Sure! I'd be happy to provide you with an introduction to machine learning basics for beginners. Machine learning is a subfield of artificial intelligence (AI) that focuses on enabling computers to learn and make predictions or decisions without being explicitly programmed. Here are some key concepts and terms to help you get started:

1. Supervised Learning: In supervised learning, the machine learning algorithm learns from labeled training data. The training data consists of input examples and their corresponding correct output or target values. The algorithm learns to generalize from this data and make predictions or classify new, unseen examples.

2. Unsupervised Learning: Unsupervised learning involves learning patterns and relationships from unlabeled data. Unlike supervised learning, there are no target values provided. Instead, the algorithm aims to discover inherent structures or clusters in the data.

3. Training Data and Test Data: Machine learning models require a dataset to learn from. The dataset is typically split into two parts: the training data and the test data. The model learns from the training data, and the test data is used to evaluate its performance and generalization ability.

4. Features and Labels: In supervised learning, the input examples are often represented by features or attributes. For example, in a spam email classification task, features might include the presence of certain keywords or the length of the email. The corresponding output or target values are called labels, indicating the class or category to which the example belongs (e.g., spam or not spam).

5. Model Evaluation Metrics: To assess the performance of a machine learning model, various evaluation metrics are used. Common metrics include accuracy (the proportion of correctly predicted examples), precision (the proportion of true positives among all positive predictions), recall (the proportion of true positives predicted correctly), and F1 score (a combination of precision and recall).

6. Overfitting and Underfitting: Overfitting occurs when a model becomes too complex and learns to memorize the training data instead of generalizing well to unseen examples. On the other hand, underfitting happens when a model is too simple and fails to capture the underlying patterns in the data. Balancing the complexity of the model is crucial to achieve good generalization.

7. Feature Engineering: Feature engineering involves selecting or creating relevant features that can help improve the performance of a machine learning model. It often requires domain knowledge and creativity to transform raw data into a suitable representation that captures the important information.

8. Bias and Variance Trade-off: The bias-variance trade-off is a fundamental concept in machine learning. Bias refers to the errors introduced by the model's assumptions and simplifications, while variance refers to the model's sensitivity to small fluctuations in the training data. Reducing bias may increase variance and vice versa. Finding the right balance is important for building a well-performing model.

9. Supervised Learning Algorithms: There are various supervised learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), and neural networks. Each algorithm has its own strengths, weaknesses, and specific use cases.

10. Unsupervised Learning Algorithms: Unsupervised learning algorithms include clustering algorithms like k-means clustering and hierarchical clustering, dimensionality reduction techniques like principal component analysis (PCA) and t-SNE, and anomaly detection algorithms, among others.

These concepts provide a starting point for understanding the basics of machine learning. As you delve deeper, you can explore more advanced topics such as deep learning, reinforcement learning, and natural language processing. Remember to practice hands-on with real-world datasets to gain practical experience and further refine your skills.

Market Size and Growth: The global market for Machine Learning (ML) in Chip Design is projected to reach USD 19.7 billion by 2033, registering a CAGR of 25.2% from 2025 to 2033. This growth is attributed to the increasing demand for faster, more power-efficient chips and the ability of ML to automate and optimize the chip design process. Key drivers include the need to reduce design time and cost, improve performance, and address emerging technologies such as AI and IoT. Market Segmentation and Trends: Based on type, supervised learning is expected to dominate the market due to its wide applications in chip design, including design rule checking, yield prediction, and fault diagnosis. Semi-supervised learning is gaining traction as it combines labeled and unlabeled data for training, offering improved accuracy. Unsupervised learning and reinforcement learning are also finding use in chip design, particularly in areas such as auto layout and routing. Major chipmakers such as Intel, NVIDIA, and Cadence Design Systems are investing heavily in ML technologies to enhance their chip design capabilities. Additionally, the adoption of ML in foundries is growing as they seek to improve yield and efficiency for their customers. This comprehensive report provides an in-depth analysis of the Machine Learning in Chip Design market, offering insights into key market dynamics, regional trends, growth drivers, and competitive landscapes. Covering the period from 2023 to 2029, the report forecasts market size and growth to assist businesses in making strategic decisions and capturing untapped opportunities.

Reaching the performance of fully supervised learning with unlabeled data and only labeling one sample per class might be ideal for deep learning applications. We demonstrate for the first time the potential for building one-shot semi-supervised (BOSS) learning on CIFAR-10 and SVHN up to attain test accuracies that are comparable to fully supervised learning. Our method combines class prototype refining, class balancing, and self-training. A good prototype choice is essential and we propose a technique for obtaining iconic examples. In addition, we demonstrate that class balancing methods substantially improve accuracy results in semi-supervised learning to levels that allow self-training to reach the level of fully supervised learning performance. Our experiments demonstrate the value with computing and analyzing test accuracies for every class, rather than only a total test accuracy. We show that our BOSS methodology can obtain total test accuracies with CIFAR-10 images and only one labeled sample per class up to 95% (compared to 94.5% for fully supervised). Similarly, the SVHN images obtains test accuracies of 97.8%, compared to 98.27% for fully supervised. Rigorous empirical evaluations provide evidence that labeling large datasets is not necessary for training deep neural networks. Our code is available at https://github.com/lnsmith54/BOSS to facilitate replication.

