The open-source data labeling tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in various AI applications. The market's expansion is fueled by several key factors: the rising adoption of machine learning and deep learning algorithms across industries, the need for efficient and cost-effective data annotation solutions, and a growing preference for customizable and flexible tools that can adapt to diverse data types and project requirements. While proprietary solutions exist, the open-source ecosystem offers advantages including community support, transparency, cost-effectiveness, and the ability to tailor tools to specific needs, fostering innovation and accessibility. The market is segmented by tool type (image, text, video, audio), deployment model (cloud, on-premise), and industry (automotive, healthcare, finance). We project a market size of approximately $500 million in 2025, with a compound annual growth rate (CAGR) of 25% from 2025 to 2033, reaching approximately $2.7 billion by 2033. This growth is tempered by challenges such as the complexities associated with data security, the need for skilled personnel to manage and use these tools effectively, and the inherent limitations of certain open-source solutions compared to their commercial counterparts. Despite these restraints, the open-source model's inherent flexibility and cost advantages will continue to attract a significant user base. The market's competitive landscape includes established players like Alecion and Appen, alongside numerous smaller companies and open-source communities actively contributing to the development and improvement of these tools. Geographical expansion is expected across North America, Europe, and Asia-Pacific, with the latter projected to witness significant growth due to the increasing adoption of AI and machine learning in developing economies. Future market trends point towards increased integration of automated labeling techniques within open-source tools, enhanced collaborative features to improve efficiency, and further specialization to cater to specific data types and industry-specific requirements. Continuous innovation and community contributions will remain crucial drivers of growth in this dynamic market segment.

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

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.

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

Introduction:

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🌟 XimageNet-12 🌟

An Explainable Visual Benchmark Dataset for Robustness Evaluation. A Dataset for Image Background Exploration!

Blur Background, Segmented Background, AI-generated Background, Bias of Tools During Annotation, Color in Background, Random Background with Real Environment

+⭐ Follow Authors for project updates.

Website: XimageNet-12

Here, we trying to understand how image background effect the Computer Vision ML model, on topics such as Detection and Classification, based on baseline Li et.al work on ICLR 2022: Explainable AI: Object Recognition With Help From Background, we are now trying to enlarge the dataset, and analysis the following topics: Blur Background / Segmented Background / AI generated Background/ Bias of tools during annotation/ Color in Background / Dependent Factor in Background/ LatenSpace Distance of Foreground/ Random Background with Real Environment! Ultimately, we also define the math equation of Robustness Scores! So if you feel interested How would we make it or join this research project? please feel free to collaborate with us!

In this paper, we propose an explainable visual dataset, XIMAGENET-12, to evaluate the robustness of visual models. XIMAGENET-12 consists of over 200K images with 15,410 manual semantic annotations. Specifically, we deliberately selected 12 categories from ImageNet, representing objects commonly encountered in practical life. To simulate real-world situations, we incorporated six diverse scenarios, such as overexposure, blurring, and color changes, etc. We further develop a quantitative criterion for robustness assessment, allowing for a nuanced understanding of how visual models perform under varying conditions, notably in relation to the background.

Progress:

• Blur Background-> Done! You can find the image Generated in the corresponding folder!
• Segmented Background -> Done! you can download the image and its corresponding transparent mask image!
• Color in Background->Done!~~ you can now download the image with different background color modified, and play with different color-ed images!
• Random Background with Real Environment -> Done! you can also find we generated the image with the photographer's real image as a background and removed the original background of the target object, but similar to the style!
• Bias of tools during annotation->Done! for this one, you won't get a new image, because this is about math and statistics data analysis when different tools and annotators are applied!
• AI generated Background-> current on progress ( 12 /12) Done!, So basically you can find one sample folder image we uploaded, please take a look at how real it is, and guess what LLM model we are using to generate the high-resolution background to make it so real :)

What tool we used to generate those images?

