Utilize our machine learning datasets to develop and validate your models. Our datasets are designed to support a variety of machine learning applications, from image recognition to natural language processing and recommendation systems. You can access a comprehensive dataset or tailor a subset to fit your specific requirements, using data from a combination of various sources and websites, including custom ones. Popular use cases include model training and validation, where the dataset can be used to ensure robust performance across different applications. Additionally, the dataset helps in algorithm benchmarking by providing extensive data to test and compare various machine learning algorithms, identifying the most effective ones for tasks such as fraud detection, sentiment analysis, and predictive maintenance. Furthermore, it supports feature engineering by allowing you to uncover significant data attributes, enhancing the predictive accuracy of your machine learning models for applications like customer segmentation, personalized marketing, and financial forecasting.

APISCRAPY's AI & ML training data is meticulously curated and labelled to ensure the best quality. Our training data comes from a variety of areas, including healthcare and banking, as well as e-commerce and natural language processing.

The benchmarking datasets used for deepBlink. The npz files contain train/valid/test splits inside and can be used directly. The files belong to the following challenges / classes:- ISBI Particle tracking challenge: microtubule, vesicle, receptor- Custom synthetic (based on http://smal.ws): particle- Custom fixed cell: smfish- Custom live cell: suntagThe csv files are to determine which image in the test splits correspond to which original image, SNR, and density.

This work applied remote sensing techniques based on unmanned aerial vehicles (UAVs) and deep learning (DL) to detect WLD in sugarcane fields at the Gal-Oya Plantation, Sri Lanka. The established methodology to detect WLD consists of UAV red, green, and blue (RGB) image acquisition, the pre-processing of the dataset, labelling, DL model tuning, and prediction.

Acknowledgements:

Narmilan Amarasingam conducted the UAV flight mission, and analysis and prepared the manuscript for final submission as a corresponding author.
Felipe Gonzalez, Kevin Powell, and Juan Sandino provided overall supervision and contributed to the writing and editing.
Surantha provided the technical guidance to conduct the UAV flight mission and research design and provided feedback on the draft manuscript.

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.

Ratinanet Deep Learning

## Overview

Ratinanet Deep Learning is a dataset for object detection tasks - it contains Breast Cancer annotations for 3,180 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).

Snapshot img

Deep Learning Market Size 2024-2028

The deep learning market size is forecast to increase by USD 10.85 billion at a CAGR of 26.06% between 2023 and 2028.

Deep learning technology is revolutionizing various industries, including healthcare. In the healthcare sector, deep learning is being extensively used for the diagnosis and treatment of musculoskeletal and inflammatory disorders. The market for deep learning services is experiencing significant growth due to the increasing availability of high-resolution medical images, electronic health records, and big data. Medical professionals are leveraging deep learning technologies for disease indications such as failure-to-success ratio, image interpretation, and biomarker identification solutions. Moreover, with the proliferation of data from various sources such as social networks, smartphones, and IoT devices, there is a growing need for advanced analytics techniques to make sense of this data. Companies In the market are collaborating to offer comprehensive information services and digital analytical solutions. However, the lack of technical expertise among medical professionals poses a challenge to the widespread adoption of deep learning technologies. The market is witnessing an influx of startups, which is intensifying the competition. Deep learning services are being integrated with compatible devices for image processing and prognosis. Molecular data analysis is another area where deep learning technologies are making a significant impact.

What will be the Size of the Deep Learning Market During the Forecast Period?

Request Free Sample

A subset of machine learning and artificial intelligence (AI), is a computational method inspired by the structure and function of the human brain. This technology utilizes neural networks, a type of machine learning model, to recognize patterns and learn from data. In the US market, deep learning is gaining significant traction due to its ability to process large amounts of data and extract meaningful insights. The market In the US is driven by several factors. One of the primary factors is the increasing availability of big data.
Moreover, with the proliferation of data from various sources such as social networks, smartphones, and IoT devices, there is a growing need for advanced analytics techniques to make sense of this data. Deep learning algorithms, with their ability to learn from vast amounts of data, are well-positioned to address this need. Another factor fueling the growth of the market In the US is the increasing adoption of cloud-based technology. Cloud-based solutions offer several advantages, including scalability, flexibility, and cost savings. These solutions enable organizations to process large datasets and train complex models without the need for expensive hardware.

How is this 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.

Application

  Image recognition
  Voice recognition
  Video surveillance and diagnostics
  Data mining


Type

  Software
  Services
  Hardware


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  South America



  Middle East and Africa

By Application Insights

The image recognition segment is estimated to witness significant growth during the forecast period.