Context

STL-10 is an image recognition dataset inspired by CIFAR-10 dataset with some improvements. With a corpus of 100,000 unlabeled images and 500 training images, this dataset is best for developing unsupervised feature learning, deep learning, self-taught learning algorithms. Unlike CIFAR-10, the dataset has a higher resolution which makes it a challenging benchmark for developing more scalable unsupervised learning methods.

Content

Data overview:

• There are three files: train_image.zips, test_images.zip and unlabeled_images.zip
• 10 classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck
• Images are 96x96 pixels, color
• 500 training images (10 pre-defined folds), 800 test images per class
• 100,000 unlabeled images for unsupervised learning. These examples are extracted from a similar but broader distribution of images. For instance, it contains other types of animals (bears, rabbits, etc.) and vehicles (trains, buses, etc.) in addition to the ones in the labeled set
• Images were acquired from labeled examples on ImageNet

The original data source recommends the following standardized testing protocol for reporting results:

1. Perform unsupervised training on the unlabeled data
2. Perform supervised training on the labeled data using 10 (pre-defined) folds of 100 examples from the training data. The indices of the examples to be used for each fold are provided
3. Report average accuracy on the full test set

Acknowledgements

Original data source and banner image: https://cs.stanford.edu/~acoates/stl10/

Please cite the following reference when using this dataset:

Adam Coates, Honglak Lee, Andrew Y. Ng An Analysis of Single Layer Networks in Unsupervised Feature Learning AISTATS, 2011.

Inspiration

• Can you train a model to accurately identify what animal or transportation object is in each image?

Image segmentation holds broad applications in medical image analysis, providing crucial support to doctors in both automatic diagnosis and computer-assisted interventions. The heterogeneity observed across various medical image datasets necessitates the training of task-specific segmentation models. However, effectively supervising the training of deep learning segmentation models typically demands dense label masks, a requirement that becomes challenging due to the constraints posed by privacy and cost issues in collecting large-scale medical datasets. These challenges collectively give rise to the limited annotations problems in medical image segmentation.

In this dissertation, we address the challenges posed by annotation deficiencies through a comprehensive exploration of various strategies. Firstly, we employ self-supervised learning to extract information from unlabeled data, presenting a tailored self-supervised method designed specifically for convolutional neural networks and 3D Vision Transformers. Secondly, our attention shifts to domain adaptation problems, leveraging images with similar content but in different modalities. We introduce the use of contrastive loss as a shape constraint in our image translation framework, resulting in both improved performance and enhanced training robustness. Thirdly, we incorporate diffusion models for data augmentation, expanding datasets with generated image-label pairs. Lastly, we explore to extract segmentation masks from image-level annotations alone. We propose a multi-task training framework for ECG abnormal beats localization and a conditional diffusion-based algorithm for tumor detection.

Data and metadata used in "Machine learning reveals the waggle drift’s role in the honey bee dance communication system"

All timestamps are given in ISO 8601 format.

The following files are included:

Berlin2019_waggle_phases.csv, Berlin2021_waggle_phases.csv

Automatic individual detections of waggle phases during our recording periods in 2019 and 2021.

timestamp: Date and time of the detection.

cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).

x_median, y_median: Median position of the bee during the waggle phase (for 2019 given in millimeters after applying a homography, for 2021 in the original image coordinates).

waggle_angle: Body orientation of the bee during the waggle phase in radians (0: oriented to the right, PI / 4: oriented upwards).

Berlin2019_dances.csv

Automatic detections of dance behavior during our recording period in 2019.

dancer_id: Unique ID of the individual bee.

dance_id: Unique ID of the dance.

ts_from, ts_to: Date and time of the beginning and end of the dance.

cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).

median_x, median_y: Median position of the individual during the dance.

feeder_cam_id: ID of the feeder that the bee was detected at prior to the dance.

Berlin2019_followers.csv

Automatic detections of attendance and following behavior, corresponding to the dances in Berlin2019_dances.csv.

dance_id: Unique ID of the dance being attended or followed.

follower_id: Unique ID of the individual attending or following the dance.

ts_from, ts_to: Date and time of the beginning and end of the interaction.

label: “attendance” or “follower”

cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).

Berlin2019_dances_with_manually_verified_times.csv

A sample of dances from Berlin2019_dances.csv where the exact timestamps have been manually verified to correspond to the beginning of the first and last waggle phase down to a precision of ca. 166 ms (video material was recorded at 6 FPS).

dance_id: Unique ID of the dance.

dancer_id: Unique ID of the dancing individual.

cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).

feeder_cam_id: ID of the feeder that the bee was detected at prior to the dance.

dance_start, dance_end: Manually verified date and times of the beginning and end of the dance.

Berlin2019_dance_classifier_labels.csv

Manually annotated waggle phases or following behavior for our recording season in 2019 that was used to train the dancing and following classifier. Can be merged with the supplied individual detections.

timestamp: Timestamp of the individual frame the behavior was observed in.

frame_id: Unique ID of the video frame the behavior was observed in.

bee_id: Unique ID of the individual bee.

label: One of “nothing”, “waggle”, “follower”

Berlin2019_dance_classifier_unlabeled.csv

Additional unlabeled samples of timestamp and individual ID with the same format as Berlin2019_dance_classifier_labels.csv, but without a label. The data points have been sampled close to detections of our waggle phase classifier, so behaviors related to the waggle dance are likely overrepresented in that sample.

