Snapshot img

US Deep Learning Market Size 2024-2028

The US deep learning market size is forecast to increase by USD 3.55 billion at a CAGR of 27.17% between 2023 and 2028. The market is experiencing significant growth due to several key drivers. Firstly, the increasing demand for industry-specific solutions is fueling market expansion. Additionally, the high data requirements for deep learning applications are leading to increased data generation and collection. Cloud analytics is another significant trend, as companies seek to leverage cloud computing for cost savings and scalability. However, challenges persist, including the escalating cyberattack rate and the need for strong customer data security. Education institutes are also investing in deep learning research and development to prepare the workforce for the future. Overall, the market is poised for continued growth, driven by these factors and the potential for innovation and advancement in various sectors.

Request Free Sample

Deep learning, a subset of artificial intelligence (AI), is a machine learning technique that uses neural networks to model and solve complex problems. This technology is gaining significant traction in various industries across the US, driven by the availability of large datasets and advancements in cloud-based technology. One of the primary areas where deep learning is making a mark is in data centers. Deep learning algorithms are being used to analyze vast amounts of data, enabling businesses to gain valuable insights and make informed decisions. Cloud-based technology is facilitating the deployment of deep learning models at scale, making it an attractive solution for businesses looking to leverage their data.

Furthermore, the market is rapidly evolving, driven by innovations in cloud-based technology, neural networks, and big-data analytics. The integration of machine vision technology and image and visual recognition has driven advancements in industries such as self driving vehicles, digital marketing, and virtual assistance. Companies are leveraging generative adversarial networks (GANs) for cutting-edge news accumulation and content generation. Additionally, machine vision is transforming sectors like retail and manufacturing by enhancing automation and human behavior analysis. With the use of human brain cells generated information, researchers are pushing the boundaries of artificial intelligence. The growing importance of photos and visual data in decision-making further accelerates the market, highlighting the potential of deep learning technologies.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Image recognition
  Voice recognition
  Video surveillance and diagnostics
  Data mining


Type

  Software
  Services
  Hardware


End-user

  Security
  Automotive
  Healthcare
  Retail and commerce
  Others


Geography

  US

By Application Insights

The Image recognition segment is estimated to witness significant growth during the forecast period. Deep learning, a subset of artificial intelligence (AI), is revolutionizing various industries in the US through its ability to analyze and interpret complex data. One of its key applications is image recognition, which utilizes neural networks and graphics processing units (GPUs) to identify objects or patterns within images and videos. This technology is increasingly being adopted in data centers and cloud-based solutions for applications such as visual search, product recommendations, and inventory management. In the automotive sector, image recognition is integral to advanced driver assistance systems (ADAS) and autonomous vehicles, enabling the identification of pedestrians, other vehicles, road signs, and lane markings.

Additionally, image recognition is essential for cybersecurity applications, industrial automation, Internet of Things (IoT) devices, and robots, enhancing their functionality and efficiency. Image recognition is transforming industries by providing accurate and real-time insights from visual data, ultimately improving user experience and productivity.

Get a glance at the market share of various segments Request Free Sample

The Image recognition segment was valued at USD 265.10 billion in 2017 and showed a gradual increase during the forecast period.

Our market researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

Market Driver

Industry-specific solutions is the key driver of the market. Deep learning has become a pivotal technology in addressing classification tasks across numerous industrie

Description:

👉 Download the dataset here

This dataset consists of a diverse collection of images, tailored specifically for the task of Animal Image Classification Dataset in the domain of animal species. It contains 15 distinct folders, each corresponding to a unique animal class, with each folder representing the name of the animal species. The dataset is composed of a variety of images that have been preprocessed and prepared for use in machine learning applications.

Dataset Details:

Image Size: Each image in the dataset has been resized to dimensions of 224x224 pixels with 3 color channels (RGB), making them suitable for immediate use in neural networks.

Data Source: Images were sourced from publicly available databases on the web. They encompass various environments, lighting conditions, and angles, ensuring a rich and diverse representation of each animal class.

Classes: The dataset includes 15 animal classes such as cats, dogs, birds, elephants, lions, and more, with each class represented by images stored in its respective folder.

Download Dataset

Preprocessing and Augmentation:

The dataset underwent extensive preprocessing using OpenCV libraries, ensuring that all images were standardized to the same size. In addition to resizing, multiple augmentation techniques were applied to diversify the dataset and improve model generalization. These augmentations include:

Rotation: Random rotations applied to simulate different perspectives.

Flipping: Horizontal flips to account for variations in animal orientation.

