About Dataset The file contains 24K unique figure obtained from various Google resources Meticulously curated figure ensuring diversity and representativeness Provides a solid foundation for developing robust and precise figure allocation algorithms Encourages exploration in the fascinating field of feed figure allocation

Unparalleled Diversity Dive into a vast collection spanning culinary landscapes worldwide. Immerse yourself in a diverse array of cuisines, from Italian pasta to Japanese sushi. Explore a rich tapestry of food imagery, meticulously curated for accuracy and breadth. Precision Labeling Benefit from meticulous labeling, ensuring each image is tagged with precision. Access detailed metadata for seamless integration into your machine learning projects. Empower your algorithms with the clarity they need to excel in food recognition tasks. Endless Applications Fuel advancements in machine learning and computer vision with this comprehensive dataset. Revolutionize food industry automation, from inventory management to quality control. Enable innovative applications in health monitoring and dietary analysis for a healthier tomorrow. Seamless Integration Seamlessly integrate our dataset into your projects with user-friendly access and documentation. Enjoy high-resolution images optimized for compatibility with a range of AI frameworks. Access support and resources to maximize the potential of our dataset for your specific needs.

Conclusion Embark on a culinary journey through the lens of artificial intelligence and unlock the potential of feed figure allocation with our SEO-optimized file. Elevate your research, elevate your projects, and elevate the way we perceive and interact with food in the digital age. Dive in today and savor the possibilities!

This dataset is sourced from Kaggle.

Mobile image recognition tools enable devices' camera systems to recognize specific images. These tools are extensively used in many programs and applications, including translation apps and image content search. As of the third quarter of 2023, Mexico recorded the highest reach of mobile image recognition tools, with close to 48 percent of its internet population reporting using such tools. Brazil followed in second place, with 47.6 percent of internet users reporting using image recognition tools on mobile on a monthly basis.

This is a subset of ImageNet called "ImageNet16" more suited for cases with limited computational budget and faster experimentation.

Each class has 400 train images and 100 test images.

* Credit also goes to original creators that constructed the dataset. Unfortunately, I was not able to relocated it online so I reupload it here.

If used in your work please cite as follows:

C. Kyrkou, "Toward Efficient Convolutional Neural Networks With Structured Ternary Patterns," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2024.3380827.

The classes corresponding to imagenet1K:

• n02009912 American_egret

• n02113624 toy_poodle

• n02123597 Siamese_cat

• n02132136 brown_bear

• n02504458 African_elephant

• n02690373 airliner

• n02835271 bicycle-built-for-two

• n02951358 canoe

• n03041632 cleaver

• n03085013 computer_keyboard

• n03196217 digital_clock

• n03977966 police_van

• n04099969 rocking_chair

• n04111531 rotisserie

• n04285008 sports_car

• n04591713 wine_bottle

From original map.txt

knife = n03041632

keyboard = n03085013

elephant = n02504458

bicycle = n02835271

airplane = n02690373

clock = n03196217

oven = n04111531

chair = n04099969

bear = n02132136

boat = n02951358

cat = n02123597

bottle = n04591713

truck = n03977966

car = n04285008

bird = n02009912

dog = n02113624

Folder Structure

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Some preliminary results:

Massive Credit to original ImageNet authors
[1] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei.ImageNet Large Scale Visual Recognition Challenge. IJCV, 2015

The global image recognition solution market is experiencing robust growth, projected to reach $663.4 million in 2025. While the provided CAGR is missing, considering the rapid advancements in AI and computer vision technologies, and the increasing adoption across diverse sectors like government, SMEs, and large enterprises, a conservative estimate of a 15% CAGR for the forecast period (2025-2033) is reasonable. This implies significant market expansion, driven by factors such as the increasing availability of high-quality data, improvements in deep learning algorithms, and the rising demand for automated image analysis across various applications. The market segmentation reveals a strong presence across different service models (SaaS, PaaS, IaaS) indicating a diverse range of deployment options catering to various organizational needs and technological capabilities. The geographical distribution reveals a strong market presence in North America and Europe, driven by early adoption and technological advancements, with significant growth potential in Asia-Pacific regions fueled by rapid digitalization and increasing investments in AI infrastructure. The leading players in this dynamic market landscape—including established tech giants like Amazon Web Services and Google alongside specialized companies like Clarifai and Scandit—are constantly innovating to improve accuracy, speed, and scalability of image recognition solutions. The competitive landscape is characterized by a combination of established players and emerging startups focusing on niche applications and specialized functionalities. Factors such as increasing data privacy concerns and the need for robust cybersecurity measures present certain restraints; however, continuous innovation and regulatory compliance efforts are likely to mitigate these challenges, paving the way for continued market growth and expansion into new sectors. The interplay of technological advancements, increasing demand, and competitive activity ensures a positive outlook for the image recognition solution market in the coming years.

