Dataset

This dataset was created by Anna Jazayeri

Released under MIT

Contents

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.

AI in Unsupervised Learning Market Outlook

According to our latest research, the AI in Unsupervised Learning market size reached USD 3.8 billion globally in 2024, demonstrating robust expansion as organizations increasingly leverage unsupervised techniques for extracting actionable insights from unlabelled data. The market is forecasted to grow at a CAGR of 28.2% from 2025 to 2033, propelling the industry to an estimated USD 36.7 billion by 2033. This remarkable growth trajectory is primarily fueled by the escalating adoption of artificial intelligence across diverse sectors, an exponential surge in data generation, and the pressing need for advanced analytics that can operate without manual data labeling.

One of the key growth factors driving the AI in Unsupervised Learning market is the rising complexity and volume of data generated by enterprises in the digital era. Organizations are inundated with unstructured and unlabelled data from sources such as social media, IoT devices, and transactional systems. Traditional supervised learning methods are often impractical due to the time and cost associated with manual labeling. Unsupervised learning algorithms, such as clustering and dimensionality reduction, offer a scalable solution by autonomously identifying patterns, anomalies, and hidden structures within vast datasets. This capability is increasingly vital for industries aiming to enhance decision-making, streamline operations, and gain a competitive edge through advanced analytics.

Another significant driver is the rapid advancement in computational power and AI infrastructure, which has made it feasible to implement sophisticated unsupervised learning models at scale. The proliferation of cloud computing and specialized AI hardware has reduced barriers to entry, enabling even small and medium enterprises to deploy unsupervised learning solutions. Additionally, the evolution of neural networks and deep learning architectures has expanded the scope of unsupervised algorithms, allowing for more complex tasks such as image recognition, natural language processing, and anomaly detection. These technological advancements are not only accelerating adoption but also fostering innovation across sectors including healthcare, finance, manufacturing, and retail.

Furthermore, regulatory compliance and the growing emphasis on data privacy are pushing organizations to adopt unsupervised learning methods. Unlike supervised approaches that require sensitive data labeling, unsupervised algorithms can process data without explicit human intervention, thereby reducing the risk of privacy breaches. This is particularly relevant in sectors such as healthcare and BFSI, where stringent data protection regulations are in place. The ability to derive insights from unlabelled data while maintaining compliance is a compelling value proposition, further propelling the market forward.

Regionally, North America continues to dominate the AI in Unsupervised Learning market owing to its advanced technological ecosystem, significant investments in AI research, and strong presence of leading market players. Europe follows closely, driven by robust regulatory frameworks and a focus on ethical AI deployment. The Asia Pacific region is exhibiting the fastest growth, fueled by rapid digital transformation, government initiatives, and increasing adoption of AI across industries. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a slower pace, as awareness and infrastructure continue to develop.

Component Analysis

The Component segment of the AI in Unsupervised Learning market is categorized into Software, Hardware, and Services, each playing a pivotal role in the overall ecosystem. The software segment, comprising machine learning frameworks, data analytics platforms, and AI development tools, holds the largest market share. This dominance is attributed to the continuous evolution of AI algorithms and the increasing availability of open-source and proprietary solutions tailored for unsupervised learning. Enterprises are investing heavily in software that can facilitate the seamless integration of unsupervised learning capabilities into existing workflows, enabling automation, predictive analytics, and pattern recognition without the need for labeled data.

The hardware segment, while smaller in comparison to software, is experiencing significant growth due to the escalating demand for high-perf

Average dice coefficients of the few-supervised learning models using 2%, 5%, and 10% of the labeled data, and semi-supervised learning models using 10% of the labeled data for training.

Domain generalization results (%) in the low-data regime with a comparison of various models in different settings (fully labeled, DG, SSL, and SSDG), evaluated on OfficeHome (Art: A, Clipart: C, Product: P, and Real-World: R). Here, u means utilization of unlabeled data. Results are reported as mean ± standard deviation over 5 runs.

AI in Semi-supervised Learning Market Outlook

According to our latest research, the AI in Semi-supervised Learning market size reached USD 1.82 billion in 2024 globally, driven by rapid advancements in artificial intelligence and machine learning applications across diverse industries. The market is expected to expand at a robust CAGR of 28.1% from 2025 to 2033, reaching a projected value of USD 17.17 billion by 2033. This exponential growth is primarily fueled by the increasing need for efficient data labeling, the proliferation of unstructured data, and the growing adoption of AI-driven solutions in both large enterprises and small and medium businesses. As per the latest research, the surging demand for automation, accuracy, and cost-efficiency in data processing is significantly accelerating the adoption of semi-supervised learning models worldwide.