Cropping: Random cropping to focus on various parts of the animal subjects.

Scaling: Minor scaling adjustments to simulate different zoom levels.

All preprocessing and augmentation were carried out to enhance the robustness of any model trained on this data, without the need for further augmentation steps. Therefore, the dataset is ready for immediate use in training deep learning models such as CNNs (Convolutional Neural Networks) or transfer learning models.

Applications:

This dataset is ideal for:

Image Classification: Train models to accurately classify different animal species.

Transfer Learning: Utilize pre-trained models to fine-tune performance on this dataset.

Computer Vision Research: Explore various computer vision tasks, such as animal identification, object detection, and species recognition.

Wildlife and Conservation Studies: Use the dataset to build Al systems capable of identifying animals in the wild for tracking and conservation efforts.

Potential Use Cases:

Education: For students and researchers to learn and experiment with animal classification using computer vision techniques.

Al and Machine Learning Competitions: A challenging dataset for machine learning competitions centered around image classification.

Mobile Applications: Can be used to develop apps for real-time animal identification using image recognition technology.

Dataset Format:

The dataset is structured for ease of use, with each folder containing images pertaining to a specific class. The file format is as follows:

Folder Structure: dataset/{class_name}/{image_files.jpg}

Image Type: JPEG/PNG

Annotations: No specific annotations are included, but each folder name serves as the label for the images within it.

This dataset is sourced from Kaggle.

The MLCommons Dollar Street Dataset is a collection of images of everyday household items from homes around the world that visually captures socioeconomic diversity of traditionally underrepresented populations. It consists of public domain data, licensed for academic, commercial and non-commercial usage, under CC-BY and CC-BY-SA 4.0. The dataset was developed because similar datasets lack socioeconomic metadata and are not representative of global diversity.

This is a subset of the original dataset that can be used for multiclass classification with 10 categories. It is designed to be used in teaching, similar to the widely used, but unlicensed CIFAR-10 dataset.

These are the preprocessing steps that were performed:

1. Only take examples with one imagenet_synonym label
2. Use only examples with the 10 most frequently occuring labels
3. Downscale images to 64 x 64 pixels
4. Split data in train and test
5. Store as numpy array

This is the label mapping:

Checkout https://github.com/carpentries-lab/deep-learning-intro/blob/main/instructors/prepare-dollar-street-data.ipynb" target="_blank" rel="noopener">this notebook to see how the subset was created.

The original dataset was downloaded from https://www.kaggle.com/datasets/mlcommons/the-dollar-street-dataset. See https://mlcommons.org/datasets/dollar-street/ for more information.

(A) Reservoir low sensitivity_Real, (B) Reservoir high sensitivity_Real, (C) Pool low sensitivity_Real, (D) Pool high sensitivity_Real, (E) Reservoir low sensitivity_Synth, (F) Reservoir high sensitivity_Synth, (G) Pool low sensitivity_Synth, (H) Pool high sensitivity_Synth, (I) T_Real, (J) T_Synth, (K) T_Aug. (TXT)

The image dataset was prepared for training deep learning image segmentation models to identify karst sinkholes. Information about the work can be found at (https://github.com/mvrl/sink-seg/). The dataset consists of a DEM image, an aerial image, and a binary sinkhole label image in an area in central Kentucky, USA. It also includes four images derived from the DEM image. The image dataset is sourced from publicly available data from Kentucky's Elevation Data & Aerial Photography Program (https://kyfromabove.ky.gov/) and Kentucky LiDAR-derived sinkholes (https://kgs.uky.edu/geomap).

This is the source code of the paper:

Patil, S.#, Dong, Y.#,*, Farah, H, & Hellendoorn, J. (2024). Efficient Sequential Neural Network Based on Spatial-Temporal Attention and Linear LSTM for Robust Lane Detection Using Multi-Frame Images (Under Review)

How to use the codes

(1) Download tvtLANE Dataset:

You can download this **dataset** from the link in the '**Dataset-Description-v1.2.pdf**' file.

BaiduYun：https://pan.baidu.com/s/1lE2CjuFa9OQwLIbi-OomTQ passcodes：tf9x

Or

Google Drive: https://drive.google.com/drive/folders/1MI5gMDspzuV44lfwzpK6PX0vKuOHUbb_?usp=sharing

The **pretrained model** is also provided in the "/model" folder, named as 98.48263448061671_RAd_lr0.001_batch70_FocalLoss_poly_alpha0.25_gamma2.0_Attention_UNet_LSTM.pth .