The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection. The publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld. ILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”. The ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.

Total number of non-empty WordNet synsets: 21841 Total number of images: 14197122 Number of images with bounding box annotations: 1,034,908 Number of synsets with SIFT features: 1000 Number of images with SIFT features: 1.2 million

MULTI-TEMPORAL REMOTE SENSING IMAGE CLASSIFICATION - A MULTI-VIEW APPROACH

VARUN CHANDOLA* AND RANGA RAJU VATSAVAI*

Abstract. Multispectral remote sensing images have been widely used for automated land use and land cover classification tasks. Often thematic classification is done using single date image, however in many instances a single date image is not informative enough to distinguish between different land cover types. In this paper we show how one can use multiple images, collected at different times of year (for example, during crop growing season), to learn a better classifier. We propose two approaches, an ensemble of classifiers approach and a co-training based approach, and show how both of these methods outperform a straightforward stacked vector approach often used in multi-temporal image classification. Additionally, the co-training based method addresses the challenge of limited labeled training data in supervised classification, as this classification scheme utilizes a large number of unlabeled samples (which comes for free) in conjunction with a small set of labeled training data.

Mobile image recognition tools can use the camera feature of smartphones and mobile devices to recognize specific images. As of the third quarter of 2023, about a third of female online users between 16 and 24 years used image recognition tools on their mobile devices every month. Image recognition tools are used in many programs and applications, including translation apps and image content search. During the measured period, Brazil was the country with the highest share of mobile image recognition tools users among its digital population.

Wirestock's AI/ML Image Training Data, 4.5M Files with Metadata: This data product is a unique offering in the realm of AI/ML training data. What sets it apart is the sheer volume and diversity of the dataset, which includes 4.5 million files spanning across 20 different categories. These categories range from Animals/Wildlife and The Arts to Technology and Transportation, providing a rich and varied dataset for AI/ML applications.

The data is sourced from Wirestock's platform, where creators upload and sell their photos, videos, and AI art online. This means that the data is not only vast but also constantly updated, ensuring a fresh and relevant dataset for your AI/ML needs. The data is collected in a GDPR-compliant manner, ensuring the privacy and rights of the creators are respected.

The primary use-cases for this data product are numerous. It is ideal for training machine learning models for image recognition, improving computer vision algorithms, and enhancing AI applications in various industries such as retail, healthcare, and transportation. The diversity of the dataset also means it can be used for more niche applications, such as training AI to recognize specific objects or scenes.

This data product fits into Wirestock's broader data offering as a key resource for AI/ML training. Wirestock is a platform for creators to sell their work, and this dataset is a collection of that work. It represents the breadth and depth of content available on Wirestock, making it a valuable resource for any company working with AI/ML.

The core benefits of this dataset are its volume, diversity, and quality. With 4.5 million files, it provides a vast resource for AI training. The diversity of the dataset, spanning 20 categories, ensures a wide range of images for training purposes. The quality of the images is also high, as they are sourced from creators selling their work on Wirestock.

In terms of how the data is collected, creators upload their work to Wirestock, where it is then sold on various marketplaces. This means the data is sourced directly from creators, ensuring a diverse and unique dataset. The data includes both the images themselves and associated metadata, providing additional context for each image.

The different image categories included in this dataset are Animals/Wildlife, The Arts, Backgrounds/Textures, Beauty/Fashion, Buildings/Landmarks, Business/Finance, Celebrities, Education, Emotions, Food Drinks, Holidays, Industrial, Interiors, Nature Parks/Outdoor, People, Religion, Science, Signs/Symbols, Sports/Recreation, Technology, Transportation, Vintage, Healthcare/Medical, Objects, and Miscellaneous. This wide range of categories ensures a diverse dataset that can cater to a variety of AI/ML applications.

AI Image Recognition Market Report is Segmented by Type (Hardware, Software, Services), by End User Verticals (Automotive, BFSI, Healthcare, Retail, Security, Other End-User Verticals), by Geography (North America, Europe, Asia Pacific, Latin America, Middle East and Africa). The Report Offers Market Forecasts and Size in Value (USD) for all the Above Segments.

