect Giant Hogweed (Heracleum mantegazzianum), and Spotted Water Hemlock (Cicuta maculata) classes to have a lower accuracy rate due to their similarity in features. Additionally, I expect Mayapple (Podophyllum peltatum) to perform the best as it has more distinct features than the other three classes.

Use cases for this project:

Ecological Conservation: Conservationists and ecologists in the Chesapeake Bay Watershed can use the object detection model to monitor and track the spread of these poisonous plant species. By detecting their presence in various ecosystems, specialists can take appropriate measures to control their growth and prevent damage to native species.

Public Health and Safety: Local governments and parks departments can utilize this model to identify and remove poisonous plants from public spaces such as parks, hiking trails, and playgrounds. This would reduce the risk of accidental exposure to these plants, ensuring a safer outdoor environment for the community.

Agricultural Management: Farmers and landowners in the Chesapeake Bay Watershed can use the computer vision model to detect the presence of poisonous plants on their property. This would help them avoid cultivating or accidentally spreading these toxic invaders, safeguarding their crops and livestock from possible harm.

Botanical Research: Researchers studying the ecology of the Chesapeake Bay Watershed can use the object detection model to conduct large-scale surveys of poisonous plant populations in the region. This data would provide valuable information on the distribution, abundance, and interactions between these toxic species and the surrounding environment.

Environmental Education: Educators can incorporate the object detection model into educational programs to teach students and the public about poisonous plants found in the Chesapeake Bay Watershed. This would raise awareness of these hazardous species, fostering a better understanding of local ecosystems and promoting responsible outdoor behaviors.

Characterized Crowd Instance-level Human Parsing (CCIHP) dataset

CCIHP dataset is devoted to fine-grained description of people in the wild with localized & characterized semantic attributes. It contains 20 attribute classes and 20 characteristic classes split into 3 categories (size, pattern and color). The annotations were made with Pixano, an opensource, smart annotation tool for computer vision applications: https://pixano.cea.fr/

CCIHP dataset provides pixelwise image annotations for:

• human segmentation,
• semantic attribute segmentation and
• semantic attribute characterization.

Dataset description

• Images:
  The image data are the same as CIHP dataset (see Section Related work) proposed at the LIP (Look Into Person) challenge. They are available at google drive and baidu drive. (Baidu link does not need access right).

• Annotations:
  Please download and unzip the CCIHP_icip.zip file. The CCIHP annotations can be found in the Training and Validation sub-folders of CCIHP_icip2021/dataset/ folder. They correspond to, respectively, 28,280 training images and 5,000 validation images. Annotations consist of:

  • Human_ids: person instance labels
  • Instance_ids: attribute instance labels
  • Category_ids: attribute category labels
  • Size_ids: size category labels
  • Pattern_ids: pattern category labels
  • Color_ids: color category labels

  Label meaning for semantic attribute/body parts:

  1. Hat: Hat, helmet, cap, hood, veil, headscarf, part covering the skull and hair of a hood/balaclava, crown...
  2. Hair
  3. Glove
  4. Sunglasses/Glasses: Sunglasses, eyewear, protective glasses...
  5. UpperClothes: T-shirt, shirt, tank top, sweater under a coat, top of a dress...
  6. Face Mask: Protective mask, surgical mask, carnival mask, facial part of a balaclava, visor of a helmet...
  7. Coat: Coat, jacket worn without anything on it, vest with nothing on it, a sweater with nothing on it...
  8. Socks
  9. Pants: Pants, shorts, tights, leggings, swimsuit bottoms... (clothing with 2 legs)
  10. Torso-skin
  11. Scarf: Scarf, bow tie, tie...
  12. Skirt: Skirt, kilt, bottom of a dress...
  13. Face
  14. Left-arm (naked part)
  15. Right-arm (naked part)
  16. Left-leg (naked part)
  17. Right-leg (naked part)
  18. Left-shoe
  19. Right-shoe
  20. Bag: Backpack, shoulder bag, fanny pack... (bag carried on oneself)
  21. Others: Jewelry, tags, bibs, belts, ribbons, pins, head decorations, headphones...