(2) Set up

## Requirements

PyTorch 0.4.0

Python 3.9

CUDA 8.0

## Preparation

### Data Preparation

The dataset contains 19383 continuous driving scenes image sequences, and 39460 frames of them are labeled. The size of images is 128*256.

The training set contains 19096 image sequences. Each 13th and 20th frame in a sequence are labeled, and the image and their labels are in “clips_13(_truth)” and “clips_20(_truth)”. All images are contained in “clips_all”.

Sequences in “0313”, “0531” and “0601” subfolders are constructed on TuSimple lane detection dataset, containing scenes in American highway. The four “weadd” folders are added images in rural road in China.

The testset has two parts: Testset #1 (270 sequences, each 13th and 20th image is labeled) for testing the overall performance of algorithms. The Testset #2 (12 kinds of hard scenes, all frames are labeled) for testing the robustness of algorithms.

To input the data, we provide three index files(train_index, val_index, and test_index). Each row in the index represents for a sequence and its label, including the former 5 input images and the last ground truth (corresponding to the last frame of 5 inputs).

The dataset needs to be put into a folder with regards to the location in index files, (i.e., txt files in "./data/". The index files should also be modified add cording to your local computer settings. If you want to use your own data, please refer to the format of our dataset and indexes.

(3) Training

Before training, change the paths including "train_path"(for train_index.txt), "val_path"(for val_index.txt), "pretrained_path" in config_Att.py to adapt to your environment.

Choose the models (UNet_ConvLSTM | SCNN_UNet_ConvLSTM | SCNN_UNet_Attention) as the default one which is also indicated by default='UNet-ConvLSTM' thus you do not need to make change for this. And adjust the arguments such as class weights (now the weights are set to fit the tvtLANE dataset), batch size, learning rate, and epochs in config_Att.py. You can also adjust other settings, e.g., optimizer, check in the codes for details.

Then simply run: train.py. If running successfully, there will be model files saved in the "./model" folder. The validating results will also be printed.

(4) Test

To evaluate the performance of a trained model, please select the trained model or put your own models into the "./model/" folder and change "pretrained_path" in test.py according to the local setting, then change "test_path" to the location of test_index.txt, and "save_path" for the saved results.

Choose the right model that would be evaluated, and then simply run: test.py.

The quantitative evaluations of Accuracy, Precision, Recall, and F1 measure would be printed, and the lane detection segmented results will be saved in the "./save/" folder as pictures.

Authors

Yongqi Dong (yongqidong369@gmail.com), Sandeep Patil, Haneen Farah, Hans Hellendoorn

BackgroundThe performance of existing image-based training models in evaluating bowel preparation on colonoscopy videos was relatively low, and only a few models used external data to prove their generalization. Therefore, this study attempted to develop a more precise and stable AI system for assessing bowel preparation of colonoscopy video.MethodsWe proposed a system named ViENDO to assess the bowel preparation quality, including two CNNs. First, Information-Net was used to identify and filter out colonoscopy video frames unsuitable for Boston bowel preparation scale (BBPS) scoring. Second, BBPS-Net was trained and tested with 5,566 suitable short video clips through three-dimensional (3D) convolutional neural network (CNN) technology to detect BBPS-based insufficient bowel preparation. Then, ViENDO was applied to complete withdrawal colonoscopy videos from multiple centers to predict BBPS segment scores in clinical settings. We also conducted a human-machine contest to compare its performance with endoscopists.ResultsIn video clips, BBPS-Net for determining inadequate bowel preparation generated an area under the curve of up to 0.98 and accuracy of 95.2%. When applied to full-length withdrawal colonoscopy videos, ViENDO assessed bowel cleanliness with an accuracy of 93.8% in the internal test set and 91.7% in the external dataset. The human-machine contest demonstrated that the accuracy of ViENDO was slightly superior compared to most endoscopists, though no statistical significance was found.ConclusionThe 3D-CNN-based AI model showed good performance in evaluating full-length bowel preparation on colonoscopy video. It has the potential as a substitute for endoscopists to provide BBPS-based assessments during daily clinical practice.