Berlin2021_waggle_phase_classifier_labels.csv

Manually annotated detections of our waggle phase detector (bb_wdd2) that were used to train the neural network filter (bb_wdd_filter) for the 2021 data.

detection_id: Unique ID of the waggle phase.

label: One of “waggle”, “activating”, “ventilating”, “trembling”, “other”. Where “waggle” denoted a waggle phase, “activating” is the shaking signal, “ventilating” is a bee fanning her wings. “trembling” denotes a tremble dance, but the distinction from the “other” class was often not clear, so “trembling” was merged into “other” for training.

orientation: The body orientation of the bee that triggered the detection in radians (0: facing to the right, PI /4: facing up).

metadata_path: Path to the individual detection in the same directory structure as created by the waggle dance detector.

Berlin2021_waggle_phase_classifier_ground_truth.zip

The output of the waggle dance detector (bb_wdd2) that corresponds to Berlin2021_waggle_phase_classifier_labels.csv and is used for training. The archive includes a directory structure as output by the bb_wdd2 and each directory includes the original image sequence that triggered the detection in an archive and the corresponding metadata. The training code supplied in bb_wdd_filter directly works with this directory structure.

Berlin2019_tracks.zip

Detections and tracks from the recording season in 2019 as produced by our tracking system. As the full data is several terabytes in size, we include the subset of our data here that is relevant for our publication which comprises over 46 million detections. We included tracks for all detected behaviors (dancing, following, attending) including one minute before and after the behavior. We also included all tracks that correspond to the labeled and unlabeled data that was used to train the dance classifier including 30 seconds before and after the data used for training. We grouped the exported data by date to make the handling easier, but to efficiently work with the data, we recommend importing it into an indexable database.

The individual files contain the following columns:

cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).

timestamp: Date and time of the detection.

frame_id: Unique ID of the video frame of the recording from which the detection was extracted.

track_id: Unique ID of an individual track (short motion path from one individual). For longer tracks, the detections can be linked based on the bee_id.

bee_id: Unique ID of the individual bee.

bee_id_confidence: Confidence between 0 and 1 that the bee_id is correct as output by our tracking system.

x_pos_hive, y_pos_hive: Spatial position of the bee in the hive on the side indicated by cam_id. Given in millimeters after applying a homography on the video material.

orientation_hive: Orientation of the bees’ thorax in the hive in radians (0: oriented to the right, PI / 4: oriented upwards).

Berlin2019_feeder_experiment_log.csv

Experiment log for our feeder experiments in 2019.

date: Date given in the format year-month-day.

feeder_cam_id: Numeric ID of the feeder.

coordinates: Longitude and latitude of the feeder. For feeders 1 and 2 this is only given once and held constant. Feeder 3 had varying locations.

time_opened, time_closed: Date and time when the feeder was set up or closed again. sucrose_solution: Concentration of the sucrose solution given as sugar:water (in terms of weight). On days where feeder 3 was open, the other two feeders offered water without sugar.

Software used to acquire and analyze the data:

bb_pipeline: Tag localization and decoding pipeline

bb_pipeline_models: Pretrained localizer and decoder models for bb_pipeline

bb_binary: Raw detection data storage format

bb_irflash: IR flash system schematics and arduino code

bb_imgacquisition: Recording and network storage

bb_behavior: Database interaction and data (pre)processing, feature extraction

bb_tracking: Tracking of bee detections over time

bb_wdd2: Automatic detection and decoding of honey bee waggle dances

bb_wdd_filter: Machine learning model to improve the accuracy of the waggle dance detector

bb_dance_networks: Detection of dancing and following behavior from trajectories

Transvaginal ultrasound is the preferred method for visualizing the cervix in most patients, offering detailed insight into cervical anatomy and structure. Accurate segmentation of ultrasound (US) images of the cervical muscles is essential for analyzing deep muscle structures, assessing their function, and monitoring treatment protocols tailored to individual patients.

The manual annotation of cervical structures in transvaginal ultrasound images is labor-intensive and time-consuming, limiting the availability of large labeled datasets required for robust machine learning models. In response to this challenge, semi supervised learning approaches have shown potential by leveraging both labeled and unlabeled data, enabling the extraction of useful information from unannotated cases. This method could reduce the need for extensive manual annotation while maintaining accuracy, thus accelerating the development of automated cervical image segmentation systems. The envisioned impact of this challenge is twofold: improving clinical decision-making through more accessible and accurate diagnostic tools and advancing machine learning techniques for medical image analysis, particularly in resource-constrained environments.

We extend the MICCAI PSFHS 2023 Challenge and the MICCAI IUGC 2024 Challenge from fully supervised settings to a semi-supervised setting that focuses on how to use unlabeled data.

Training/Validation/Test=500/90/300

The dataset can be accessible after signing the data-sharing agreement and sending it to the organizer (fugc.isbi25@gmail.com).

Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable performance on many computer vision tasks. Due to their large parameter space, they require many labeled samples when trained in a supervised setting. The costs of annotating data manually can render the use of DCNNs infeasible. We present a novel framework called RenderGAN that can generate large amounts of realistic, labeled images by combining a 3D model and the Generative Adversarial Network framework. In our approach, image augmentations (e.g., lighting, background, and detail) are learned from unlabeled data such that the generated images are strikingly realistic while preserving the labels known from the 3D model. We apply the RenderGAN framework to generate images of barcode-like markers that are attached to honeybees. Training a DCNN on data generated by the RenderGAN yields considerably better performance than training it on various baselines.