We employed a combination of tools and methodologies to generate the images in this dataset, ensuring both efficiency and quality in the annotation and synthesis processes.

• IoG Net: Initially, we utilized the IoG Net, which played a foundational role in our image generation pipeline.
• Polygon Faster Labeling Tool: To facilitate the annotation process, we developed a custom Polygon Faster Labeling Tool, streamlining the labeling of objects within the images.AnyLabeling Open-source Project: We also experimented with the AnyLabeling open-source project, exploring its potential for our annotation needs.
• V7 Lab Tool: Eventually, we found that the V7 Lab Tool provided the most efficient labeling speed and delivered high-quality annotations. As a result, we standardized the annotation process using this tool.
• Data Augmentation: For the synthesis of synthetic images, we relied on a combination of deep learning frameworks, including scikit-learn and OpenCV. These tools allowed us to augment and manipulate images effectively to create a diverse range of backgrounds and variations.
• GenAI: Our dataset includes images generated using the Stable Diffusion XL model, along with versions 1.5 and 2.0 of the Stable Diffusion model. These generative models played a pivotal role in crafting realistic and varied backgrounds.

For a detailed breakdown of our prompt engineering and hyperparameters, we invite you to consult our upcoming paper. This publication will provide comprehensive insights into our methodologies, enabling a deeper understanding of the image generation process.

How to use our dataset?

this dataset has been/could be downloaded via Kaggl...

Data Annotation Tools Market Outlook

According to our latest research, the global Data Annotation Tools market size reached USD 2.1 billion in 2024. The market is set to expand at a robust CAGR of 26.7% from 2025 to 2033, projecting a remarkable value of USD 18.1 billion by 2033. The primary growth driver for this market is the escalating adoption of artificial intelligence (AI) and machine learning (ML) across various industries, which necessitates high-quality labeled data for model training and validation.

One of the most significant growth factors propelling the data annotation tools market is the exponential rise in AI-powered applications across sectors such as healthcare, automotive, retail, and BFSI. As organizations increasingly integrate AI and ML into their core operations, the demand for accurately annotated data has surged. Data annotation tools play a crucial role in transforming raw, unstructured data into structured, labeled datasets that can be efficiently used to train sophisticated algorithms. The proliferation of deep learning and natural language processing technologies further amplifies the need for comprehensive data labeling solutions. This trend is particularly evident in industries like healthcare, where annotated medical images are vital for diagnostic algorithms, and in automotive, where labeled sensor data supports the evolution of autonomous vehicles.

Another prominent driver is the shift toward automation and digital transformation, which has accelerated the deployment of data annotation tools. Enterprises are increasingly adopting automated and semi-automated annotation platforms to enhance productivity, reduce manual errors, and streamline the data preparation process. The emergence of cloud-based annotation solutions has also contributed to market growth by enabling remote collaboration, scalability, and integration with advanced AI development pipelines. Furthermore, the growing complexity and variety of data types, including text, audio, image, and video, necessitate versatile annotation tools capable of handling multimodal datasets, thus broadening the market's scope and applications.

The market is also benefiting from a surge in government and private investments aimed at fostering AI innovation and digital infrastructure. Several governments across North America, Europe, and Asia Pacific have launched initiatives and funding programs to support AI research and development, including the creation of high-quality, annotated datasets. These efforts are complemented by strategic partnerships between technology vendors, research institutions, and enterprises, which are collectively advancing the capabilities of data annotation tools. As regulatory standards for data privacy and security become more stringent, there is an increasing emphasis on secure, compliant annotation solutions, further driving innovation and market demand.

From a regional perspective, North America currently dominates the data annotation tools market, driven by the presence of major technology companies, well-established AI research ecosystems, and significant investments in digital transformation. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid industrialization, expanding IT infrastructure, and a burgeoning startup ecosystem focused on AI and data science. Europe also holds a substantial market share, supported by robust regulatory frameworks and active participation in AI research. Latin America and the Middle East & Africa are gradually catching up, with increasing adoption in sectors such as retail, automotive, and government. The global landscape is characterized by dynamic regional trends, with each market contributing uniquely to the overall growth trajectory.