Multiplexed imaging technologies provide insights into complex tissue architectures. However, challenges arise due to software fragmentation with cumbersome data handoffs, inefficiencies in processing large images (8 to 40 gigabytes per image), and limited spatial analysis capabilities. To efficiently analyze multiplexed imaging data, we developed SPACEc, a scalable end-to-end Python solution, that handles image extraction, cell segmentation, and data preprocessing and incorporates machine-learning-enabled, multi-scaled, spatial analysis, operated through a user-friendly and interactive interface. The demonstration dataset was derived from a previous analysis and contains TMA cores from a human tonsil and tonsillitis sample that were acquired with the Akoya PhenocyclerFusion platform. The dataset can be used to test the workflow and establish it on a user’s system or to familiarize oneself with the pipeline. Methods Tissue samples: Tonsil cores were extracted from a larger multi-tumor tissue microarray (TMA), which included a total of 66 unique tissues (51 malignant and semi-malignant tissues, as well as 15 non-malignant tissues). Representative tissue regions were annotated on corresponding hematoxylin and eosin (H&E)-stained sections by a board-certified surgical pathologist (S.Z.). Annotations were used to generate the 66 cores each with cores of 1mm diameter. FFPE tissue blocks were retrieved from the tissue archives of the Institute of Pathology, University Medical Center Mainz, Germany, and the Department of Dermatology, University Medical Center Mainz, Germany. The multi-tumor-TMA block was sectioned at 3µm thickness onto SuperFrost Plus microscopy slides before being processed for CODEX multiplex imaging as previously described. CODEX multiplexed imaging and processing To run the CODEX machine, the slide was taken from the storage buffer and placed in PBS for 10 minutes to equilibrate. After drying the PBS with a tissue, a flow cell was sealed onto the tissue slide. The assembled slide and flow cell were then placed in a PhenoCycler Buffer made from 10X PhenoCycler Buffer & Additive for at least 10 minutes before starting the experiment. A 96-well reporter plate was prepared with each reporter corresponding to the correct barcoded antibody for each cycle, with up to 3 reporters per cycle per well. The fluorescence reporters were mixed with 1X PhenoCycler Buffer, Additive, nuclear-staining reagent, and assay reagent according to the manufacturer's instructions. With the reporter plate and assembled slide and flow cell placed into the CODEX machine, the automated multiplexed imaging experiment was initiated. Each imaging cycle included steps for reporter binding, imaging of three fluorescent channels, and reporter stripping to prepare for the next cycle and set of markers. This was repeated until all markers were imaged. After the experiment, a .qptiff image file containing individual antibody channels and the DAPI channel was obtained. Image stitching, drift compensation, deconvolution, and cycle concatenation are performed within the Akoya PhenoCycler software. The raw imaging data output (tiff, 377.442nm per pixel for 20x CODEX) is first examined with QuPath software (https://qupath.github.io/) for inspection of staining quality. Any markers that produce unexpected patterns or low signal-to-noise ratios should be excluded from the ensuing analysis. The qptiff files must be converted into tiff files for input into SPACEc. Data preprocessing includes image stitching, drift compensation, deconvolution, and cycle concatenation performed using the Akoya Phenocycler software. The raw imaging data (qptiff, 377.442 nm/pixel for 20x CODEX) files from the Akoya PhenoCycler technology were first examined with QuPath software (https://qupath.github.io/) to inspect staining qualities. Markers with untenable patterns or low signal-to-noise ratios were excluded from further analysis. A custom CODEX analysis pipeline was used to process all acquired CODEX data (scripts available upon request). The qptiff files were converted into tiff files for tissue detection (watershed algorithm) and cell segmentation.

Semantic segmentation results using a training dataset of real underwater sonar images and synthetic underwater sonar images.