One of the most significant growth factors for the AI in Semi-supervised Learning market is the explosive increase in data generation across industries such as healthcare, finance, retail, and automotive. Organizations are continually collecting vast amounts of structured and unstructured data, but the process of labeling this data for supervised learning remains time-consuming and expensive. Semi-supervised learning offers a compelling solution by leveraging small amounts of labeled data alongside large volumes of unlabeled data, thus reducing the dependency on extensive manual annotation. This approach not only accelerates the deployment of AI models but also enhances their accuracy and scalability, making it highly attractive for enterprises seeking to maximize the value of their data assets while minimizing operational costs.

Another critical driver propelling the growth of the AI in Semi-supervised Learning market is the increasing sophistication of AI algorithms and the integration of advanced technologies such as deep learning, natural language processing, and computer vision. These advancements have enabled semi-supervised learning models to achieve remarkable performance in complex tasks like image and speech recognition, medical diagnostics, and fraud detection. The ability to process and interpret vast datasets with minimal supervision is particularly valuable in sectors where labeled data is scarce or expensive to obtain. Furthermore, the ongoing investments in research and development by leading technology companies and academic institutions are fostering innovation, resulting in more robust and scalable semi-supervised learning frameworks that can be seamlessly integrated into enterprise workflows.

The proliferation of cloud computing and the increasing adoption of hybrid and multi-cloud environments are also contributing significantly to the expansion of the AI in Semi-supervised Learning market. Cloud-based deployment offers unparalleled scalability, flexibility, and cost-efficiency, allowing organizations of all sizes to access cutting-edge AI tools and infrastructure without the need for substantial upfront investments. This democratization of AI technology is empowering small and medium enterprises to leverage semi-supervised learning for competitive advantage, driving widespread adoption across regions and industries. Additionally, the emergence of AI-as-a-Service (AIaaS) platforms is further simplifying the integration and management of semi-supervised learning models, enabling businesses to accelerate their digital transformation initiatives and unlock new growth opportunities.

From a regional perspective, North America currently dominates the AI in Semi-supervised Learning market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading AI vendors, robust technological infrastructure, and high investments in AI research and development are key factors driving market growth in these regions. Asia Pacific is expected to witness the fastest CAGR during the forecast period, fueled by rapid digitalization, expanding IT infrastructure, and increasing government initiatives to promote AI adoption. Meanwhile, Latin America and the Middle East & Africa are also showing promising growth potential, supported by rising awareness of AI benefits and growing investments in digital transformation projects across various sectors.

Component Analysis

The component segment of the AI in Semi-supervised Learning market is divided into software, hardware, and services, each playing a pivotal role in the adoption and implementation of semi-s

Self-Supervised Learning Market Outlook

According to our latest research, the global self-supervised learning market size reached USD 10.2 billion in 2024, demonstrating rapid adoption across multiple sectors. The market is set to expand at a strong CAGR of 33.1% from 2025 to 2033, propelled by the growing need for advanced artificial intelligence solutions that minimize dependency on labeled data. By 2033, the market is forecasted to achieve an impressive size of USD 117.2 billion, underscoring the transformative potential of self-supervised learning in revolutionizing data-driven decision-making and automation across industries. This growth trajectory is supported by increasing investments in AI research, the proliferation of big data, and the urgent demand for scalable machine learning models.

The primary growth driver for the self-supervised learning market is the exponential surge in data generation across industries and the corresponding need for efficient data labeling techniques. Traditional supervised learning requires vast amounts of labeled data, which is both time-consuming and expensive to annotate. Self-supervised learning, by contrast, leverages unlabeled data to train models, significantly reducing operational costs and accelerating the deployment of AI systems. This paradigm shift is particularly critical in sectors like healthcare, finance, and autonomous vehicles, where large datasets are abundant but labeled examples are scarce. As organizations seek to unlock value from their data assets, self-supervised learning is emerging as a cornerstone technology, enabling more robust, scalable, and generalizable AI applications.