  Label meaning for size characterization:

  1. Short: Small, short, narrow
  2. Long: Long, large, big
  3. Undetermined: If the attribute is partially hidden
  4. Sparse/bald: For hair attribute only

  Label meaning for pattern characterization:

  1. Solid
  2. Geometrical: Stripes, Checks, Dots...
  3. Fancy: Flowers, Military...
  4. Letters: Letters, numbers, symbols...

  Label meaning for color characterization:

  1. Dark: No dominant color, includes black, navy blue
  2. Medium: No dominant color, including gray
  3. Light: No dominant color, including white
  4. Brown
  5. Red
  6. Pink
  7. Yellow
  8. Orange
  9. Green
  10. Blue
  11. Purple
  12. Multicolor: When there is a pattern with several colors

Related work

Our work is based on CIHP image dataset from: Ke Gong, Xiaodan Liang, Yicheng Li, Yimin Chen, Ming Yang and Liang Lin, "Instance-level Human Parsing via Part Grouping Network", ECCV 2018.

Evaluation

To evaluate the predictions given by a Human Parsing with Characteristics model, you can run the python scripts in CCIHP_icip2021/evaluation/ folder.

Requirements

• python==3.6+
• opencv-python
• pillow

Evaluation steps

1. Run generate_characteristic_instance_part_ccihp.py
2. Run eval_test_characteristic_inst_part_ap_ccihp.py for mean Average Precision based on characterized region (AP^(cr)_(vol)). It evaluates the prediction of characteristic (class & score) relative to each instanced and characterized attribute mask, independently of the attribute class prediction.
3. Run metric_ccihp_miou_evaluation.py for a mIoU performance evaluation of semantic predictions (attribute or characteristics).

License

Data annotations are under Creative Commons Attribution Non Commercial 4.0 license (see LICENSE file).

Evaluation codes are under MIT license.

Citation

A. Loesch and R. Audigier, "Describe Me If You Can! Characterized Instance-Level Human Parsing," 2021 IEEE International Conference on Image Processing (ICIP), 2021, pp. 2528-2532, doi: 10.1109/ICIP42928.2021.9506509.

@INPROCEEDINGS{ccihp_dataset_2021,
 author={Loesch, Angelique and Audigier, Romaric},
 booktitle={2021 IEEE International Conference on Image Processing (ICIP)}, 
 title={Describe Me If You Can! Characterized Instance-Level Human Parsing}, 
 year={2021},
 volume={},
 number={},
 pages={2528-2532},
 doi={10.1109/ICIP42928.2021.9506509}},

Contact

If you have any question about this dataset, you can contact us by email at: ccihp-dataset@cea.fr

Overview

The License Plates dataset is a object detection dataset of different vehicles (i.e. cars, vans, etc.) and their respective license plate. Annotations also include examples of "vehicle" and "license-plate". This dataset has a train/validation/test split of 245/70/35 respectively. https://i.imgur.com/JmRgjBq.png" alt="Dataset Example">

Use Cases

This dataset could be used to create a vehicle and license plate detection object detection model. Roboflow provides a great guide on creating a license plate and vehicle object detection model.

Using this Dataset

This dataset is a subset of the Open Images Dataset. The annotations are licensed by Google LLC under CC BY 4.0 license. Some annotations have been combined or removed using Roboflow's annotation management tools to better align the annotations with the purpose of the dataset. The images have a CC BY 2.0 license.

About Roboflow

Roboflow creates tools that make computer vision easy to use for any developer, even if you're not a machine learning expert. You can use it to organize, label, inspect, convert, and export your image datasets. And even to train and deploy computer vision models with no code required. https://i.imgur.com/WHFqYSJ.png" alt="https://roboflow.com">