Weed Detection (Unsupervised + Supervised Learning)

Overview

This dataset is designed to support both supervised and unsupervised learning for the task of weed detection in crop fields. It provides labeled data in YOLO format suitable for training object detection models, unlabeled data for semi-supervised or unsupervised learning, and a separate test set for evaluation. The objective is to detect and distinguish between weed and crop instances using deep learning models like YOLOv5 or YOLOv8.

Dataset Structure

│ ├── labeled/ │ ├── images/ # Labeled images for training │ └── labels/ # YOLO-format annotations │ ├── unlabeled/ # Unlabeled images for unsupervised or semi-supervised learning │ └── test/ ├── images/ # Test images └── labels/ # Ground truth annotations in YOLO format

Video Dataset Market Outlook

Based on our latest research, the global video dataset market size reached USD 2.1 billion in 2024 and is projected to grow at a robust CAGR of 19.7% during the forecast period, reaching a value of USD 10.3 billion by 2033. This remarkable growth trajectory is driven by the increasing adoption of artificial intelligence and machine learning technologies, which heavily rely on high-quality video datasets for training and validation purposes. As organizations across industries seek to leverage advanced analytics and automation, the demand for comprehensive, well-annotated video datasets is accelerating rapidly, establishing the video dataset market as a critical enabler for next-generation digital solutions.

One of the primary growth factors propelling the video dataset market is the exponential rise in the deployment of computer vision applications across diverse sectors. Industries such as automotive, healthcare, retail, and security are increasingly integrating AI-powered vision systems for tasks ranging from autonomous navigation and medical diagnostics to customer behavior analysis and surveillance. The effectiveness of these systems hinges on the availability of large, diverse, and accurately labeled video datasets that can be used to train robust machine learning models. With the proliferation of video-enabled devices and sensors, the volume of raw video data has surged, further fueling the need for curated datasets that can be harnessed to unlock actionable insights and drive automation.

Another significant driver for the video dataset market is the growing emphasis on data-driven research and innovation within academic, commercial, and governmental institutions. Universities and research organizations are leveraging video datasets to advance studies in areas such as robotics, behavioral science, and smart city development. Similarly, commercial entities are utilizing these datasets to enhance product offerings, improve customer experiences, and gain a competitive edge through AI-driven solutions. Government and defense agencies are also investing in video datasets to bolster national security, surveillance, and public safety initiatives. This broad-based adoption across end-users is catalyzing the expansion of the video dataset market, as stakeholders recognize the strategic value of high-quality video data in driving technological progress and operational efficiency.

The emergence of synthetic and augmented video datasets represents a transformative trend within the market, addressing challenges related to data scarcity, privacy, and bias. Synthetic datasets, generated using advanced simulation and generative AI techniques, enable organizations to create vast amounts of labeled video data tailored to specific scenarios without the need for extensive real-world data collection. This approach not only accelerates model development but also enhances data diversity and mitigates ethical concerns associated with using sensitive or personally identifiable information. As the technology for generating and validating synthetic video data matures, its adoption is expected to further accelerate, opening new avenues for innovation and market growth.

Regionally, North America continues to dominate the video dataset market, accounting for the largest share in 2024 due to its advanced technological ecosystem, strong presence of leading AI companies, and substantial investments in research and development. However, the Asia Pacific region is witnessing the fastest growth, driven by rapid digital transformation, increasing adoption of AI in sectors like manufacturing and healthcare, and supportive government policies. Europe also represents a significant market, characterized by its focus on data privacy and regulatory compliance, which is shaping the development and utilization of video datasets across industries. These regional dynamics underscore the global nature of the video dataset market and highlight the diverse opportunities for stakeholders worldwide.

Dataset Type Analysis

The video dataset market is segmented by dataset type into labeled, unlabeled, and synthetic datasets, each serving distinct purposes and addressing unique industry requirements. Labeled video datasets are foundational for supervised learning applications, where annotated frames and sequences enable machine learning models to learn complex patterns and behaviors. The demand for labeled datasets is particularly high in sectors

To analyse large numbers of texts, social science researchers are increasingly confronting the challenge of text classification. When manual labeling is not possible and researchers have to find automatized ways to classify texts, computer science provides a useful toolbox of machine-learning methods whose performance remains understudied in the social sciences. In this article, we compare the performance of the most widely used text classifiers by applying them to a typical research scenario in social science research: a relatively small labeled dataset with infrequent occurrence of categories of interest, which is a part of a large unlabeled dataset. As an example case, we look at Twitter communication regarding climate change, a topic of increasing scholarly interest in interdisciplinary social science research. Using a novel dataset including 5,750 tweets from various international organizations regarding the highly ambiguous concept of climate change, we evaluate the performance of methods in automatically classifying tweets based on whether they are about climate change or not. In this context, we highlight two main findings. First, supervised machine-learning methods perform better than state-of-the-art lexicons, in particular as class balance increases. Second, traditional machine-learning methods, such as logistic regression and random forest, perform similarly to sophisticated deep-learning methods, whilst requiring much less training time and computational resources. The results have important implications for the analysis of short texts in social science research.