Another significant factor fueling market expansion is the rapid advancement in computing infrastructure and algorithmic innovation. The availability of high-performance hardware, such as GPUs and TPUs, coupled with breakthroughs in neural network architectures, has made it feasible to train complex self-supervised models on massive datasets. Additionally, the open-source movement and collaborative research have democratized access to state-of-the-art self-supervised learning frameworks, fostering innovation and lowering barriers to entry for enterprises of all sizes. These technological advancements are empowering organizations to experiment with self-supervised learning at scale, driving adoption across a wide range of applications, from natural language processing to computer vision and robotics.

The market is also benefiting from the growing emphasis on ethical AI and data privacy. Self-supervised learning methods, which minimize the need for sensitive labeled data, are increasingly being adopted to address privacy concerns and regulatory compliance requirements. This is particularly relevant in regions with stringent data protection regulations, such as the European Union. Furthermore, the ability of self-supervised learning to generalize across domains and tasks is enabling businesses to build more resilient and adaptable AI systems, further accelerating market growth. The convergence of these factors is positioning self-supervised learning as a key enabler of next-generation AI solutions.

Transfer Learning is emerging as a pivotal technique in the realm of self-supervised learning, offering a bridge between different domains and tasks. By leveraging knowledge from pre-trained models, transfer learning allows for the adaptation of AI systems to new, related tasks with minimal additional data. This approach is particularly beneficial in scenarios where labeled data is scarce, enabling models to generalize better and learn more efficiently. The integration of transfer learning into self-supervised frameworks is enhancing the ability of AI systems to tackle complex problems across various industries, from healthcare diagnostics to autonomous driving. As the demand for versatile and efficient AI solutions grows, transfer learning is set to play a crucial role in the evolution of self-supervised learning technologies.

From a regional perspective, North America currently leads the self-supervised learning market, accounting for the largest share due to its robust AI research ecosystem, significant investments from technology giants, and early adoption across verticals. However, Asia Pacific is projected to witness the fastest growth over the forecast period, driven by the rapid digital tran

The Dataset

These datasets were used while writing the following work:

Polo, F. M., Ciochetti, I., and Bertolo, E. (2021). Predicting legal proceedings status: approaches based on sequential text data. In Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law, pages 264–265.

Please cite us if you use our datasets in your academic work:

@inproceedings{polo2021predicting,
 title={Predicting legal proceedings status: approaches based on sequential text data},
 author={Polo, Felipe Maia and Ciochetti, Itamar and Bertolo, Emerson},
 booktitle={Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law},
 pages={264--265},
 year={2021}
}

More details below!

Context

Every legal proceeding in Brazil is one of three possible classes of status: (i) archived proceedings, (ii) active proceedings, and (iii) suspended proceedings. The three possible classes are given in a specific instant in time, which may be temporary or permanent. Moreover, they are decided by the courts to organize their workflow, which in Brazil may reach thousands of simultaneous cases per judge. Developing machine learning models to classify legal proceedings according to their status can assist public and private institutions in managing large portfolios of legal proceedings, providing gains in scale and efficiency.

In this dataset, each proceeding is made up of a sequence of short texts called “motions” written in Portuguese by the courts’ administrative staff. The motions relate to the proceedings, but not necessarily to their legal status.

Content

Our data is composed of two datasets: a dataset of ~3*10^6 unlabeled motions and a dataset containing 6449 legal proceedings, each with an individual and a variable number of motions, but which have been labeled by lawyers. Among the labeled data, 47.14% is classified as archived (class 1), 45.23% is classified as active (class 2), and 7.63% is classified as suspended (class 3).

The datasets we use are representative samples from the first (São Paulo) and third (Rio de Janeiro) most significant state courts. State courts handle the most variable types of cases throughout Brazil and are responsible for 80% of the total amount of lawsuits. Therefore, these datasets are a good representation of a very significant portion of the use of language and expressions in Brazilian legal vocabulary.

Regarding the labels dataset, the key "-1" denotes the most recent text while "-2" the second most recent and so on.

Acknowledgements

We would like to thank Ana Carolina Domingues Borges, Andrews Adriani Angeli, and Nathália Caroline Juarez Delgado from Tikal Tech for helping us to obtain the datasets. This work would not be possible without their efforts.