In the realm of artificial intelligence (AI), image recognition holds significant value, particularly in sectors such as banking and finance (BFSI). This technology's ability to accurately identify and categorize images is invaluable, as extensive image repositories In these industries cannot be easily forged. BFSI firms utilize AI image recognition for various applications, including personalizing customer communication, maintaining a competitive edge, and automating repetitive tasks to boost productivity. For instance, social media platforms like Facebook employ this technology to correctly identify and assign images to the right user account with an impressive accuracy rate of approximately 98%. Moreover, AI image recognition plays a crucial role in eliminating fraudulent social media accounts.

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

The image recognition segment was valued at USD 1.05 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 36% 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.

For more insights on the market share of various regions, Request Free S

Snapshot img

US Deep Learning Market Size 2025-2029

The deep learning market size in US is forecast to increase by USD 5.02 billion at a CAGR of 30.1% between 2024 and 2029.

The deep learning market is experiencing robust growth, driven by the increasing adoption of artificial intelligence (AI) in various industries for advanced solutioning. This trend is fueled by the availability of vast amounts of data, which is a key requirement for deep learning algorithms to function effectively. Industry-specific solutions are gaining traction, as businesses seek to leverage deep learning for specific use cases such as image and speech recognition, fraud detection, and predictive maintenance. Alongside, intuitive data visualization tools are simplifying complex neural network outputs, helping stakeholders understand and validate insights. 


However, challenges remain, including the need for powerful computing resources, data privacy concerns, and the high cost of implementing and maintaining deep learning systems. Despite these hurdles, the market's potential for innovation and disruption is immense, making it an exciting space for businesses to explore further. Semi-supervised learning, data labeling, and data cleaning facilitate efficient training of deep learning models. Cloud analytics is another significant trend, as companies seek to leverage cloud computing for cost savings and scalability. 

What will be the Size of the market During the Forecast Period?

Request Free Sample

Deep learning, a subset of machine learning, continues to shape industries by enabling advanced applications such as image and speech recognition, text generation, and pattern recognition. Reinforcement learning, a type of deep learning, gains traction, with deep reinforcement learning leading the charge. Anomaly detection, a crucial application of unsupervised learning, safeguards systems against security vulnerabilities. Ethical implications and fairness considerations are increasingly important in deep learning, with emphasis on explainable AI and model interpretability. Graph neural networks and attention mechanisms enhance data preprocessing for sequential data modeling and object detection. Time series forecasting and dataset creation further expand deep learning's reach, while privacy preservation and bias mitigation ensure responsible use.

In summary, deep learning's market dynamics reflect a constant pursuit of innovation, efficiency, and ethical considerations. The Deep Learning Market in the US is flourishing as organizations embrace intelligent systems powered by supervised learning and emerging self-supervised learning techniques. These methods refine predictive capabilities and reduce reliance on labeled data, boosting scalability. BFSI firms utilize AI image recognition for various applications, including personalizing customer communication, maintaining a competitive edge, and automating repetitive tasks to boost productivity. Sophisticated feature extraction algorithms now enable models to isolate patterns with high precision, particularly in applications such as image classification for healthcare, security, and retail.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 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

  North America

    US

By Application Insights

The Image recognition segment is estimated to witness significant growth during the forecast period. In the realm of artificial intelligence (AI) and machine learning, image recognition, a subset of computer vision, is gaining significant traction. This technology utilizes neural networks, deep learning models, and various machine learning algorithms to decipher visual data from images and videos. Image recognition is instrumental in numerous applications, including visual search, product recommendations, and inventory management. Consumers can take photographs of products to discover similar items, enhancing the online shopping experience. In the automotive sector, image recognition is indispensable for advanced driver assistance systems (ADAS) and autonomous vehicles, enabling the identification of pedestrians, other vehicles, road signs, and lane markings.

Furthermore, image recognition plays a pivotal role in augmented reality (AR) and virtual reality (VR) applications, where it tracks physical objects and overlays digital content onto real-world scenarios. The model training process involves the backpropagation algorithm, which calculates the loss fu

The Geometric Shapes Dataset for Deep Learning includes 20,000 high-quality images of circles, squares, triangles, rectangles, ellipses, octagons, parallelograms, pentagons, and rhombuses in multiple colors. It is designed for AI, ML, and computer vision applications such as shape recognition and object detection.

