The booming video annotation service market is projected to reach $7 Billion by 2033, driven by AI and ML advancements. Explore key trends, applications (medical, autonomous vehicles, agriculture), top companies, and regional insights in this comprehensive market analysis.

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.

This dataset contains UAV RGB videos (MP4) recorded with a Phantom4 RTK in a vineyard during the harvesting campaign of 2023. It also includes frames and annotations (PNG) to boost Object Detection and Tracking of grape bunches. There are two types of videos: (1) videos capturing the side of the canopy from a frontal point of view only, and (2) videos that collect the data from multiple perspectives to avoid leaf occlusion, common in commercial vineyards. All flights were executed 3 meters above ground level, with a clear sky and wind speed below 0.5 m/s.

Fish Tracking - Object Detection dataset

The collection of video frames, capturing various types of fish swimming in the water. The dataset includes fish of different colors, sizes and with different swimming speeds.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on our website to buy the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F05e0a6bd3bdaf28d534777ac1dee8b42%2Fout1.gif?generation=1692029879238359&alt=media" alt="">

Data Format

Each video frame from images folder is paired with an annotations.xml file that meticulously defines the tracking of each fish using bouunding boxes.

The data labeling is visualized in the boxes folder.

Example of the XML-file

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Object tracking might be made in accordance with your requirements.

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keywords: animal recognition, animal detection, farming, fish recognition, fish detection, image-based recognition, fish images dataset, object detection, object tracking, deep learning, computer vision, animal contacts, images dataset, agriculture, fish species annotations, fish pond, pond water, sea, fish farms, environment, aquaculture production, bounding boxes

This dataset contains measurements of water quality parameters along with behavior-related variables obtained from fish recognition and localization using machine learning techniques on video footage. The water quality data were collected using a Hanna HI98194 multiparameter probe, while the video footage was taken with a webcam connected to a Raspberry Pi 4. A total of 247 uniformly distributed frames from the video footage were manually annotated to train a YOLOv4 model for fish recognition and tracking. Using the trained model, the complete video footage was processed and the time series of fish pairwise distance and average traveled distance were calculated from the recognition model. This dataset can potentially be used to study fish behavior-related and water quality variables and their relationship in fish farming scenarios. Dataset files: training_data.zip: 247 uniformly distributed frames from the entirety of the video footage were annotated for model training and can be found here in various formats including YOLO. waterquality_data.zip: includes 55 hours and 28 minutes of data from physico-chemical variables collected from a water tank with a Hanna HI98194 probe. The collected parameters were temperature, dissolved oxygen, electrical conductivity, resistivity, total dissolved solids and pH. behavioral_data.zip: contains the processed variables obtained from the video footage using the trained machine learning model. weights.zip: the weights used for the YOLOv4 model can be found here. video_xx.zip: contains 51 hours and 40 minutes of one-minute recordings of the fish tank. Split into 26 two hour sections for download. Useful to extract behavior-related variables.

Version history: v1: Initial release. v2: Changed the format for waterquality_data and behavioral_data to fix an error where the timestamp seconds were lost and replaced with 0 when saving and reopening the files.

The AI Data Labeling Services market is booming, projected to reach $40B+ by 2033! Learn about market trends, key players (Scale AI, Labelbox, Appen), and growth drivers in this comprehensive analysis. Explore regional insights and understand the impact of cloud-based solutions on this rapidly evolving sector.

Long-term autonomous monitoring of wild fish populations surrounding fish farms can contribute to a better understanding of interactions between wild and farmed fish, which can have wide-ranging implications for disease transmission, stress in farmed fish, wild fish behavior and nutritional status, etc. The ability to monitor the presence of wild fish and its variability with time and space will improve our understanding of the dynamics of such interactions and the implications that follow. Many efforts are underway to recognize fish species using artificial intelligence. However there are not many image datasets publicly available to train these neural networks, and even fewer that include species that are relevant for the aquaculture sector. Here we present a public dataset of annotated images for fish species recognition with deep learning. The dataset contains 9487 annotated images of farmed salmonids and 3027 annotated images of saithe and it is expected to grow in the near future. This dataset was the result of processing nearly 50 hours of video footage taken inside and outside cages from several fish farms in Norway. The footage was processed with a semi-automatic system to create large image datasets of fish under water. The system combines techniques of image processing with deep neural networks in an iterative process to extract, label, and annotate images from video sources. The details of the system are described in a journal paper that is currently under review. This information will be updated when the paper is published.

This dataset contains materials needed to reproduce the results in the paper 'ADVANCING PRECISION LIVESTOCK FARMING IN PIGS THROUGH MARKERLESS POSE ESTIMATION: A comparison between DEEPLABCUT AND SLEAP'. The experiment consisted on annotation a video of pigs using DeepLabCut and SLEAP, run training and compare the performance of both software.