Weak Supervision for AI Market Outlook

According to our latest research, the global Weak Supervision for AI market size reached USD 1.62 billion in 2024, reflecting a robust expansion in the adoption of advanced AI training methodologies. The market is projected to exhibit a remarkable CAGR of 32.1% from 2025 to 2033, positioning the sector to attain a value of USD 20.09 billion by 2033. This exponential growth is driven by the accelerating need for scalable, cost-effective, and high-quality labeled data to train sophisticated AI models, particularly as organizations across industries seek more efficient alternatives to traditional manual annotation processes.

One of the primary growth factors fueling the weak supervision for AI market is the escalating complexity and scale of AI models, which require vast amounts of labeled data for effective training. Traditional supervised learning methods, which depend on meticulously labeled datasets, are increasingly becoming impractical due to the time, cost, and human resource constraints involved. Weak supervision offers a compelling solution by enabling the use of imperfect, noisy, or partially labeled data, thus dramatically reducing the time and expense associated with data annotation. This paradigm shift is particularly attractive for enterprises aiming to accelerate AI deployment cycles and maintain a competitive edge in rapidly evolving markets. The integration of weak supervision frameworks with existing machine learning pipelines further enhances the appeal, as organizations can leverage legacy data assets and incorporate domain expertise with minimal disruption.

Another significant driver is the growing adoption of AI across diverse verticals such as healthcare, finance, retail, and automotive, each with unique data annotation challenges. In healthcare, for example, the availability of labeled medical images or patient records is often limited by privacy concerns and expert availability. Weak supervision enables the extraction of valuable insights from partially labeled or unlabeled datasets, facilitating breakthroughs in disease diagnosis, drug discovery, and personalized medicine. Similarly, in financial services, weak supervision techniques help in fraud detection and risk assessment by leveraging large volumes of transactional and behavioral data. The scalability and adaptability of weak supervision methodologies are positioning them as indispensable tools in the toolkit of data scientists and AI engineers worldwide.

Technological advancements in natural language processing, computer vision, and speech recognition are also contributing to the surge in weak supervision demand. As AI models become more sophisticated and are applied to increasingly complex tasks, the need for nuanced and context-aware training data grows. Weak supervision frameworks enable the aggregation of multiple noisy sources—such as heuristic rules, crowdsourced labels, and external knowledge bases—into coherent training signals. This not only improves model accuracy but also enhances the interpretability and robustness of AI systems. The proliferation of open-source tools and platforms dedicated to weak supervision is further democratizing access, allowing organizations of all sizes to experiment and innovate without prohibitive upfront investments.

From a regional perspective, North America currently leads the global weak supervision for AI market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the presence of leading technology companies, a mature AI research ecosystem, and substantial investments in data infrastructure. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digital transformation, government initiatives supporting AI adoption, and a burgeoning startup landscape. Europe, with its strong regulatory focus on data privacy and ethics, is also witnessing increased uptake of weak supervision techniques as organizations seek to balance innovation with compliance. The Middle East & Africa and Latin America are gradually catching up, propelled by investments in smart city projects and the modernization of legacy IT systems.

This dataset was collected using a Distributed Acoustic Sensing (DAS) system with phase-sensitive Optical Time-Domain Reflectometry (Φ-OTDR) technology. It includes labeled and unlabeled acoustic signal measurements gathered around a university campus, covering activities such as walking, running, vehicular movement, and potential security threats like fiber manipulation and fence climbing. The data was captured using an Optasense ODH-F DAS interrogator, which monitors signals from a buried single-mode fiber optic cable. The dataset, stored in HDF5 format, serves as a critical resource for training machine learning models aimed at event classification in DAS systems. Each event is identified by power spectral density (PSD) representations and labeled accordingly. This dataset is ideal for researchers developing and validating machine learning algorithms for DAS-based applications, including structural health monitoring and perimeter security.

Image Dataset Market Outlook

According to our latest research, the global Image Dataset market size reached USD 2.91 billion in 2024, with a robust year-on-year growth trajectory. The market is anticipated to expand at a CAGR of 21.5% from 2025 to 2033, culminating in a projected market value of USD 20.2 billion by 2033. The primary growth drivers include the proliferation of artificial intelligence (AI) and machine learning (ML) applications across various industries, the increasing need for high-quality annotated data for model training, and the accelerated adoption of computer vision technologies. As per the latest research, the surge in demand for image datasets is fundamentally transforming industries such as healthcare, automotive, and retail, where visual data is pivotal to innovation and automation.

A key growth factor for the Image Dataset market is the exponential rise in AI-driven solutions that rely heavily on large, diverse, and accurately labeled datasets. The sophistication of deep learning algorithms, particularly convolutional neural networks (CNNs), has heightened the necessity for high-quality image datasets to ensure reliable and accurate model performance. Industries like healthcare utilize medical imaging datasets for diagnostics and treatment planning, while autonomous vehicles depend on vast and varied image datasets to enhance object detection and navigation capabilities. Furthermore, the growing trend of synthetic data generation is addressing data scarcity and privacy concerns, providing scalable and customizable datasets for training robust AI models.

Another critical driver is the rapid adoption of computer vision across multiple sectors, including security and surveillance, agriculture, and manufacturing. Organizations are increasingly leveraging image datasets to automate visual inspection, monitor production lines, and implement advanced safety systems. The retail and e-commerce segment has witnessed a significant uptick in demand for image datasets to power recommendation engines, virtual try-on solutions, and inventory management systems. The expansion of facial recognition technology in both public and private sectors, for applications ranging from access control to personalized marketing, further underscores the indispensable role of comprehensive image datasets in enabling innovative services and solutions.