Satellite Image Classification

## Overview

Satellite Image Classification is a dataset for classification tasks - it contains Objects annotations for 2,000 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Deepfake Image Classification

## Overview

Deepfake Image Classification is a dataset for classification tasks - it contains Face annotations for 9,869 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Image Recognition and Classification Technology market is experiencing robust growth, driven by increasing adoption across diverse sectors. While precise market size figures for 2025 aren't provided, considering the presence of major players like Google, Amazon, and Microsoft, alongside numerous specialized firms, a reasonable estimate for the 2025 market size would be in the range of $15 billion to $20 billion. This substantial value reflects the technology's integration into various applications, from automated quality control in manufacturing to advanced diagnostics in healthcare. The Compound Annual Growth Rate (CAGR), though unspecified, is likely to be in the high single digits or low double digits, fueled by continuous advancements in artificial intelligence (AI), deep learning, and the increasing availability of high-quality image data. Key growth drivers include the rising demand for automation, improved accuracy in image analysis, and the proliferation of connected devices generating vast amounts of visual data. Trends point towards increasing use of cloud-based solutions for image processing, the development of more efficient and robust algorithms, and a focus on addressing ethical concerns related to bias and privacy in image recognition systems. While challenges such as data security and the need for highly specialized expertise exist, the overall market trajectory remains positive. The market segmentation highlights the breadth of applications. Object detection, facial recognition, and optical character recognition (OCR) are leading segments, with significant demand from IT & Telecom, healthcare, and retail industries. The geographical distribution shows strong presence in North America and Europe, reflecting these regions' advanced technological infrastructure and higher adoption rates. However, rapidly developing economies in Asia-Pacific present significant growth opportunities. The competitive landscape is intensely competitive, featuring both established tech giants and specialized startups. The presence of numerous players indicates a dynamic market with ongoing innovation and consolidation. Future growth will depend on further improvements in algorithm accuracy, reducing computational costs, and addressing ethical concerns to ensure responsible implementation of this powerful technology.

The global Image Recognition market size was valued USD 51.12 billion in 2023 and is expected to rise to USD 164.53 billion by 2032 at a CAGR of 13.87%.

Snapshot img

AI Image Recognition Market Size 2024-2028

The AI image recognition market size is forecast to increase by USD 3.78 billion at a CAGR of 23.04% between 2023 and 2028.

The market is experiencing significant growth, driven by advancements in the medical imaging field and the increasing popularity of cloud-based image analysis solutions. The medical industry's reliance on accurate and efficient image analysis for diagnosis and treatment planning is fueling market growth. The integration of AI image recognition with the Internet of Things (IoT) and Industry 4.0 is expected to drive further growth in this market. Additionally, cloud-based image recognition solutions offer cost savings, scalability, and accessibility, making them an attractive option for businesses and organizations. However, concerns regarding data privacy and security are emerging challenges for the market. As more sensitive medical and personal images are being stored and analyzed in the cloud, ensuring strong security measures and regulatory compliance is essential to mitigate potential risks. Overall, the market is poised for continued growth, with advancements in technology and increasing adoption across various industries.

What will be the Size of the AI Image Recognition Market During the Forecast Period?

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The market is witnessing significant growth due to the increasing adoption of computer vision technology in various industries. AI-enhanced cameras, language recognition software, and deep learning models are driving the market's expansion. Financial transaction analysis, self-driving car algorithms, and healthcare, particularly diagnostic radiology, are some major applications. The market is also influenced by the integration of big data, the Internet of Things (IoT), Industry 4.0, machine learning, and deep learning models. Cloud computing and edge computing technologies are enabling real-time image recognition and analysis. Autonomous driving solutions, face identification, and social networking websites are other significant areas of application. Safety and security, facial recognition, airports, and security checkpoints are key end-users. Machine learning and 3D object detection are emerging trends in the market. Mobility solutions and quantum computing are also expected to provide new growth opportunities.

How is this AI Image Recognition Industry segmented and which is the largest segment?

The industry 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.

End-user

  BFSI
  Automotive
  Retail
  Security
  Others


Geography

  North America

    US


  APAC

    China
    Japan


  Europe

    Germany
    UK


  South America



  Middle East and Africa

By End-user Insights

The BFSI segment is estimated to witness significant growth during the forecast period. The technology plays a pivotal role in various sectors, particularly in vehicle performance monitoring with the use of high-resolution cameras and sensors. Computing devices and image recognition software, coupled with IT systems and neural networks, enable visual recognition systems to achieve high accuracy, efficiency, and versatility. This technology is extensively used in retail stores for inventory tracking and stock level management, preventing misplaced items and optimizing replenishment processes. In the BFSI sector, it is instrumental in personalizing customer communication, enhancing competitiveness, and automating monotonous tasks. Social networking websites, including Facebook, utilize this technology for face identification and removing fake accounts.
Airports and security checkpoints employ AI image recognition for safety and security purposes, including facial recognition and contactless solutions. Advancements in AI image recognition include quantum machine learning, 3D object detection, and quantum computing, which enable big data analytics and image databases to facilitate event detection, image reconstruction, and video tracking. Mobility solutions and gesture recognition further expand the application scope of AI image recognition technology.