Component Analysis

The data annotation tools market is segmented by component into software and services, each playing a pivotal role in the market's overall ecosystem. Software solutions form the backbone of the market, providing the technical infrastructure for auto

The zip file here contains 1,179 pairs of human-generated segmentation labels and images from Emergency Response Imagery collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. A total of 1,054 unique images were labeled. 946 images were annotated by a single labeler. 95 images were annotated by two labelers. 11 images were annotated by three labelers. 2 images were annotated by five labelers. All authors contributed to labeling, and all labeling was done with an open-source labeling tool (Buscombe et al., 2022). All pixels in each image are labeled with one of four classes: 0 (water), 1 (bare sand), 2 (vegetation - both sparse and dense), 4 (the built environment - buildings, roads, parking lots, boats, etc.) The csv file provided here is a list of each image file name (which includes the anonymized labeler ID), the name of the image without the labeler ID, the name of the corresponding NOAA jpg, the NOAA flight name, the storm name, the latitude and longitude of the image, and a column stating if the image has been labeled multiple times. Images labeled here correspond to multiple NOAA flights — all listed in the csv file for each jpeg image. These jpeg images can be downloaded directly from NOAA (https://storms.ngs.noaa.gov/) or using Moretz et al. (2020a, 2020b). The images included in this data release correspond to original NOAA images that have been resized and then split into quadrants (using ImageMagick). The naming convention corresponds to the image quarter — the *-0.jpg is upper left, *-1.jpg is upper right, *-2.jpg is lower left, and *-3.jpg is the lower right.

About: This dataset contains annotated images of apple leaves with different diseases from the PlantVillage dataset, which can be used for image segmentation-based research. The Mask RCNN annotation style is used. This dataset will aid in accurately locating the damage on the leaves, which is necessary for predicting the severity of the disease.

Black Rot, Apple Scab, and Cedar Apple Rust are the three diseases that have been annotated. This annotated dataset can be used to find diseases on apple leaves by using image segmentation.

The data was annotated using MakeSense.AI, an open-source image annotation application. Annotating the damage on the leaves was done with the "Rect" tool. Annotation files are saved in.xml format with the two diagonally positioned corners of the bounding boxes. Various colors are used for various classes when labeling.

A total of 850 images have been annotated. 680 images are kept in the train folder, and 170 images are in the valid folder. The folder structure is as follows:

annotated_apple_leaf_disease | |--train|--annots | |--images | |--valid|--annots | |--images

If you are using this dataset or found any of this information contributing towards your research, please cite:

1. @InProceedings{MitraMK_MaskRCNN_2022, author="Mitra, Alakananda and Mohanty, Saraju P. and Kougianos, Elias", title={{A Smart Agriculture Framework to Automatically Track the Spread of Plant Diseases Using Mask Region-Based Convolutional Neural Network}}, booktitle="Proceedings of the 5th IFIP International Internet of Things Conference (IFIP-IoT)", year="2022", pages="68--85", doi={10.1007/978-3-031-18872-5_5} }

2. @InProceedings{MitraMK_aGROdet_2022, author="Mitra, Alakananda and Mohanty, Saraju P. and Kougianos, Elias", title={{aGROdet: A Novel Framework for Plant Disease Detection and Leaf Damage Estimation}}, booktitle="Proceedings of the 5th IFIP International Internet of Things Conference (IFIP-IoT)", year="2022", pages="3--22", doi={10.1007/978-3-031-18872-5_1} }

3. Original PlantVillage Dataset: @article{DBLP:journals/corr/HughesS15, Author = {David P. Hughes and Marcel Salath{'{e} } }, Title = {An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowdsourcing}, journal = {CoRR}, volume = {abs/1511.08060}, year = {2015},
   url = {http://arxiv.org/abs/1511.08060}, archivePrefix = {arXiv}, eprint = {1511.08060}, timestamp = {Mon, 13 Aug 2018 16:48:21 +0200}, biburl = {https://dblp.org/rec/bib/journals/corr/HughesS15}, bibsource = {dblp computer science bibliography, https://dblp.org} }

The csv files contain human-generated labels for Emergency Response Imagery collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. All authors contributed to labeling the imagery. All labeling was done with an open-source labeling tool (Rafique et al., 2020).