Inspiration

Can you develop good machine learning classifiers for text sequences? :)

The proposed dataset, termed PC-Urban (Urban Point Cloud), is captured with an Ouster LiDAR sensor with 64 channels. The sensor is installed on an SUV that drives through the downtown of Perth, Western Australia (WA), Australia. The dataset comprises over 4.3 billion points captured for 66K sensor frames. The labelled data is organized as registered and raw point cloud frames, where the former has a different number of registered consecutive frames. We provide 25 class labels in the dataset covering 23 million points and 5K instances. Labelling is performed with PC-Annotate and can easily be extended by the end-users employing the same tool.The data is organized into unlabelled and labelled 3D point clouds. The unlabelled data is provided in .PCAP file format, which is the direct output format of the used Ouster LiDAR sensor. Raw frames are extracted from the recorded .PCAP files in the form of Ply and Excel files using the Ouster Studio Software. Labelled 3D point cloud data consists of registered or raw point clouds. A labelled point cloud is a combination of Ply, Excel, Labels and Summary files. A point cloud in Ply file contains X, Y, Z values along with color information. An Excel file contains X, Y, Z values, Intensity, Reflectivity, Ring, Noise, and Range of each point. These attributes can be useful in semantic segmentation using deep learning algorithms. The Label and Label Summary files have been explained in the previous section. Our one GB raw data contains nearly 1,300 raw frames, whereas 66,425 frames are provided in the dataset, each comprising 65,536 points. Hence, 4.3 billion points captured with the Ouster LiDAR sensor are provided. Annotation of 25 general outdoor classes is provided, which include car, building, bridge, tree, road, letterbox, traffic signal, light-pole, rubbish bin, cycles, motorcycle, truck, bus, bushes, road sign board, advertising board, road divider, road lane, pedestrians, side-path, wall, bus stop, water, zebra-crossing, and background. With the released data, a total of 143 scenes are annotated which include both raw and registered frames.

Domain generalization results (%) in the low-data regime with a comparison of various models in different settings (fully labeled, DG, SSL, and SSDG), evaluated on PACS (Photo: P, Art: A, Cartoon: C, and Sketch: S). Here, u means utilization of unlabeled data. Results are reported as mean ± standard deviation over 5 random seeds.

Semi-Supervised Learning for Pallet Vision Market Outlook

According to our latest research, the global Semi-Supervised Learning for Pallet Vision market size reached USD 1.12 billion in 2024, reflecting robust adoption across logistics, manufacturing, and retail sectors. The market is projected to grow at a CAGR of 19.3% from 2025 to 2033, reaching an estimated USD 5.34 billion by 2033. This impressive growth trajectory is primarily driven by the rising demand for automation, efficiency, and real-time data analytics in supply chain and warehouse operations, underpinned by advancements in artificial intelligence and computer vision technologies.

The surge in demand for semi-supervised learning for pallet vision is fundamentally rooted in the need for more intelligent, scalable, and cost-effective solutions for managing complex warehouse and logistics environments. Traditional supervised learning models require vast amounts of labeled data, which is often expensive and time-consuming to obtain, especially in dynamic industrial settings. Semi-supervised learning bridges this gap by leveraging both labeled and unlabeled data, enabling organizations to rapidly deploy vision-based systems for pallet identification, tracking, and quality inspection with reduced annotation costs. This approach not only accelerates model development but also enhances adaptability to new pallet types, environmental changes, and operational nuances, positioning it as a transformative force in industrial automation.

Another significant growth factor is the increasing adoption of Industry 4.0 principles, where the integration of AI-powered vision systems is becoming a cornerstone of digital transformation strategies. Enterprises across logistics, retail, and manufacturing are investing heavily in smart warehousing, automated material handling, and predictive maintenance, all of which benefit from robust pallet vision solutions. The ability of semi-supervised learning algorithms to continuously learn from operational data and improve over time is driving higher accuracy rates in object detection, inventory tracking, and anomaly identification. This continuous improvement loop is essential for meeting the evolving demands of omnichannel retail, just-in-time manufacturing, and globalized supply chains, further fueling market expansion.

Furthermore, regulatory pressures and increasing emphasis on workplace safety and compliance are propelling the adoption of advanced pallet vision systems. Governments and industry bodies are mandating stricter standards for product traceability, load verification, and quality assurance, particularly in sectors like food and beverage, pharmaceuticals, and automotive. Semi-supervised learning enables organizations to rapidly adapt to these regulatory requirements by facilitating faster model retraining and deployment while maintaining high levels of accuracy and reliability. As a result, companies are able to mitigate risks, reduce manual errors, and ensure compliance, which is a critical driver for sustained market growth.