The market is also witnessing a surge in partnerships and collaborations between dataset providers, research institutions, and technology companies. This collaborative ecosystem fosters the development of diverse and high-quality datasets tailored to specific industry requirements. The increasing availability of open-source and publicly accessible image datasets is democratizing AI research and innovation, enabling startups and academic institutions to contribute to advancements in computer vision. However, the market continues to grapple with challenges related to data privacy, annotation accuracy, and the ethical use of visual data, which are prompting the development of secure, compliant, and ethically sourced datasets.

Regionally, North America remains at the forefront of the Image Dataset market, driven by a mature AI ecosystem, significant investments in research and development, and the presence of major technology companies. Asia Pacific is rapidly emerging as a high-growth region, buoyed by expanding digital infrastructure, government initiatives promoting AI adoption, and a burgeoning startup landscape. Europe is also witnessing robust growth, particularly in sectors such as automotive, healthcare, and manufacturing, where regulatory frameworks emphasize data privacy and quality. The Middle East & Africa and Latin America are gradually catching up, with increasing investments in smart city projects and digital transformation initiatives fueling demand for image datasets.

Type Analysis

The Image Dataset market by type is segmented into Labeled, Unlabeled, and Synthetic datasets. Labeled datasets, which include images annotated with relevant metadata or tags, are fundamental to sup

Self-Supervised Learning for Robotic Grasping Market Outlook

According to our latest research, the global market size for Self-Supervised Learning for Robotic Grasping stood at USD 1.38 billion in 2024, with a robust CAGR of 32.8% expected over the forecast period. The market is projected to reach USD 16.35 billion by 2033, driven by rapid advancements in artificial intelligence, increasing automation across industries, and the growing demand for intelligent robotic systems capable of complex manipulation tasks. As per the latest research, significant growth factors include the integration of advanced machine learning models, the expansion of collaborative robotics, and the rising adoption of cloud-based deployment for scalable robotic solutions.

One of the primary growth drivers for the Self-Supervised Learning for Robotic Grasping market is the increasing sophistication of deep learning algorithms, particularly those enabling robots to learn from unlabeled data. Industries such as manufacturing, logistics, and healthcare are increasingly relying on robots that can autonomously improve their grasping capabilities without extensive human intervention. This trend is further accelerated by the need for flexible automation in highly dynamic environments, where traditional supervised learning methods prove costly and time-consuming. The ability of self-supervised learning to reduce dependency on large labeled datasets not only cuts operational costs but also accelerates deployment timelines, making it highly attractive for organizations aiming to maintain a competitive edge.

Another significant factor fueling market growth is the rapid expansion of collaborative and service robots in sectors beyond traditional manufacturing. As e-commerce, food and beverage, and healthcare sectors experience surging demand for automation, there is a rising emphasis on robots that can interact safely and effectively with humans. Self-supervised learning enables these robots to adapt to new objects, environments, and tasks with minimal reprogramming, thereby enhancing their utility across diverse applications. This adaptability is crucial for sectors dealing with highly variable product mixes and unpredictable operational conditions, further solidifying the role of self-supervised learning as a transformative technology in robotic grasping.

The proliferation of cloud-based solutions constitutes another pivotal growth factor in the Self-Supervised Learning for Robotic Grasping market. Cloud-based deployment models offer unparalleled scalability, allowing organizations to leverage vast computational resources for training and updating robotic models. This, in turn, facilitates continuous learning and improvement of robotic systems deployed across geographically dispersed locations. Additionally, the integration of edge computing with cloud platforms ensures real-time responsiveness and data privacy, which are critical for applications in sensitive environments such as healthcare and automotive manufacturing. As a result, cloud-based self-supervised learning solutions are witnessing rapid adoption, especially among enterprises seeking to future-proof their automation strategies.

From a regional perspective, Asia Pacific dominates the Self-Supervised Learning for Robotic Grasping market, accounting for the largest revenue share in 2024. This leadership is attributed to the region’s robust manufacturing ecosystem, aggressive investments in smart factories, and the presence of leading robotics innovators. North America and Europe follow closely, driven by technological advancements, strong R&D infrastructure, and high adoption rates in industries such as automotive and electronics. The Middle East & Africa and Latin America are also emerging as promising markets, fueled by increasing automation initiatives and supportive government policies. The regional landscape is characterized by intense competition, rapid technological adoption, and a growing focus on developing indigenous robotic capabilities.

Technology Analysis

The Technology segment of the Self-Supervised Learning for Robotic Grasping market is characterized by rapid innovation and diversification, with several distinct approaches driving advancements in robotic manipulation. Convolutional Neural Networks (CNNs) are at the forefront, enabling robots to interpret complex visual data and recognize objects with high accuracy. CNNs

This dataset comprises approximately 80,000 meticulously organized Bangla character images, serving as a valuable resource for research in Bangla character recognition. Featuring 583 distinct character classes, including numerical characters, it provides a comprehensive foundation for researchers exploring various machine learning algorithms, developing deep neural networks, or conducting comparative studies in the field.

