The open-source data labeling tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in machine learning and artificial intelligence applications. The market's expansion is fueled by several key factors: the rising adoption of AI across various industries, the need for cost-effective data annotation solutions, and the growing preference for flexible and customizable tools. While precise market sizing data is unavailable, considering the substantial growth in the broader data annotation market and the increasing popularity of open-source solutions, we can reasonably estimate the 2025 market size to be approximately $500 million. This signifies a significant opportunity for providers of open-source tools, particularly those offering innovative features and strong community support. Assuming a conservative Compound Annual Growth Rate (CAGR) of 25% for the forecast period (2025-2033), the market is projected to reach approximately $4.8 billion by 2033. This growth trajectory is supported by the continuous advancements in AI and the ever-increasing volume of data requiring labeling. Several challenges restrain market growth, including the need for specialized technical expertise to effectively implement and manage open-source tools, and the potential for inconsistencies in data quality compared to commercial solutions. However, the inherent advantages of open-source tools—cost-effectiveness, customization, and community-driven improvements—are expected to outweigh these challenges. The increasing availability of user-friendly interfaces and pre-trained models is further enhancing the accessibility and appeal of open-source solutions. The market segmentation encompasses various tool types based on functionality and applications (image annotation, text annotation, video annotation etc.), deployment models (cloud-based, on-premise), and target industries (healthcare, automotive, finance etc.). Leading players are continuously enhancing their offerings, fostering community engagement, and expanding their service portfolios to capitalize on this expanding market.

The open-source 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's expansion is fueled by several key factors: the rising adoption of AI across various industries (including automotive, healthcare, and finance), the need for efficient and cost-effective data annotation solutions, and a growing preference for flexible, customizable tools offered by open-source platforms. While cloud-based solutions currently dominate the market due to scalability and accessibility, on-premise deployments remain significant for organizations with stringent data security requirements. The competitive landscape is dynamic, with numerous established players and emerging startups vying for market share. The market is segmented geographically, with North America and Europe currently holding the largest shares due to early adoption of AI technologies and robust research & development activities. However, the Asia-Pacific region is projected to witness significant growth in the coming years, driven by increasing investments in AI infrastructure and talent development. Challenges remain, such as the need for skilled annotators and the ongoing evolution of annotation techniques to handle increasingly complex data types. The forecast period (2025-2033) suggests continued expansion, with a projected Compound Annual Growth Rate (CAGR) – let's conservatively estimate this at 15% based on typical growth in related software sectors. This growth will be influenced by advancements in automation and semi-automated annotation tools, as well as the emergence of novel annotation paradigms. The market is expected to see further consolidation, with larger players potentially acquiring smaller, specialized companies. The increasing focus on data privacy and security will necessitate the development of more robust and compliant open-source annotation tools. Specific application segments like healthcare, with its stringent regulatory landscape, and the automotive industry, with its reliance on autonomous driving technology, will continue to be major drivers of market growth. The increasing availability of open-source datasets and pre-trained models will indirectly contribute to the market’s expansion by lowering the barrier to entry for AI development.

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

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 data annotation and labeling tools market is experiencing robust growth, driven by the escalating demand for high-quality training data in the burgeoning fields of artificial intelligence (AI) and machine learning (ML). The market's expansion is fueled by the increasing adoption of AI across diverse sectors, including autonomous vehicles, healthcare, and finance. These industries require vast amounts of accurately labeled data to train their AI models, leading to a significant surge in the demand for efficient and scalable annotation tools. While precise market sizing for 2025 is unavailable, considering a conservative estimate and assuming a CAGR of 25% (a reasonable figure given industry growth), we can project a market value exceeding $2 billion in 2025, rising significantly over the forecast period (2025-2033). Key trends include the growing adoption of cloud-based solutions, increased automation in the annotation process through AI-assisted tools, and a heightened focus on data privacy and security. The rise of synthetic data generation is also beginning to impact the market, offering potential cost savings and improved data diversity. However, challenges remain. The high cost of skilled annotators, the need for continuous quality control, and the inherent complexities of labeling diverse data types (images, text, audio, video) pose significant restraints on market growth. While leading players like Labelbox, Scale AI, and SuperAnnotate dominate the market with advanced features and robust scalability, smaller companies and open-source tools continue to compete, often focusing on niche applications or offering cost-effective alternatives. The competitive landscape is dynamic, with continuous innovation and mergers and acquisitions shaping the future of this rapidly evolving market. Regional variations in adoption are also expected, with North America and Europe likely leading the market, followed by Asia-Pacific and other regions. This continuous evolution necessitates careful strategic planning and adaptation for businesses operating in or considering entry into this space.

The global image annotation service market is projected to grow from USD 1,126.0 million in 2025 to USD 2,839.6 million by 2033, at a CAGR of 12.1% during the forecast period. The growth of the market is attributed to the increasing demand for image annotation services from various end-use industries, such as computer vision, artificial intelligence, and machine learning. The key drivers of the market include the rising need for data annotation for training machine learning models, the increasing adoption of AI and ML technologies across various industries, and the growing demand for image annotation services from e-commerce and social media companies. However, the market growth is restrained by the high cost of image annotation services and the availability of open-source image annotation tools. The market is segmented based on application, type, and region. The computer vision segment is expected to hold the largest share of the market during the forecast period, owing to the increasing demand for image annotation services for training computer vision models. The image classification segment is expected to dominate the market during the forecast period, owing to the wide range of applications of image classification in various industries. The North America region is expected to hold the largest share of the market during the forecast period, owing to the presence of a large number of technology companies in the region. Executive Summary The global image annotation service market is projected to grow to $350 million by 2030, driven by the increasing demand for computer vision and artificial intelligence applications.

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.