All csv files provide the userID (the ID of the anonymous labeler), the NOAA flight, the NOAA image, and 6 labels — allWater (if the image was all water), devType (if the image had buildings/development), washoverType (if the image had washover deposits), dmgType (if the image showed damage to built environment), impactType (if the labeler could identify the coastal impact, using the Storm Impact Scale from Sallenger, 2000), and terrainType (the type of physical environment).

Images labeled here correspond to multiple NOAA flights — all listed in the csv file for each jpeg image. These jpeg images can be downloaded directly from NOAA (https://storms.ngs.noaa.gov/) or using Moretz et al. (2020a, 2020b).

There are three csv files:

ReleaseData_10172022.csv has 10,237 labels for 4250 images. These labels were generated by coastal scientists. The csv also contains the Latitude and Longitude of the image center (from NOAA).

ReleaseDataQuads.csv has 400 labels for 100 images. These labels were generated by coastal scientists. The images labeled in this set correspond to original NOAA images that have been split into quadrants. Splitting images was done with ImageMagick. The command used to split the images was:

magick mogrify -crop 2x2@ +repage -path ../quadrants *.jpg

The naming convention corresponds to the image quarter — the *-0.jpg is upper left, *-1.jpg is upper right, *-2.jpg is lower left, and *-3.jpg is the lower right.

ReleaseDataNCE.csv has 400 labels for 100 images. These images were labeled by non-coastal scientists. Note that the 100 images were also labeled by coastal scientists — those labels can be found in ReleaseData_v3.csv.

There is another companion dataset to this, with slightly different labels (Goldstein et al., 2020).

A zip file of images is also provided for demonstration purposes (images.zip). These are resized copies made with imagemagick, with the longest dimension set at 2000 pixels ( mogrify -resize 2000x2000). For full size images, please download the jpegs directly from NOAA.

Connectomics is fundamental in propelling our understanding of the nervous system's organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from four different animals and five datasets, amounting to around 180 h of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of four pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/. With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.

The csv files contain human-generated labels for Emergency Response Imagery collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Isaias, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. All authors contributed to labeling the imagery. All labeling was done with an open-source labeling tool (Rafique et al., 2020). All csv files provide the userID (the ID of the anonymous labeler), the NOAA flight, the NOAA image, and 6 labels — allWater (if the image was all water), devType (if the image had buildings/development), washoverType (if the image had washover deposits), dmgType (if the image showed damage to built environment), impactType (if the labeler could identify the coastal impact, using the Storm Impact Scale from Sallenger, 2000), and terrainType (the type of physical environment). Images labeled here correspond to multiple NOAA flights — all listed in the csv file for each jpeg image. These jpeg images can be downloaded directly from NOAA (https://storms.ngs.noaa.gov/) or using Moretz et al. (2020a, 2020b). There are three csv files: ReleaseData_10172022.csv has 10,237 labels for 4250 images. These labels were generated by coastal scientists. The csv also contains the Latitude and Longitude of the image center (from NOAA). ReleaseDataQuads.csv has 400 labels for 100 images. These labels were generated by coastal scientists. The images labeled in this set correspond to original NOAA images that have been split into quadrants. Splitting images was done with ImageMagick. The command used to split the images was: magickmogrify−crop2x2∘+repa≥−path.. rants⋅.jpgmagickmogrify-crop2x2∘+repa≥-path.. rants⋅.jpg The naming convention corresponds to the image quarter — the *-0.jpg is upper left, *-1.jpg is upper right, *-2.jpg is lower left, and *-3.jpg is the lower right. ReleaseDataNCE.csv has 400 labels for 100 images. These images were labeled by non-coastal scientists. Note that the 100 images were also labeled by coastal scientists — those labels can be found in ReleaseData_v3.csv. There is another companion dataset to this, with slightly different labels (Goldstein et al., 2020). A zip file of images is also provided for demonstration purposes (images.zip). These are resized copies made with imagemagick, with the longest dimension set at 2000 pixels ( mogrify−resize2000x2000mogrify-resize2000x2000). For full size images, please download the jpegs directly from NOAA.