Regionally, North America continues to lead the global semi-supervised learning for pallet vision market due to its advanced logistics infrastructure, high adoption of AI technologies, and strong presence of major solution providers. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid industrialization, e-commerce expansion, and increasing investments in smart manufacturing across China, Japan, and India. Europe also demonstrates substantial growth potential, supported by stringent regulatory frameworks and widespread digital transformation initiatives. As these regions continue to invest in automation and AI-driven supply chain solutions, the market is expected to witness significant growth opportunities worldwide.

Component Analysis

The component segment of the semi-supervised learning for pallet vision market is divided into software, hardware, and services, each playing a pivotal role in the overall ecosystem. S

The MotionDataHAR dataset combines unlabeled and labeled motion data from multiple users. The labeled data is collected through movement trials and precise instructions, whereas the unlabeled data was collected in a completely uncontrolled, real-world environment that should represent the real-world use-case. The collection of unlabeled data was started at random time points throughout the day (iOS Background Tasks) and through significant location change. This ensures an uncontrolled data collection approach that represents the downstream prediction task.

Data

This dataset contains raw device motion data that was collected from various iOS devices and users. Those users performed five explicit activities: WALKING, RUNNING, SITTING / LYING, WALKING DOWNSTAIRS and WALKING UPSTAIRS. The majority of the data is labeled with the NONE label, indicating that there is no label present for this data. The unlabeled data is collected with a sampling rate of 15Hz and the labeled data with a sampling rate of 30Hz. This was done to achieve a more energy efficient solution, because the unlabeled data was collected in a real-life scenario during day-to-day activities.

Relevant Papers

Huetter, S. (2023). Personalized Self-Supervised Learning for Real-World Human Activity Recognition [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.109782

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...

Domain generalization results (%) in the low-data regime with a comparison of various models in different settings (fully labeled, DG, SSL, and SSDG), evaluated on miniDomainNet (Clipart: C, Painting: P, Real: R, and Sketch: S). Here, u means utilization of unlabeled data. Results are reported as mean ± standard deviation over 5 runs.

This item is part of the collection "AIS Trajectories from Danish Waters for Abnormal Behavior Detection"

DOI: https://doi.org/10.11583/DTU.c.6287841

Using Deep Learning for detection of maritime abnormal behaviour in spatio temporal trajectories is a relatively new and promising application. Open access to the Automatic Identification System (AIS) has made large amounts of maritime trajectories publically avaliable. However, these trajectories are unannotated when it comes to the detection of abnormal behaviour.

The lack of annotated datasets for abnormality detection on maritime trajectories makes it difficult to evaluate and compare suggested models quantitavely. With this dataset, we attempt to provide a way for researchers to evaluate and compare performance.

We have manually labelled trajectories which showcase abnormal behaviour following an collision accident. The annotated dataset consists of 521 data points with 25 abnormal trajectories. The abnormal trajectories cover amoung other; Colliding vessels, vessels engaged in Search-and-Rescue activities, law enforcement, and commercial maritime traffic forced to deviate from the normal course

These datasets consists of unlabelled trajectories for the purpose of training unsupervised models. For labelled datasets for evaluation please refer to the collection. Link in Related publications.

The data is saved using the pickle format for Python Each dataset is split into 2 files with naming convention:

datasetInfo_XXX
data_XXX

Files named "data_XXX" contains the extracted trajectories serialized sequentially one at a time and must be read as such. Please refer to provided utility functions for examples. Files named "datasetInfo" contains Metadata related to the dataset and indecies at which trajectories begin in "data_XXX" files.

The data are sequences of maritime trajectories defined by their; timestamp, latitude/longitude position, speed, course, and unique ship identifer MMSI. In addition, the dataset contains metadata related to creation parameters. The dataset has been limited to a specific time period, ship types, moving AIS navigational statuses, and filtered within an region of interest (ROI). Trajectories were split if exceeding an upper limit and short trajectories were discarded. All values are given as metadata in the dataset and used in the naming syntax.

Naming syntax: data_AIS_Custom_STARTDATE_ENDDATE_SHIPTYPES_MINLENGTH_MAXLENGTH_RESAMPLEPERIOD.pkl

See datasheet for more detailed information and we refer to provided utility functions for examples on how to read and plot the data.