To generate this dataset, Bangla words were initially written on A4-sized pages. Photocopies of the text were distributed to individuals, primarily students from Nazirhat Collegiate High School and Nazirhat College. Participants reproduced the text on another A4-sized paper based on the photocopy. The resulting dataset is organized as a zip file containing two main folders: "traindata" and "testdata."

Traindata:

Together Folder: This folder contains all images without labels. The corresponding labels and image names are stored in a CSV file called "full_df_train.csv." The CSV file has three columns: "image_name," "Label," and "long_label." The "Label" column categorizes classes into broader groups such as 'consonant,' 'vowel,' 'compound,' 'number,' 'kar-fola,' etc. The "long_label" column provides more detailed labels, with 583 individual classes denoted by numbers like 1, 2, 3, 4, etc.

Total_datafinal Folder: This folder is independent and contains 583 subfolders, each representing a distinct class. Images within each subfolder correspond to the respective class name. Unlike the "Together" folder, the images here are labeled independently, making it suitable for different use cases. The "long_label" in the "Together" folder and the subfolder names in this folder are identical.

Testdata:

Test_Datatogether Folder: Similar to the "Together" folder in the training data, this folder contains images without labels. The corresponding labels and image names are stored in a CSV file connected to it.

Test_Data Folder: This folder is independent and mirrors the structure of the "Total_datafinal" folder in the training set. It consists of 583 subfolders, each representing a class, with images stored accordingly.

The meticulous organization ensures the dataset's usability and accessibility for various research applications in the realm of Bangla character recognition. Researchers can leverage this dataset for tasks such as Bangla character recognition, with the flexibility to use the labeled or unlabeled versions based on their specific requirements.

![]() The STL-10 dataset is an image recognition dataset for developing unsupervised feature learning, deep learning, self-taught learning algorithms. It is inspired by the CIFAR-10 dataset but with some modifications. In particular, each class has fewer labeled training examples than in CIFAR-10, but a very large set of unlabeled examples is provided to learn image models prior to supervised training. The primary challenge is to make use of the unlabeled data (which comes from a similar but different distribution from the labeled data) to build a useful prior. We also expect that the higher resolution of this dataset (96x96) will make it a challenging benchmark for developing more scalable unsupervised learning methods. Overview 10 classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck. Images are 96x96 pixels, color. 500 training images (10 pre-defined folds), 800 test images per class. 100000 unlabeled images for uns

Image Dataset Market Outlook

According to our latest research, the global image dataset market size reached USD 2.13 billion in 2024, reflecting robust expansion driven by advancements in artificial intelligence and machine learning. The market is poised to grow at a CAGR of 22.7% from 2025 to 2033, with the total market value forecasted to reach USD 15.18 billion by 2033. This significant growth is fueled by increasing adoption of computer vision solutions across industries, rapid advancements in autonomous vehicles, and the surging demand for high-quality annotated datasets for training AI models.

One of the primary growth factors for the image dataset market is the escalating integration of artificial intelligence and machine learning technologies in various sectors, including healthcare, automotive, retail, and agriculture. As AI models become more sophisticated, the necessity for large, diverse, and high-quality image datasets has intensified. These datasets are critical for training, validating, and testing computer vision algorithms, enabling breakthroughs in facial recognition, object detection, and image segmentation. The proliferation of smart devices and IoT solutions is also generating vast volumes of visual data, further amplifying the need for curated and annotated image datasets to extract actionable insights and drive automation.

Another key driver propelling the image dataset market is the rapid evolution of autonomous vehicles and robotics. Advanced driver-assistance systems (ADAS) and fully autonomous vehicles require massive datasets to accurately interpret real-world environments, recognize obstacles, and make split-second decisions. Similarly, robotics applications in manufacturing, logistics, and agriculture depend on robust image datasets for navigation, object manipulation, and process automation. The increasing investments in R&D for autonomous systems, coupled with regulatory support in several regions, are fostering a conducive environment for the growth of the image dataset market. The emergence of synthetic and augmented datasets is also addressing data scarcity issues, enabling more comprehensive and bias-free model training.

The healthcare sector represents another significant growth avenue for the image dataset market. The adoption of AI-powered medical imaging solutions for diagnostics, treatment planning, and disease monitoring is accelerating, particularly in radiology, pathology, and ophthalmology. High-quality labeled image datasets are indispensable for training algorithms to detect anomalies, classify diseases, and recommend interventions with high accuracy. The ongoing digital transformation of healthcare infrastructure, coupled with rising investments in telemedicine and remote diagnostics, is expected to further boost the demand for specialized medical image datasets. Additionally, collaborations between healthcare providers, research institutions, and data vendors are facilitating the creation and sharing of large-scale, anonymized datasets while addressing privacy and compliance requirements.

From a regional perspective, North America continues to dominate the image dataset market, driven by the presence of leading technology companies, robust research ecosystems, and early adoption of AI applications. However, the Asia Pacific region is emerging as the fastest-growing market, fueled by rapid digitalization, increasing investments in AI and automation, and the expansion of smart city initiatives. Europe is also witnessing substantial growth, supported by strong governmental focus on AI ethics, data privacy, and innovation funding. Latin America and the Middle East & Africa are gradually catching up, with growing awareness of AI’s potential and increasing collaborations between local enterprises and global technology providers. The competitive landscape is characterized by the entry of new data vendors, strategic partnerships, and continuous innovation in data annotation and augmentation techniques.