We provide annotated fish imagery data for use in deep learning models (e.g., convolutional neural networks) for individual and species recognition. For individual recognition models, the dataset consists of annotated .json files of individual brook trout imagery collected at the Eastern Ecological Science Center's Experimental Stream Laboratory. For species recognition models, the dataset consists of annotated .json files for 7 freshwater fish species: lake trout, largemouth bass, smallmouth bass, brook trout, rainbow trout, walleye, and northern pike. Species imagery was compiled from Anglers Atlas and modified to remove human faces for privacy protection. We used open-source VGG image annotation software developed by Oxford University: https://res1wwwd-o-trobotsd-o-toxd-o-tacd-o-tuk.vcapture.xyz/~vgg/software/via/via-1.0.6.html.

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

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 accessibl...

The Fetal Tissue Annotation Dataset (FeTA) consist of manually annotated, T2-weighted, super-resolution reconstructed fetal cerebral magnetic resonance images. It is a mixture of normally developing cases and pathologies. The dataset is a valuable source for developing automated image segmentation algorithms as it provides open source MRI data and expert manual annotations, which is a particularly time consuming process. Each fetal brains were labeled for 7 tissue categories: grey matter, white matter, external CSF spaces, ventricle system, deep gray matter, cerebellum and brainstem.

From May 2021, access to the FeTA dataset is only possible on the Synapse platform. We released the second version with 80 cases, which must be used for participants of the MICCAI Fetal Tissue Annotation Challenge in 2021. Please visit the following sites for further information:

https://feta-2021.grand-challenge.org/

https://www.synapse.org/#!Synapse:syn25649159/wiki/610007

Background

Congenital disorders are one of the leading causes of infant mortality worldwide. Recently, fetal MRI has started to emerge as a valuable tool for investigating the neurological development of fetuses with congenital disorders in order to aid in prenatal planning. Moreover, fetal MRI is a powerful tool to portray the complex neurodevelopmental events during human gestation, which remain to be completely characterized. Automated segmentation and quantification of the highly complex and rapidly changing brain morphology in MRI data would improve the diagnostic process, as manual segmentation is both time consuming and prone to human error and inter-rater variability. The automatic segmentation of the developing human brain would be a first step in being able to perform such an analysis. The FeTA Dataset and the Challenge we plan to organize are important steps in the development of reproducible methods of analyzing high resolution MR images of the developing fetal brain. Such new algorithms will have the potential to better understand the underlying causes of congenital disorders and ultimately to support decision-making and prenatal planning.

Automated vs. manual annotation.

Project Overview:

Problem Statement: Addressing traffic accidents and fatalities by enforcing helmet regulations among motorcyclists.

Objective: Develop a deep learning-based system for helmet detection and tracking using YOLO algorithms.

Dataset Collection and Preparation:

Data Sources: Traffic images from open-source website pexels , Kaggle, and Roboflow.

Dataset Diversity and Quality Testing: Rigorous feasibility testing, evaluating completeness, balance, annotation quality, and data size.

Dataset Characteristics: Contains diverse scenarios with varying lighting conditions, perspectives, and backgrounds.

Annotation: Classified images into three categories: With Helmet, Without Helmet, and Licence Plate, ensuring precise object localization. These classes serve as the core labels for the object detection and tracking system, enabling the YOLO model to discern and identify these critical elements in traffic images.

Annotation Tool: Roboflow software used for processing and annotating images.

Contributers: Wasule S.V and Khadatkar G.W

We have developed an open-source software called BIRDS (bi-channel image registration and deep-learning segmentation) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image pre-processing, bi-channel registration, automatic annotation, creation of a 3D digital frame, high-resolution visualization, and expandable quantitative analysis. This new bi-channel registration algorithm is adaptive to various types of whole-brain data from different microscopy platforms and shows dramatically improved registration accuracy. Additionally, as this platform combines registration with neural networks, its improved function relative to other platforms lies in the fact that the registration procedure can readily provide training data for network construction, while the trained neural network can efficiently segment incomplete/defective brain data that is otherwise difficult to register. Our software is thus optimized to enable either mi...

There are two group of 1×1×10 μm high-resolution mouse brain datasets using serial two-photon tomography (STPT), and there are coronal sections.

Informal street vending is a significant aspect of the informal economy and is vital for understanding urban environments. While extensively studied in disciplines like anthropology, economics, and sociology, the global number of street vending remains unclear. Understanding this phenomenon is crucial for analyzing social and economic indicators. However, traditional methods for studying street vending, such as interviews and observational techniques, are often costly and labor-intensive, limiting their scalability and frequency.

To address these challenges, we present the Steet-level Imagery Dataset for detecting Informal Vendors in Urban Environment. This dataset was created using video footage captured by a GoPro action camera mounted on a motorcycle handlebar, producing side-looking images at two-second intervals. The final dataset consists of 2,794 annotated images. To comply with GDPR privacy guidelines, pedestrian faces, and vehicle license plates were anonymized using an open-source Python pipeline powered by the YOLO object detection algorithm.

Annotations were created using the LabelImg tool, where street vendors were identified and labeled with bounding boxes. Each annotation specifies one of three vendor types: "fixed-stall-vendor," "semi-fixed-vendor," and "itinerant-vendor," enabling granular analysis. All annotations are stored in YOLO format for seamless integration into machine learning workflows.

To improve model generalization and address class imbalance, data augmentation techniques were applied. These include geometric transformations such as rotation, flipping, scaling, and shearing, along with spectral adjustments like brightness, contrast, hue changes, blur, and CLAHE (Contrast Limited Adaptive Histogram Equalization).

This dataset is valuable for researchers aiming to develop machine-learning models to detect and analyze informal economic activities. By facilitating scalable and efficient analysis, it supports advancements in urban studies and related fields.