Dataset belonging to the paper: Data-Driven Machine Learning-Informed Framework for Model Predictive Control in Vehicles

labeled_seed.csv: Processed and labeled data of all maneuvers combined into a single file, sorted by label

raw_track_session.csv: Untouched CSV file from Racebox track session

unlabeled_exemplar.csv: Processed but unlabeled data of street and track data

The Concrete Aggregate Dataset consists of high resolution images acquired from 40 different concrete cylinders, cut lengthwise as to display the particle distribution in the concrete, with a ground sampling distance of 0.03mm. In order to train and evaluate approaches for the semantic segmentation of the concrete aggregate images, currently 17 of the 40 images have been annotated by manually associating one of the classes aggregate or suspension to each pixel. We encourage to use the remaining unlabelled images for semi-supervised segmentation approaches, in which unlabelled data is leveraged in addition to labelled training data in order to improve the segmentation performance. In the subsequent figure, five examplary tiles of size 448x448 pixels and their annotated label masks are shown. The diversity of the appearance of both, aggregate and suspension can be noted. In the figure below, the distribution of the aggregate particles in dependency on their sizes is depicted. The variation of the size of the particles contained in the data set ranges up to 15mm of maximum particle diameter. However, the majority of particles, namely more than 50% exhibit a maximum diameter of less then 3mm (100px). As a consequence, approximately 80% of the particles possess an area of 5mm ^2 or less.It has to be noted that particles with a size less then 20px are barely distinguishable from the suspension and are therefore not contained in the reference data. If you make use of the proposed data, please cite the publication listed below.

This dataset contains microscopic images of multiple cell lines captured by multiple microcopic without use of any fluorescent labeling and a manually annotated ground truth for subsequent use in segmentation algorithms. Dataset also includes images reconstructed according to the methods described below in order to ease further segmentation.

Our data consist of

244 labelled images of PC-3 (7,907 cells), 205 labelled PNT1A (9,288 cells), in the paper designated as "QPI_Seg_PNT1A_PC3", and

1,819 unlabelled images with a mixture of 22Rv1, A2058, A2780, A8780, DU145, Fadu, G361, HOB and LNCaP used for pretraining, in the paper designated as "QPI_Cell_unlabelled".

See Vicar et al. XXXX 2021 DOI XXX (TBA after publishing)

Code using this dataset is available at XXXX (TBA after publishing)

Materials and methods

A set of adherent cell lines of various origins, tumorigenic potential, and morphology were used in this paper (PC-3, PNT1A, 22Rv1, DU145, LNCaP, A2058, A2780, A8780, Fadu, G361, HOB). PC-3, PNT1A, 22Rv1, DU145, LNCaP, A2780, and G361 cell lines were cultured in RPMI-1640 medium, A2058, FaDu, and HOB cell lines were cultured in DMEM-F12 medium, all supplemented with antibiotics (penicillin 100 U/ml and streptomycin 0.1 mg/ml), and with 10% fetal bovine serum (FBS). Prior to microscopy acquisition, the cells were maintained at 37 °C in a humidified (60%) incubator with 5% CO\textsubscript{2} (Sanyo, Japan). For acquisition purposes, the cells were cultivated in the Flow chamber µ-Slide I Luer Family (Ibidi, Martinsried, Germany). To maintain standard cultivation conditions during time-lapse experiments, cells were placed in the gas chamber H201 - for Mad City Labs Z100/Z500 piezo Z-stage (Okolab, Ottaviano NA, Italy). For the acquisition of QPI, a coherence-controlled holographic microscope (Telight, Q-Phase) was used. Objective Nikon Plan 10×/0.3 was used for hologram acquisition with a CCD camera (XIMEA MR4021MC). Holographic data were numerically reconstructed with the Fourier transform method (described in Slaby, 2013 and phase unwrapping was used on the phase image. QPI datasets used in this paper were acquired during various experimental setups and treatments. In most cases, experiments were conducted with the time-lapse acquisition. The final dataset contains images acquired at least three hours apart.