Background: The composition of tissue types present within a wound is a useful indicator of its healing progression and could be helpful in guiding its treatment. Additionally, this measure is clinically used in wound healing tools (e.g. BWAT) to assess risk and recommend treatment. However, the identification of wound tissue and the estimation of their relative composition is highly subjective and variable. This results in incorrect assessments being reported, leading to downstream impacts including inappropriate dressing selection, failure to identify wounds at risk of not healing, or failure to make appropriate referrals to specialists. Objective: To measure inter-and intra-rater variability in manual tissue segmentation and quantification among a cohort of wound care clinicians. To determine if an objective assessment of tissue types (i.e., size, amount) can be achieved using a deep convolutional neural network that predicts wound tissue types. The proposed objective measurement by machine learning model’s performance is reported in terms of mean intersection over union (mIOU) between model prediction and the ground truth labels. Finally, to compare the performance of the model wound tissue identification by a cohort of wound care clinicians. Methods: A dataset of 58 anonymized wound images of various types of chronic wounds from Swift Medical’s Wound Database was used to conduct the inter-rater and intra-rater agreement study. The dataset was split into 3 subsets, with 50% overlap between subsets to measure intra-rater agreement. Four different tissue types (epithelial, granulation, slough and eschar) within the wound bed were independently labelled by the 5 wound clinicians using a browser-based image annotation tool. Each subset was labelled at one-week intervals. Inter-rater and intra rater agreement was computed. Next, two separate deep convolutional neural networks architectures were developed for wound segmentation and tissue segmentation and are used in sequence in the proposed workflow. These models were trained using 465,187 wound image-label pairs and 17,000 image-label pairs respectively. This is by far the largest and most diverse reported dataset of labelled wound images used for training deep learning models for wound and wound tissue segmentation. This allows our models to be robust, unbiased towards skin tones and generalize well to unseen data. The deep learning model architectures were designed to be fast and nimble to allow them to run in near real-time on mobile devices. Results: We observed considerable variability when a cohort of wound clinicians was tasked to label the different tissue types within the wound using a browser-based image annotation tool. We report poor to moderate inter-rater agreement in identifying tissue types in chronic wound images. A very poor Krippendorff alpha value of 0.014 for inter-rater variability when identifying epithelization has been observed, while granulation is most consistently identified by the clinicians. The intra-rater ICC(3,1) (Intra-Class Correlation) however indicates raters are relatively consistent when labelling the same image multiple times over a period of time. Our deep learning models achieved a mean intersection over union (mIOU) of 0.8644 and 0.7192 for wound and tissue segmentation respectively. A cohort of wound clinicians, by consensus, rated 91% of the tissue segmentation results to be between fair and good in terms of tissue identification and segmentation quality. Conclusions: Our inter-rater agreement study validates that clinicians may exhibit considerable variability when identifying and visually estimating tissue proportion within the wound bed. The proposed deep learning model provides objective tissue identification and measurements to assist clinicians in documenting the wound more accurately. Our solution works on off-the-shelf mobile devices and was trained with the largest and most diverse chronic wound dataset ever reported and leading to a robust model when deployed. The proposed solution brings us a step closer to more accurate wound documentation and may lead to improved healing outcomes when deployed at scale.

Community science image libraries offer a massive, but largely untapped, source of observational data for phenological research. The iNaturalist platform offers a particularly rich archive, containing more than 49 million verifiable, georeferenced, open access images, encompassing seven continents and over 278,000 species. A critical limitation preventing scientists from taking full advantage of this rich data source is labor. Each image must be manually inspected and categorized by phenophase, which is both time-intensive and costly. Consequently, researchers may only be able to use a subset of the total number of images available in the database. While iNaturalist has the potential to yield enough data for high-resolution and spatially extensive studies, it requires more efficient tools for phenological data extraction. A promising solution is automation of the image annotation process using deep learning. Recent innovations in deep learning have made these open-source tools accessible to a general research audience. However, it is unknown whether deep learning tools can accurately and efficiently annotate phenophases in community science images. Here, we train a convolutional neural network (CNN) to annotate images of Alliaria petiolata into distinct phenophases from iNaturalist and compare the performance of the model with non-expert human annotators. We demonstrate that researchers can successfully employ deep learning techniques to extract phenological information from community science images. A CNN classified two-stage phenology (flowering and non-flowering) with 95.9% accuracy and classified four-stage phenology (vegetative, budding, flowering, and fruiting) with 86.4% accuracy. The overall accuracy of the CNN did not differ from humans (p = 0.383), although performance varied across phenophases. We found that a primary challenge of using deep learning for image annotation was not related to the model itself, but instead in the quality of the community science images. Up to 4% of A. petiolata images in iNaturalist were taken from an improper distance, were physically manipulated, or were digitally altered, which limited both human and machine annotators in accurately classifying phenology. Thus, we provide a list of photography guidelines that could be included in community science platforms to inform community scientists in the best practices for creating images that facilitate phenological analysis.