The materials are:

• The video used for annotations and model comparison
• The instruction on how to use this dataset
• The SLEAP.zip file containing the SLEAP project (.slp project file with corresponding artifacts)
• The DeepLabCut.zip file containing the SLEAP project (.yaml project file with corresponding artifacts)
• The list of citations that cited both DeepLabCut and SLEAP framework

La taille du marché mondial des outils d'annotation de données s'élève à 102.38 milliards USD en 2023, et devrait atteindre 908.57 milliards USD d'ici 2032, à un TCAC de 24.4 % de 2024 à 2032.

This dataset focuses on drone-based rice panicle detection in Gazipur, Bangladesh, offering valuable visual data to researchers in agricultural studies. Captured using an advanced drone with a 4K resolution camera, the dataset comprises 2193 high-resolution images of rice fields and 5701 images after augmentation. All the images are annotated with precision to aid in automated rice panicle identification. Its main purpose is to support the development of algorithms and systems for critical agricultural tasks like crop monitoring and yield estimation, as well as disease identification and plant health evaluation. The dataset's creation involved extracting frames from drone-recorded video footage and meticulously annotating them with manual and deep learning algorithms using a semi-automatic approach.

High-level overview of the experimental setup for rice panicle data collection and creating an annotated image by splitting the extracting image into a 4 × 4 image size of 960×540.

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Full Paper available in the following link:

Comprehensive dataset of annotated rice panicle image from Bangladesh

Der globale Markt für Datenannotationstools wird im Jahr 2023 ein Volumen von 102.38 Milliarden US-Dollar haben und bis 2032 voraussichtlich 908.57 Milliarden US-Dollar erreichen, bei einer durchschnittlichen jährlichen Wachstumsrate von 24.4 % zwischen 2024 und 2032.

Properties of 12 videos annotated for pig behavior tracking performance analysis.

WSI Annotation Services Market Outlook

As per our latest research, the global WSI Annotation Services market size stood at USD 1.42 billion in 2024, reflecting robust expansion driven by advancements in artificial intelligence and machine learning applications across diverse sectors. The market is expected to grow at a CAGR of 23.7% from 2025 to 2033, reaching a forecasted value of USD 11.19 billion by 2033. The primary growth factor fueling this remarkable trajectory is the surging demand for high-quality annotated data to train sophisticated AI models, particularly in sectors like autonomous vehicles, healthcare diagnostics, and retail automation.

One of the most significant growth drivers for the WSI Annotation Services market is the escalating adoption of AI-powered solutions across industries. As artificial intelligence becomes increasingly integral to business processes and consumer products, the necessity for accurately annotated data has soared. Companies are leveraging WSI annotation services to enhance the precision of machine learning algorithms, particularly in image, text, video, and audio data domains. This trend is particularly pronounced in sectors such as autonomous vehicles, where annotated data is essential for object detection and navigation, and in healthcare, where annotated medical images underpin diagnostic AI tools. The proliferation of digital transformation initiatives and the need to process large volumes of unstructured data further amplify the market’s expansion.

Another critical growth factor is the rapid evolution of data annotation technologies and methodologies. The market has witnessed substantial investments in automation tools, cloud-based platforms, and AI-assisted annotation frameworks that streamline the annotation process, enhance accuracy, and reduce turnaround times. These advancements are making WSI annotation services more accessible and cost-effective for organizations of all sizes, from startups to large enterprises. Furthermore, the growing emphasis on data privacy and regulatory compliance has spurred the adoption of secure, on-premises, and hybrid deployment models, broadening the market’s appeal across highly regulated industries such as BFSI and healthcare. The integration of advanced quality control mechanisms and scalable annotation workflows has further reinforced market growth.

The increasing focus on industry-specific applications is also propelling the WSI Annotation Services market forward. In retail and e-commerce, for instance, annotated data is pivotal for developing recommendation engines, visual search tools, and customer sentiment analysis. In agriculture, annotation services enable the deployment of precision farming technologies by facilitating crop and livestock monitoring through annotated images and sensor data. The security and surveillance sector is leveraging annotation for facial recognition, anomaly detection, and threat assessment. This diversification of use cases is driving demand for specialized annotation services tailored to the unique requirements of each industry, thereby expanding the market’s scope and value proposition.

From a regional perspective, North America continues to dominate the WSI Annotation Services market, accounting for the largest revenue share in 2024, closely followed by Europe and the Asia Pacific. The presence of leading technology companies, robust digital infrastructure, and a mature AI ecosystem are key factors underpinning North America’s leadership. However, the Asia Pacific region is emerging as the fastest-growing market, fueled by rapid digitization, increasing investments in AI research, and the proliferation of tech startups. Europe’s market growth is supported by strong regulatory frameworks and a focus on ethical AI development. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions increasingly adopt AI-driven solutions.

Type Analysis

The WSI Annotation Services market by