Methods Cotton plants were grown in a well-controlled greenhouse in the NC State Phytotron as described previously (Pierce et al, 2019). Flowers were tagged on the day of anthesis and harvested three days post anthesis (3 DPA). The distinct fiber shapes had already formed by 2 DPA (Stiff and Haigler, 2016; Graham and Haigler, 2021), and fibers were still relatively short at 3 DPA, which facilitated the visualization of multiple fiber tips in one image. Cotton fiber sample preparation, digital image collection, and image analysis: Ovules with attached fiber were fixed in the greenhouse. The fixative previously used (Histochoice) (Stiff and Haigler, 2016; Pierce et al., 2019; Graham and Haigler, 2021) is obsolete, which led to testing and validation of another low-toxicity, formalin-free fixative (#A5472; Sigma-Aldrich, St. Louis, MO; Fig. S1). The boll wall was removed without damaging the ovules. (Using a razor blade, cut away the top 3 mm of the boll. Make about 1 mm deep longitudinal incisions between the locule walls, and finally cut around the base of the boll.) All of the ovules with attached fiber were lifted out of the locules and fixed (1 h, RT, 1:10 tissue:fixative ratio) prior to optional storage at 4°C. Immediately before imaging, ovules were examined under a stereo microscope (incident light, black background, 31X) to select three vigorous ovules from each boll while avoiding drying. Ovules were rinsed (3 x 5 min) in buffer [0.05 M PIPES, 12 mM EGTA. 5 mM EDTA and 0.1% (w/v) Tween 80, pH 6.8], which had lower osmolarity than a microtubule-stabilizing buffer used previously for aldehyde-fixed fibers (Seagull, 1990; Graham and Haigler, 2021). While steadying an ovule with forceps, one to three small pieces of its chalazal end with attached fibers were dissected away using a small knife (#10055-12; Fine Science Tools, Foster City, CA). Each ovule piece was placed in a single well of a 24-well slide (#63430-04; Electron Microscopy Sciences, Hatfield, PA) containing a single drop of buffer prior to applying and sealing a 24 x 60 mm coverslip with vaseline. Samples were imaged with brightfield optics and default settings for the 2.83 mega-pixel, color, CCD camera of the Keyence BZ-X810 imaging system (www.keyence.com; housed in the Cellular and Molecular Imaging Facility of NC State). The location of each sample in the 24-well slides was identified visually using a 2X objective and mapped using the navigation function of the integrated Keyence software. Using the 10X objective lens (plan-apochromatic; NA 0.45) and 60% closed condenser aperture setting, a region with many fiber apices was selected for imaging using the multi-point and z-stack capture functions. The precise location was recorded by the software prior to visual setting of the limits of the z-plane range (1.2 µm step size). Typically, three 24-sample slides (representing three accessions) were set up in parallel prior to automatic image capture. The captured z-stacks for each sample were processed into one two-dimensional image using the full-focus function of the software. (Occasional samples contained too much debris for computer vision to be effective, and these were reimaged.) Resources in this dataset:Resource Title: Deltapine 90 - Manually Annotated Training Set. File Name: GH3 DP90 Keyence 1_45 JPEG.zipResource Description: These images were manually annotated in Labelbox.Resource Title: Deltapine 90 - AI-Assisted Annotated Training Set. File Name: GH3 DP90 Keyence 46_101 JPEG.zipResource Description: These images were AI-labeled in RoboFlow and then manually reviewed in RoboFlow. Resource Title: Deltapine 90 - Manually Annotated Training-Validation Set. File Name: GH3 DP90 Keyence 102_125 JPEG.zipResource Description: These images were manually labeled in LabelBox, and then used for training-validation for the machine learning model.Resource Title: Phytogen 800 - Evaluation Test Images. File Name: Gb cv Phytogen 800.zipResource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.Resource Title: Pima 3-79 - Evaluation Test Images. File Name: Gb cv Pima 379.zipResource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.Resource Title: Pima S-7 - Evaluation Test Images. File Name: Gb cv Pima S7.zipResource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.Resource Title: Coker 312 - Evaluation Test Images. File Name: Gh cv Coker 312.zipResource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.Resource Title: Deltapine 90 - Evaluation Test Images. File Name: Gh cv Deltapine 90.zipResource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.Resource Title: Half and Half - Evaluation Test Images. File Name: Gh cv Half and Half.zipResource Description: These images were used to validate the machine learning model. They were manually annotated in ImageJ.Resource Title: Fiber Tip Annotations - Manual. File Name: manual_annotations.coco_.jsonResource Description: Annotations in COCO.json format for fibers. Manually annotated in Labelbox.Resource Title: Fiber Tip Annotations - AI-Assisted. File Name: ai_assisted_annotations.coco_.jsonResource Description: Annotations in COCO.json format for fibers. AI annotated with human review in Roboflow. Resource Title: Model Weights (iteration 600). File Name: model_weights.zipResource Description: The final model, provided as a zipped Pytorch .pth file. It was chosen at training iteration 600. The model weights can be imported for use of the fiber tip type detection neural network in Python.Resource Software Recommended: Google Colab,url: https://research.google.com/colaboratory/

The dataset used is the OASIS MRI dataset (https://sites.wustl.edu/oasisbrains/), which consists of 80,000 brain MRI images. The images have been divided into four classes based on Alzheimer's progression. The dataset aims to provide a valuable resource for analyzing and detecting early signs of Alzheimer's disease.

To make the dataset accessible, the original .img and .hdr files were converted into Nifti format (.nii) using FSL (FMRIB Software Library). The converted MRI images of 461 patients have been uploaded to a GitHub repository, which can be accessed in multiple parts.