This dataset contains adversarial attacks on Deep Learning (DL) when it is employed for the classification of wireless modulated communication signals. The attack is executed with an obfuscating waveform that is embedded in the transmitted signal in such a way that prevents the extraction of clean data for training from a wireless eavesdropper. At the same time it allows a legitimate receiver (LRx) to demodulate the data.

UMA CV Deep Learning

## Overview

UMA CV Deep Learning is a dataset for object detection tasks - it contains Buoys annotations for 748 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).

Acute poisoning is a significant global health burden, and the causative agent is often unclear. The primary aim of this pilot study was to develop a deep learning algorithm that predicts the most probable agent a poisoned patient was exposed to from a pre-specified list of drugs. Data were queried from the National Poison Data System (NPDS) from 2014 through 2018 for eight single-agent poisonings (acetaminophen, diphenhydramine, aspirin, calcium channel blockers, sulfonylureas, benzodiazepines, bupropion, and lithium). Two Deep Neural Networks (PyTorch and Keras) designed for multi-class classification tasks were applied. There were 201,031 single-agent poisonings included in the analysis. For distinguishing among selected poisonings, PyTorch model had specificity of 97%, accuracy of 83%, precision of 83%, recall of 83%, and a F1-score of 82%. Keras had specificity of 98%, accuracy of 83%, precision of 84%, recall of 83%, and a F1-score of 83%. The best performance was achieved in the diagnosis of single-agent poisoning in diagnosing poisoning by lithium, sulfonylureas, diphenhydramine, calcium channel blockers, then acetaminophen, in PyTorch (F1-score = 99%, 94%, 85%, 83%, and 82%, respectively) and Keras (F1-score = 99%, 94%, 86%, 82%, and 82%, respectively). Deep neural networks can potentially help in distinguishing the causative agent of acute poisoning. This study used a small list of drugs, with polysubstance ingestions excluded.Reproducible source code and results can be obtained at https://github.com/ashiskb/npds-workspace.git.

The Deep Learning track focuses on IR tasks where a large training set is available, allowing us to compare a variety of retrieval approaches including deep neural networks and strong non-neural approaches, to see what works best in a large-data regime.

SYNERGY is a free and open dataset on study selection in systematic reviews, comprising 169,288 academic works from 26 systematic reviews. Only 2,834 (1.67%) of the academic works in the binary classified dataset are included in the systematic reviews. This makes the SYNERGY dataset a unique dataset for the development of information retrieval algorithms, especially for sparse labels. Due to the many available variables available per record (i.e. titles, abstracts, authors, references, topics), this dataset is useful for researchers in NLP, machine learning, network analysis, and more. In total, the dataset contains 82,668,134 trainable data points. The easiest way to get the SYNERGY dataset is via the synergy-dataset Python package. See https://github.com/asreview/synergy-dataset for all information.

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.

The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER - Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,527 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (number of trainable parameters), 8 network "shapes", and 14 depths on both CPU and GPU hardware collected using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use, and highlights the impact of cache effects.

BUTTER-E is intended to be joined with the BUTTER dataset (see "BUTTER - Empirical Deep Learning Dataset on OEDI" resource below) which characterizes the performance of 483k distinct fully connected neural networks but does not include energy measurements.

What this collection is: A curated, binary-classified image dataset of grayscale (1 band) 400 x 400-pixel size, or image chips, in a JPEG format extracted from processed Sentinel-1 Synthetic Aperture Radar (SAR) satellite scenes acquired over various regions of the world, and featuring clear open ocean chips, look-alikes (wind or biogenic features) and oil slick chips.

This binary dataset contains chips labelled as: - "0" for chips not containing any oil features (look-alikes or clean seas)
- "1" for those containing oil features.

This binary dataset is imbalanced, and biased towards "0" labelled chips (i.e., no oil features), which correspond to 66% of the dataset. Chips containing oil features, labelled "1", correspond to 34% of the dataset.

Why: This dataset can be used for training, validation and/or testing of machine learning, including deep learning, algorithms for the detection of oil features in SAR imagery. Directly applicable for algorithm development for the European Space Agency Sentinel-1 SAR mission (https://sentinel.esa.int/web/sentinel/missions/sentinel-1 ), it may be suitable for the development of detection algorithms for other SAR satellite sensors.