Folder structure and file and filename description

labelled (QPI_Seg_PNT1A_PC3): 205 FOVs PNT1A and 244 FOVs PC-3 cells with segmentation labels, e.g. 00001_PC3_img.tif - 32bit tiff image (in pg/um2 values) 00001_PC3_mask.png - 8bit image with mask with unique grayscale value corresponding to single cell in FOV.

unlabelled (QPI_Cell_unlabelled): 11 varying cell lines, total 1819 FOVs, 32bit tiff image (in pg/um2 values)

Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. The paper can be found at https://arxiv.org/pdf/2206.08023.pdf

In addition to providing the labeled 600 CT and MRI scans, we expect to provide 2000 CT and 1200 MRI scans without labels to support more learning tasks (semi-supervised, un-supervised, domain adaption, ...). The link can be found in:

• labeled data (500CT+100MRI)
• unlabeled data Part I (900CT)
• unlabeled data Part II (1100CT) (Now there are 1000CT, we will replenish to 1100CT)
• unlabeled data Part III (1200MRI)

if you found this dataset useful for your research, please cite:

@article{ji2022amos,
 title={AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation},
 author={Ji, Yuanfeng and Bai, Haotian and Yang, Jie and Ge, Chongjian and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhang, Lingyan and Ma, Wanling and Wan, Xiang and others},
 journal={arXiv preprint arXiv:2206.08023},
 year={2022}
}

Semi-Supervised Learning for Pallet Vision Market Outlook

According to our latest research, the global Semi-Supervised Learning for Pallet Vision market size reached USD 712 million in 2024, driven by surging automation demands and the rapid transformation of supply chain processes. The market is expected to register a robust CAGR of 21.4% during the forecast period, with the value forecasted to reach USD 4.35 billion by 2033. This remarkable growth is primarily fueled by the pressing need for intelligent, scalable, and cost-efficient pallet vision solutions that leverage semi-supervised learning to optimize warehouse operations, reduce human error, and enhance throughput across diverse industries.

One of the most significant growth drivers for the Semi-Supervised Learning for Pallet Vision market is the exponential rise in global e-commerce and omnichannel retailing. As online retail continues to boom, warehouses and distribution centers are under immense pressure to process, sort, and ship products with unprecedented speed and accuracy. Semi-supervised learning algorithms, which combine the strengths of both supervised and unsupervised methods, are proving invaluable for pallet vision applications. They enable systems to learn from limited labeled data while leveraging vast amounts of unlabeled visual data, resulting in higher accuracy for tasks such as pallet identification, defect detection, and inventory tracking. This capability is especially crucial in dynamic environments where manual labeling is costly and time-consuming, and where adaptability to new pallet types, sizes, and conditions is essential.

Another key growth factor is the integration of semi-supervised learning with advanced computer vision hardware and IoT sensors. The proliferation of affordable high-resolution cameras, edge computing devices, and cloud-based analytics platforms has made it feasible for organizations of all sizes to deploy sophisticated pallet vision systems. Semi-supervised learning models can continuously improve their performance as new data is captured, enabling predictive maintenance, real-time quality inspection, and automated sorting with minimal human intervention. This technology not only reduces operational costs but also enhances workplace safety by minimizing the need for manual inspections in hazardous or high-traffic areas. As a result, both large enterprises and small to mid-sized businesses are increasingly investing in these solutions to stay competitive and compliant with industry regulations.

Furthermore, the market is benefiting from growing partnerships between technology providers, logistics companies, and manufacturing enterprises. Collaborative efforts are accelerating the development of industry-specific pallet vision applications that address unique operational challenges, such as cold chain management, fragile goods handling, and SKU-level traceability. Governments and industry associations are also supporting the adoption of intelligent automation through incentives and standardization initiatives, further propelling market growth. The increasing focus on sustainability and waste reduction is prompting organizations to leverage semi-supervised learning for optimizing pallet usage, reducing damage, and minimizing returns, thereby contributing to both profitability and environmental responsibility.

Regionally, North America currently dominates the Semi-Supervised Learning for Pallet Vision market, accounting for more than 38% of the global revenue in 2024. This leadership is attributed to the region's advanced logistics infrastructure, high adoption of automation technologies, and the presence of leading technology vendors. However, Asia Pacific is emerging as the fastest-growing region, fueled by massive investments in smart warehousing and manufacturing automation, particularly in China, Japan, and India. Europe, with its strong emphasis on Industry 4.0 and sustainable logistics, is also witnessing significant uptake of semi-supervised pallet vision solutions. Latin America and the Middle East & Africa are gradually catching up, supported by expanding retail and e-commerce sectors and increasing awareness of the benefits of intelligent automation.

Component Analysis

The Semi-Supervised Learning for Pallet Vision market is segmented by component into software, hardware, and services, each playing a pivotal role in the ecosystem. Software solutions form the backbone of pallet vision