For the neural network training, 2D images were used as input. The brain images were sliced along the z-axis into 256 pieces, and slices ranging from 100 to 160 were selected from each patient. This approach resulted in a comprehensive dataset for analysis.

Patient classification was performed based on the provided metadata and Clinical Dementia Rating (CDR) values, resulting in four classes: demented, very mild demented, mild demented, and non-demented. These classes enable the detection and study of different stages of Alzheimer's disease progression.

During the dataset preparation, the .nii MRI scans were converted to .jpg files. Although this conversion presented some challenges, the files were successfully processed using appropriate tools. The resulting dataset size is 1.3 GB.

With this comprehensive dataset, the project aims to explore various neural network models and achieve optimal results in Alzheimer's disease detection and analysis.

Acknowledgments: “Data were provided 1-12 by OASIS-1: Cross-Sectional: Principal Investigators: D. Marcus, R, Buckner, J, Csernansky J. Morris; P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382”

Citation: OASIS-1: Cross-Sectional: https://doi.org/10.1162/jocn.2007.19.9.1498

If you are looking for processed NifTi image version of this dataset please click here

The dataset is prepared and intended as a data source for development of a stress analysis method based on machine learning. It consists of finite element stress analyses of randomly generated mechanical structures. The dataset contains more than 270,794 pairs of stress analyses images (von Mises stress) of randomly generated 2D structures with predefined thickness and material properties. All the structures are fixed at their bottom edges and loaded with gravity force only. See PREVIEW directory with some examples. The zip file contains all the files in the dataset.

Data Annotation and Labeling Market Outlook

The global data annotation and labeling market size was valued at approximately USD 1.6 billion in 2023 and is projected to grow to USD 8.5 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 20.5% during the forecast period. A key growth factor driving this market is the increasing demand for high-quality labeled data to train and validate machine learning and artificial intelligence models.

The rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies has significantly increased the demand for precise and accurate data annotation and labeling. As AI and ML applications become more widespread across various industries, the need for large volumes of accurately labeled data is more critical than ever. This requirement is driving investments in sophisticated data annotation tools and platforms that can deliver high-quality labeled datasets efficiently. Moreover, the complexity of data types being used in AI/ML applications—from text and images to audio and video—necessitates advanced annotation solutions that can handle diverse data formats.

Another major factor contributing to the growth of the data annotation and labeling market is the increasing adoption of automated data labeling tools. While manual annotation remains essential for ensuring high-quality outcomes, automation technologies are increasingly being integrated into annotation workflows to improve efficiency and reduce costs. These automated tools leverage AI and ML to annotate data with minimal human intervention, thus expediting the data preparation process and enabling organizations to deploy AI/ML models more rapidly. Additionally, the rise of semi-supervised learning approaches, which combine both manual and automated methods, is further propelling market growth.

The expansion of sectors such as healthcare, automotive, and retail is also fueling the demand for data annotation and labeling services. In healthcare, for instance, annotated medical images are crucial for training diagnostic algorithms, while in the automotive sector, labeled data is indispensable for developing autonomous driving systems. Retailers are increasingly relying on annotated data to enhance customer experiences through personalized recommendations and improved search functionalities. The growing reliance on data-driven decision-making across these and other sectors underscores the vital role of data annotation and labeling in modern business operations.

Regionally, North America is expected to maintain its leadership position in the data annotation and labeling market, driven by the presence of major technology companies and extensive R&D activities in AI and ML. Europe is also anticipated to witness significant growth, supported by government initiatives to promote AI technologies and increased investment in digital transformation projects. The Asia Pacific region is expected to emerge as a lucrative market, with countries like China and India making substantial investments in AI research and development. Additionally, the increasing adoption of AI/ML technologies in various industries across the Middle East & Africa and Latin America is likely to contribute to market growth in these regions.

Type Analysis

The data annotation and labeling market is segmented by type, which includes text, image/video, and audio. Text annotation is a critical segment, driven by the proliferation of natural language processing (NLP) applications. Text data annotation involves labeling words, phrases, or sentences to help algorithms understand language context, sentiment, and intent. This type of annotation is vital for developing chatbots, voice assistants, and other language-based AI applications. As businesses increasingly adopt NLP for customer service and content analysis, the demand for text annotation services is expected to rise significantly.