Overview of this dataset: Total number of chips (both classes) is N=5,630 Class 0 1 Total 3,725 1,905

Further information and description is found in the ReadMe file provided (ReadMe_Sentinel1_SAR_OilNoOil_20221215.txt)

This systematic review provides a state-of-art of Artificial Intelligence (AI) models such as Machine Learning (ML) and Deep Learning (DL) development and its applications in Mexico in diverse fields. These models are recognized as powerful tools in many fields due to their capability to carry out several tasks such as forecasting, image classification, recognition, natural language processing, machine translation, etc. This review article aimed to provide comprehensive information on the Machine Learning and Deep Learning algorithms applied in Mexico. A total of 120 original research papers were included and details such as trends in publication, spatial location, institutions, publishing issues, subject areas, algorithms applied, and performance metrics were discussed. Furthermore, future directions and opportunities are presented. A total of 15 subject areas were identified, where Social Sciences and Medicine were the main application areas. It observed that Artificial Neural Networks (ANN) models were preferred, probably due to their capability to learn and model non-linear and complex relationships in addition to other popular models such as Random Forest (RF) and Support Vector Machines (SVM). It identified that the selection and application of the algorithms rely on the study objective and the data patterns. Regarding the performance metrics applied, accuracy and recall were the most employed. This paper could assist the readers in understanding the several Machine Learning and Deep Learning techniques used and their subject area of application in the Artificial Intelligence field in the country. Moreover, the study could provide significant knowledge in the development and implementation of a national AI strategy, according to country needs.

Deep Learning in Security Market Outlook

The global Deep Learning in Security market size is projected to grow from USD 1.3 billion in 2023 to USD 6.4 billion by 2032, at a Compound Annual Growth Rate (CAGR) of 19.6% during the forecast period. The rapid increase in cyber threats and the growing sophistication of attacks are significant growth factors driving this market. The necessity to secure data in a world that is increasingly digital is compelling organizations to adopt deep learning technologies to enhance their security systems, ensuring robust protection against potential breaches.

The increasing digitization across various sectors such as BFSI, healthcare, and IT and telecommunications is contributing significantly to the market's growth. As organizations move towards digital transformation, the complexity and volume of data increase, necessitating advanced security measures. Deep learning, with its ability to analyze vast amounts of data and identify patterns, offers an effective solution for detecting and mitigating potential security threats in real-time. Moreover, the growing adoption of Internet of Things (IoT) devices has expanded the attack surface, driving the need for more sophisticated security solutions powered by deep learning.

Another critical growth factor is the rising awareness regarding the limitations of traditional security measures. Conventional security methods, which are primarily rule-based, struggle to keep up with the evolving nature of cyber threats. Deep learning security systems, on the other hand, can learn and adapt to new attack vectors, making them more efficient in identifying and preventing sophisticated cyber-attacks. Additionally, the increasing investments in artificial intelligence (AI) and machine learning (ML) by both public and private sectors are further propelling the adoption of deep learning in security.

The regulatory landscape also plays a crucial role in the growth of the deep learning in security market. Governments worldwide are implementing stringent data protection and cybersecurity regulations, pushing organizations to adopt advanced security technologies. Compliance with such regulations necessitates the deployment of robust security measures, including those powered by deep learning. The market's growth is also supported by technological advancements in AI and ML, making deep learning models more accessible and efficient.

Artificial Intelligence in Security is becoming an integral part of modern cybersecurity strategies. As cyber threats become more sophisticated, AI technologies are being leveraged to enhance security measures, providing real-time threat detection and response. AI's ability to analyze vast amounts of data and identify patterns allows it to detect anomalies that might indicate a security breach. This capability is crucial in an era where cyber-attacks are not only more frequent but also more complex. By integrating AI into security systems, organizations can proactively address potential threats, reducing the risk of data breaches and ensuring the safety of their digital assets. The continuous advancements in AI technology are making security solutions more robust and efficient, enabling organizations to stay ahead of emerging threats.

Regionally, North America is expected to dominate the deep learning in security market, owing to the presence of major technology companies and high adoption rates of advanced security solutions. Asia-Pacific is anticipated to witness significant growth, driven by the increasing digitization and rising cyber threats in the region. Europe also presents substantial opportunities due to stringent data protection regulations such as GDPR, which necessitate advanced security measures.

Component Analysis

The Deep Learning in Security market can be segmented by component into Software, Hardware, and Services. The software segment is expected to hold the largest market share due to its vital role in deploying deep learning models for security purposes. Software solutions, encompassing algorithms, platforms, and security management systems, are indispensable for analyzing data and identifying threats. Their adaptability and scalability make them preferable for organizations of all sizes, contributing significantly to market growth. Moreover, the continuous advancements in algorithms and the development of new software tools are enhancing the efficiency and effectiveness of security solutions.