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

The BFSI segment was valued at USD 297.30 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 63% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. Machine learning and deep learning models are powering cloud and edge computing technologies, enhancing autonomous driving solutions in the automotive sector. AI te

Common Objects Day and Night (CODaN) is an image classification dataset for zero-shot day-night domain adaptation / generalization.

The CODaN dataset consists of 15,500 224x224 colour images in 10 classes, with 1,550 images per class. There are 10,000 training images, 500 validation images, 2,500 daytime test images and 2,500 nighttime test images.

The dataset is collected from the excellent COCO, ImageNet and ExDark datasets. All images are filtered and cropped such that they have the same dimensions and are completely mutually exclusive, i.e. do not contain objects of different classes, nor do belong objects to multiple classes.

Dataset Card for Sudoku Image Recognition

Images of Sudoku puzzles for multilabel classification.

  Dataset Details







  Dataset Description

This dataset consists of 1400 labelled images of Sudoku puzzles. It is intended for training and evaluating a system that can automatically determine the state of each cell in the puzzle: whether it is solved or unsolved, and which digits it contains. The images are split into train (1000), val (200) and test (200).… See the full description on the dataset page: https://huggingface.co/datasets/Lexski/sudoku-image-recognition.

The LOCBEEF dataset contains 3268 images of local Aceh beef collected from 07:00 a.m - 22:00 p.m, more information about the clock is shown in Fig. The dataset contains two categories of directories, namely train, and test. Furthermore, each subdirectory consists of fresh and rotten. An example of the image can be seen in Figs. 2 and 3. The directory structure for the data is shown in Fig. 1. The image directory for train contains 2228 images each subdirectory contains 1114 images, and the test directory contains 980 images for each subdirectory containing 490 images. For images have a resolution of 176 x 144 pixel, 320 x 240 pixel, 640 x 480 pixel, 720 x 480 pixel, 720 x 720 pixel, 1280 x 720 pixel, 1920 x 1080 pixel, 2560 x 1920 pixel, 3120 x 3120 pixel, 3264 x 2248 pixel, and 4160 x 3120 pixel.

The classification of LOCBEEF datasets has been carried out using the deep learning method of Convolutional Neural Networks with an image composition of 70% training data and 30% test data. Images with the mentioned dimensions are included in the LOCBEEF dataset to apply to the Resnet50.

Stai Image Classification

## Overview

Stai Image Classification is a dataset for object detection tasks - it contains Different Classes annotations for 570 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset Description: Human Faces and Objects Dataset (HFO-5000) The Human Faces and Objects Dataset (HFO-5000) is a curated collection of 5,000 images, categorized into three distinct classes: male faces (1,500), female faces (1,500), and objects (2,000). This dataset is designed for machine learning and computer vision applications, including image classification, face detection, and object recognition. The dataset provides high-quality, labeled images with a structured CSV file for seamless integration into deep learning pipelines.

Column Description: The dataset is accompanied by a CSV file that contains essential metadata for each image. The CSV file includes the following columns: file_name: The name of the image file (e.g., image_001.jpg). label: The category of the image, with three possible values: "male" (for male face images) "female" (for female face images) "object" (for images of various objects) file_path: The full or relative path to the image file within the dataset directory.

Uniqueness and Key Features: 1) Balanced Distribution: The dataset maintains an even distribution of human faces (male and female) to minimize bias in classification tasks. 2) Diverse Object Selection: The object category consists of a wide variety of items, ensuring robustness in distinguishing between human and non-human entities. 3) High-Quality Images: The dataset consists of clear and well-defined images, suitable for both training and testing AI models. 4) Structured Annotations: The CSV file simplifies dataset management and integration into machine learning workflows. 5) Potential Use Cases: This dataset can be used for tasks such as gender classification, facial recognition benchmarking, human-object differentiation, and transfer learning applications.

Conclusion: The HFO-5000 dataset provides a well-structured, diverse, and high-quality set of labeled images that can be used for various computer vision tasks. Its balanced distribution of human faces and objects ensures fairness in training AI models, making it a valuable resource for researchers and developers. By offering structured metadata and a wide range of images, this dataset facilitates advancements in deep learning applications related to facial recognition and object classification.

According to Future Market Insights, image recognition in retail is expected to reach a market valuation of US$ 2.11 billion by the end of 2023, accelerating at a CAGR of 23% over the forecast period (2023 to 2033). Increasing numbers of retail outlets and online stores have led to rapid growth in the retail image recognition market.