Image and video annotation represents another substantial segment within the data annotation and labeling market. This type involves labeling objects, features, and activities within images and videos to train computer vision models. The automotive industry's growing focus on developing autonomous vehicles is a significant driver for image and video annotation. Annotated images and videos are essential for training algorithms to recognize and respond to various road conditions, signs, and obstacles. Additionally, sectors like healthcare, where medical imaging data needs precise annotation for diagnostic AI tools, and retail, which uses visual data for inventory management and customer insigh

Introduction

Stomatal density analysis is essential for studying plant species but is traditionally time-consuming and labor-intensive when performed manually. Utilizing a deep learning model for stomatal counting can significantly reduce processing time and enable high-throughput analysis of Arabidopsis phenotypes. https://www.sciencefacts.net/wp-content/uploads/2021/01/Stomata-Diagram.jpg" alt="">Figure 1. Illustration of stomata on the plant leaf surface.

In this project, we created an annotated dataset using Arabidopsis leaf impressions and trained YOLO convolutional neural network (CNN) models for stomata detection. Currently, I am developing a pipeline to automatically count stomata from short videos of leaf impressions.

Dataset Preparation

Microscopic images of leaf impressions are rarely fully in focus due to leaf surface curvature (Figure 2), complicating both manual and algorithm-based stomatal counting. To overcome this, videos with changing focal planes are used to capture the entire field of view. https://i.ibb.co/CtwgjFN/2024-10-12-095316514.png" alt="">Figure 2. Representative microscopic image of Arabidopsis leaf impression.

However, CNNs cannot be trained directly on video data. Therefore, the dataset must consist of annotated images. I applied focus stacking technology to create fully focused images from multiple video frames (Figure 3). These images were then annotated to form the final dataset, available on the project page. https://i.ibb.co/FV9WZ8v/2024-10-12-113819061.png" alt="">Figure 3. Pipeline for dataset preparation: focus stacking video frames to create a single image, followed by annotation using Roboflow.

Training

YOLO CNN models were trained on this dataset. All versions of YOLOv8 models have been uploaded and can be accessed on this page. More advanced YOLOv9 and YOLOv10 models, which show improved performance, are still under development and will be integrated into the final project pipeline.

Implementation

Converting videos to fully focused images is time-intensive, especially when processing large quantities. To address this, I am developing a method to apply the model directly to video data using a heatmap approach. The model detects each stoma across multiple frames, generating a heatmap where the numbers represent the frequency of stoma detection (Figure 4). This process is being automated with Python scripts to allow batch processing of multiple videos. https://i.ibb.co/xgKwWLM/heatmap-with-numbers-threshold.jpg" alt=""> Figure 4. Heatmap generated by a script using the YOLOv9 CNN model: circles represent stomata, and numbers indicate the number of frames in which encircled stoma was detected.

XYZ* dataset with 1000 documents.

Lipid nanoparticles (LNPs) were prepared as described (https://doi.org/10.1038/s42003-021-02441-2) using the lipids DLin-KC2-DMA, DSPC, cholesterol, and PEG-DMG2000 at mol ratios of 50:10:38.5:1.5. Four sample types were prepared: LNPs in the presence and absence of RNA, and with LNPs ejected into pH 4 and pH 7.4 buffer after microfluidic assembly. To prepare samples for imaging, 3 ?L of LNP formulation was applied to holey carbon grids (Quantifoil, R3.5/1, 200 mesh copper). Grids were then incubated for 30 s at 298 K and 100% humidity before blotting and plunge-freezing into liquid ethane using a Vitrobot Mark IV (Thermo Fisher Scientific). Grids were imaged at 200 kV using a Talos Arctica system equipped with a Falcon 3EC detector (Thermo Fisher Scientific). A nominal magnification of 45,000x was used, corresponding to images with a pixel count of 4096x4096 and a calibrated pixel spacing of 0.223 nm. Micrographs were collected as dose-fractionated ?movies? at nominal defocus values between -1 and -3 ?m, with 10 s total exposures consisting of 66 frames with a total electron dose of 12,000 electrons per square nanometer. Movies were motion-corrected using MotionCor2 (https://doi.org/10.1038/nmeth.4193), resulting in flattened micrographs suitable for downstream particle segmentation. A total of 38 images were manually segmented into particle and non-particle regions. Segmentation masks and their corresponding images are deposited in this data set.