The Website Screenshots dataset is a synthetically generated dataset composed of screenshots from over 1000 of the world's top websites. They have been automatically annotated to label the following classes: :fa-spacer: * button - navigation links, tabs, etc. * heading - text that was enclosed in <h1> to <h6> tags. * link - inline, textual <a> tags. * label - text labeling form fields. * text - all other text. * image - <img>, <svg>, or <video> tags, and icons. * iframe - ads and 3rd party content.

Example

This is an example image and annotation from the dataset: https://i.imgur.com/mOG3u3Z.png" alt="WIkipedia Screenshot">

Usage

Annotated screenshots are very useful in Robotic Process Automation. But they can be expensive to label. This dataset would cost over $4000 for humans to label on popular labeling services. We hope this dataset provides a good starting point for your project. Try it with a model from our model library.

The dataset contains 1689 train data, 243 test data and 483 valid data.

Links to code and bioRxiv pre-print: 1. Multi-lens Neural Machine (MLNM) Code 2. An AI-assisted Tool For Efficient Prostate Cancer Diagnosis (bioRxiv Pre-print) Digitized hematoxylin and eosin (H&E)-stained whole-slide-images (WSIs) of 40 prostatectomy and 59 core needle biopsy specimens were collected from 99 prostate cancer patients at Tan Tock Seng Hospital, Singapore. There were 99 WSIs in total such that each specimen had one WSI. H&E-stained slides were scanned at 40× magnification (specimen-level pixel size 0·25μm × 0·25μm) using Aperio AT2 Slide Scanner (Leica Biosystems). Institutional board review from the hospital were obtained for this study, and all the data were de-identified. Prostate glandular structures in core needle biopsy slides were manually annotated and classified using the ASAP annotation tool (ASAP). A senior pathologist reviewed 10% of the annotations in each slide, ensuring that some reference annotations were provided to the researcher at different regions of the core. It is to be noted that partial glands appearing at the edges of the biopsy cores were not annotated. Patches of size 512 × 512 pixels were cropped from whole slide images at resolutions 5×, 10×, 20×, and 40× with an annotated gland centered at each patch. This dataset contains these cropped images. This dataset is used to train two AI models for Gland Segmentation (99 patients) and Gland Classification (46 patients). Tables 1 and 2 illustrate both gland segmentation and gland classification datasets. We have put the two corresponding sub-datasets as two zip files as follows: gland_segmentation_dataset.zip gland_classification_dataset.zip Table 1: The number of slides and patches in training, validation, and test sets for gland segmentation task. There is one H&E stained WSI for each prostatectomy or core needle biopsy specimen. #Slides Train Valid Test Total Prostatectomy 17 8 15 40 Biopsy 26 13 20 59 Total 43 21 35 99 #Patches Train Valid Test Total Prostatectomy 7795 3753 7224 18772 Biopsy 5559 4028 5981 15568 Total 13354 7781 13205 34340 Table 2: The number of slides and patches in training, validation, and test sets for gland classification task. There is one H&E stained WSI for each prostatectomy or core needle biopsy specimen. The gland classification datasets are the subsets of the gland segmentation datasets. GS: Gleason Score. B: Benign. M: Malignant. #Slides (GS 3+3:3+4:4+3) Train Valid Test Total Biopsy 10:9:1 3:7:0 6:10:0 19:26:1 #Patches (B:M) Train Valid Test Total Biopsy 1557:2277 1216:1341 1543:2718 4316:6336 NB: Gland classification folder (gland_classification_dataset.zip) may contain extra patches, labels of which could not be identified from H&E slides. They were not used in the machine learning study. This study was funded by the Biomedical Research Council of the Agency for Science, Technology and Research, Singapore.