Global Data Annotation Tools Market size at US$ 102.38 Billion in 2023, set to reach US$ 908.57 Billion by 2032 at a CAGR of 24.4% from 2024 to 2032.

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.

AI Playground (AIP) is an open-source, Unreal Engine-based tool for generating and labeling virtual image data. With AIP, it is trivial to capture the same image under different conditions (e.g., fidelity, lighting, etc.) and with different ground truths (e.g., depth or surface normal values). AIP is easily extendable and can be used with or without code.

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">

The semantic segmentation of clothes is a challenging task due to the wide variety of clothing styles, layers and shapes. The UTFPR-SBD3 contains 4,500 images manually annotated at pixel level in 18 classes plus background. To ensure the high quality of the dataset, all images were manually annotated at the pixel level using JS Segment Annotator, 2 a free web-based image annotation tool. The raw images were carefully selected to avoid, as far as possible, classes with low number of instances.

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

These images are for testing with the self-supervised machine learning demo code posted to GitHub. The code is written in Matlab and the main .m files for which these are inputs are SSL_Demo_2; SSL_Declumping_2. https://github.com/Raphael-4540/Self-Supervised-ML The title and abstract for this work are as follows: Self-Supervised Machine Learning for Live Cell Imagery Segmentation Machine learning algorithms hold the promise of greatly improving live cell image analysis by way of (1) analyzing far more imagery than can be achieved by more traditional manual approaches and (2) by eliminating the subjective nature of researchers and diagnosticians selecting the cells or cell features to be included in the analyzed data set. Currently, however, even the most sophisticated algorithms require time-consuming user supervision at the front end, such that the subjectivity problem is not removed but rather incorporated into the algorithm’s initial training steps and then repeatedly applied to the imagery. To address this roadblock, we have developed a self-supervised machine learning (SSL) algorithm that recursively trains itself directly from the live cell imagery data, thus providing objective segmentation and quantification without the need for user configuration. The approach incorporates an optical flow algorithm component to self-label cell and background pixels for training, followed by the extraction of additional feature vectors for the automated generation of a cell/background classification model. Because it is self-trained, the software has no user-adjustable parameters and does not require curated training imagery. The algorithm was applied to automatically segment cells from their background for a variety of cell types and five commonly used imaging modalities - fluorescence, phase contrast, differential interference contrast (DIC), transmitted light and interference reflection microscopy (IRM). The approach is broadly applicable in that it enables completely automated cell segmentation for long-term live cell phenotyping applications, regardless of the input imagery’s optical modality, magnification or cell type – an important step towards accessible and configuration-free cell image analysis tools. {"references": ["Robitaille, Michael et al; Robust Optical Flow Algorithm for General, Label-free Cell Segmentation; doi: https://doi.org/10.1101/2020.10.26.355958", "Robitaille, Michael, et al; A Self-Supervised Machine Learning Approach for Objective Live Cell Segmentation and Analysis; doi: https://doi.org/10.1101/2021.01.07.425773"]} The Figure 4 Time Series zip file contains 19 tiffs captured from a 2050 x 2050 CMOS camera and so the run time is much longer than for the other files. On a laptop running SSL_Demo_2 (Windows 10) the folder to 11 minutes to process