The Synset Boulevard dataset contains a total of 259,200 synthetically generated images of cars from a frontal traffic camera perspective, annotated by vehicle makes, models and years of construction for machine learning methods (ML) in the scope (task) of vehicle make and model recognition (VMMR). The data set contains 162 vehicle models from 43 brands with 200 images each, as well as 8 sub-data sets each to be able to investigate different imaging qualities. In addition to the classification annotations, the data set also contains label images for semantic segmentation, as well as information on image and scene properties, as well as vehicle color. The dataset was presented in May 2024 by Anne Sielemann, Stefan Wolf, Masoud Roschani, Jens Ziehn and Jürgen Beyerer in the publication: Sielemann, A., Wolf, S., Roschani, M., Ziehn, J. and Beyerer, J. (2024). Synset Boulevard: A Synthetic Image Dataset for VMMR. In 2024 IEEE International Conference on Robotics and Automation (ICRA). The model information is based on information from the ADAC online database (www.adac.de/rund-ums-fahrzeug/autokatalog/marken-modelle). The data was generated using the simulation environment OCTANE (www.octane.org), which uses the Cycles ray tracer of the Blender project. The dataset's website provides detailed information on the generation process and model assumptions. The dataset is therefore also intended to be used for the suitability analysis of simulated, synthetic datasets. The data set was developed as part of the Fraunhofer PREPARE program in the "ML4Safety" project with the funding code PREPARE 40-02702, as well as funded by the "Invest BW" funding program of the Ministry of Economic Affairs, Labour and Tourism as part of the "FeinSyn" research project.

Training and validation datasets_arrow detection.zip:
Training and validation datasets for arrow detection using Faster R-CNN model. A total of 6,471 images have been prepared, including 2,332 images from five different sources and 4,139 augmented images.

Test dataset_arrow detection.zip:
Test dataset for arrow detection using Faster R-CNN model. A total of 100 images have been prepared from 89 papers searched through PubMed Central (PMC).

EBPI outputs.txt:
Reaction information extracted using EBPI from 49,846 biological pathway images across 466 target chemicals.

The dataset comprises images from two different particle systems namely a) Vansil ® WG Wollastonite particles and b) L-Glutamic acid particles. The images were captured in situ using two different EasyViewer probes (EasyViewer 100 and EasyViewer 400) where pre-weighed solids were suspended in solvent to create a known solid loading composition. Along with the images, in-line Chord Length Distribution (CLD) data was also measured with the ParticleTrack G400 probe. The developed image analysis algorithm can be validated using either the CLD data collected in situ or with the manufacturer's size data provided for the Wollastonite dataset.

System A: Vansil ® WG Wollastonite

Wollastonite, a natural calcium silicate mineral known for its elevated brightness, serves as an effective functional filler and reinforcement in various materials such as thermoplastics, thermosets, engineering alloys, and elastomers. The distinctive needle-like shape and high aspect ratio of VANSIL wollastonite particles contribute to enhanced mechanical strength and stiffness, while simultaneously improving surface quality, toughness, and durability. To produce VANSIL WG, the ore containing wollastonite is milled in such a way that the fine, needle-like particles are preserved and recovered. It is used as a sample system as it is easy to prepare, stable, and optically similar to many particles found in small molecule crystallizations.

Properties

1. Average Aspect Ratio: 15:1
2. Average Needle Length: 90um
3. 200 mesh retention, %: 15-20%
4. 325 mesh retention, %: 30-35%
5. Density: 2.9 g/cm^3
6. Particle Size (Horiba LA-300) a. D10, um: 4 b. D50, um: 28 c. D90, um: 117 d. D95, um: 146

Sample Preparation

As received samples of weight % from 0.1, 1, 5, 10, and 20 % were suspended in water.

System B: L-Glutamic Acid

Glutamic acid is an α-amino acid crucial for protein biosynthesis in nearly all living organisms.

Sample Preparation

As received sample of 5 wt% was suspended in acetone.

PAT Instrumentation

All instruments were calibration to Mettler Toledo Factory Specifications before measurement. Default collection parameters were used. i. EasyViewer 100 ii. EasyViewer 400 iii. ParticleTrack G400

Starter task to get started with the Dataset:

Utilizing this dataset can start by building a deep-learning framework to segment crystallization images. Image segmentation includes the classification of the image’s pixels in regions. This way each segment can be analyzed individually as per their importance depending on the application. Semantic and instance are two techniques of image segmentation that can be applied to the images of this dataset. Semantic segmentation assigns object class labels to each pixel which in this dataset would detect and classify the samples with significantly different morphologies. Instance segmentation identifies and segments individual objects which in this dataset would identify the shape, type, size distribution, and count of particles.

The framework development can start with semantic segmentation as follows. Download the images from EasyViewer 100 and EasyViewer 400, and the corresponding ParticleTrack G400 data followed by annotating the images to prepare the data for training. The annotation step is a crucial component of a deep learning-based approach. The metadata of images acquired by ParticleTrack G400 can be referenced for this purpose.

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