Type Analysis

The image dataset market by type is segmented into labeled, unlabeled, synthetic, and augmented datasets. Labeled image datasets account for the largest share, as they are crucial for supervised learning tasks where AI models require annotated data to learn from. These datasets are extensively used in applications such as object detection, facial recognition, and medical imaging, where precise labeling improves model accuracy and reliability. The demand for label

Dataset Source: https://www.aicrowd.com/challenges/data-purchasing-challenge-2022

🕵️ Introduction Data for machine learning tasks usually does not come for free but has to be purchased. The costs and benefits of data have to be weighed against each other. This is challenging. First, data usually has combinatorial value. For instance, different observations might complement or substitute each other for a given machine learning task. In such cases, the decision to purchase one group of observations has to be made conditional on the decision to purchase another group of observations. If these relationships are high-dimensional, finding the optimal bundle becomes computationally hard. Second, data comes at different quality, for instance, with different levels of noise. Third, data has to be acquired under the assumption of being valuable out-of-sample. Distribution shifts have to be anticipated.

In this competition, you face these data purchasing challenges in the context of an multi-label image classification task in a quality control setting.

📑 Problem Statement

In short: You have to classify images. Some images in your training set are labelled but most of them aren't. How do you decide which images to label if you have a limited budget to do so?

In more detail: You face a multi-label image classification task. The dataset consists of synthetically generated images of painted metal sheets. A classifier is meant to predict whether the sheets have production damages and if so which ones. You have access to a set of images, a subset of which are labelled with respect to production damages. Because labeling is costly and your budget is limited, you have to decide for which of the unlabelled images labels should be purchased in order to maximize prediction accuracy.

Each of the images have a 4 dimensional label representing the presence or the absence of ['scratch_small', 'scratch_large', 'dent_small', 'dent_large'] in the images.

You are required to submit code, which can be run in three different phases:

Pre-Training Phase

In the Pre-Training Phase, your code will have access to 5,000 labelled images on a multi-label image classification task with 4 classes. It is up to you, how you wish to use this data. For instance, you might want to pre-train a classification model. Purchase Phase

In the Purchase Phase, your code, after going through the Pre-Training Phase will have access to an unlabelled dataset of 10,000 images. You will have a budget of 3,000 label purchases, that you can freely use across any of the images in the unlabelled dataset to obtain their labels. You are tasked with designing your own approach on how to select the optimal subset of 3,000 images in the unlabelled dataset, which would help you optimize your model's performance on the prediction task. You can then continue training your model (which has been pre-trained in the pre-training phase) using the newly purchased labels. Prediction Phase

In the Prediction Phase, your code will have access to a test set of 3,000 unlabelled images, for which you have to generate and submit predictions. Your submission will be evaluated based on the performance of your predictions on this test set. Your code will have access to a node with 4 CPUS, 16 GB RAM, 1 NVIDIA T4 GPU and 3 hours of runtime per submission. In the final round of this challenge, your code will be evaluated across multiple budget-runtime constraints.

💾 Dataset

The datasets for this challenge can be accessed in the Resources Section.

training.tar.gz: The training set containing 5,000 images with their associated labels. During your local experiments you are allowed to use the data as you please. unlabelled.tar.gz: The unlabelled set containing 10,000 images, and their associated labels. During your local experiments you are only allowed to access the labels through the provided purchase_label function. validation.tar.gz: The validation set containing 3,000 images, and their associated labels. During your local experiments you are only allowed to use the labels of the validation set to measure the performance of your models and experiments. debug.tar.gz.: A small set of 100 images with their associated labels, that you can use for integration testing, and for trying out the provided starter kit. NOTE While you run your local experiments on this dataset, your submissions will be evaluated on a dataset which might be sampled from a different distribution, and is not the same as this publicly released version.

👥 Participation

🖊 Evaluation Criteria The challenge will use the Accuracy Score, Hamming Loss and the Exact Match Ratio during evaluation. The primary score will be the Accuracy Score.

📅 Timeline This challenge has two Rounds.

Round 1 : Feb 4th – Feb 28th, 2022

The first round submissions will be evaluated based on one budget-compute constraint pair (max. of 3,00...

Accurate and efficient crop classification using remotely sensed data can provide fundamental and important information for crop yield estimation. Existing crop classification approaches are usually designed to be strong in some specific scenarios but not for multi-scenario crop classification. In this study, we proposed a new deep learning approach for multi-scenario crop classification, named Cropformer. Cropformer can extract global features and local features, to solve the problem that current crop classification methods extract a single feature. Specifically, Cropformer is a two-step classification approach, where the first step is self-supervised pre-training to accumulate knowledge of crop growth, and the second step is a fine-tuned supervised classification based on the weights from the first step. The unlabeled time series and the labeled time series are used as input for the first and second steps respectively. Multi-scenario crop classification experiments including full-season crop classification, in-season crop classification, few-sample crop classification, and transfer of classification models were conducted in five study areas with complex crop types and compared with several existing competitive approaches. Experimental results showed that Cropformer can not only obtain a very significant accuracy advantage in crop classification, but also can obtain higher accuracy with fewer samples. Compared to other approaches, the classification performance of Cropformer during model transfer and the efficiency of the classification were outstanding. The results showed that Cropformer could build up a priori knowledge using unlabeled data and learn generalized features using labeled data, making it applicable to crop classification in multiple scenarios.