19,997 machine learning datasets
19,997 dataset results
The training subset consists of 15 robotic nephrectomy procedures captured on the da Vinci X or Xi system. There are 149 frames per video sequence, and the dimension of each frame is 1280x1024. Segmentation annotations are provided with 10 different classes, including instruments, kidneys, and other objects in the surgical scenario. The main differences with the 2017 instrument segmentation dataset are annotation of kidney parenchyma, surgical objects such as suturing needles, Suturing thread clips, and additional instruments. We annotated the graphical representation of the interaction between the surgical instruments and the defective tissue in the surgical scene with the help of our clinical expertise with the da Vinci Xi robotic system. We also delineate the bounding box to identify all the surgical objects. Kidney and instruments are represented as nodes and active edges annotated as the interaction class in the graph. In total, 12 kinds of interactions were identified to generat
BIOSED-ACPD: BIOacoustic Sound Event Detection - Adaptive Change Point Detection dataset
Optimization of pedestrian evacuation in different environments
An image sequence dataset of growing snowflakes in HDF5 format. Generated by the Gravner-Griffeath LCA model for snow crystal growth. Useful for modeling crystal growth with neural networks.
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Sandhi Kosh is the first Sanskrit Sandhi Benchmark created to evaluate the Sanskrit Sandhi Tools. The corpus is provided below free of cost for research purposes only. If you use this corups in your research, please cite the following paper:
PatternCom is a composed image retrieval benchmark based on PatternNet. PatternNet is a large-scale high-resolution remote sensing image retrieval dataset. There are 38 classes and each class has 800 images of size 256×256 pixels. In PatternCom, we select some classes to be depicted in query images, and add a query text that defines an attribute relevant to that class. For instance, query images of “swimming pools” are combined with text queries defining “shape” as “rectangular”, “oval”, and “kidney-shaped”. In total, PatternCom includes six attributes consisted of up to four different classes each. Each attribute can be associated with two to five values per class. The number of positives ranges from 2 to 1345 and there are more than 21k queries in total.
The FashionFail dataset comprises 2,495 high-resolution images (2400x2400 pixels) of products found on e-commerce websites. It is designed to address the limitations of existing state-of-the-art fashion parsing models. FashionFail consists of a diverse set of online shopping images with categories that are compatible with the established Fashionpedia dataset. The dataset is divided into training, validation, and test sets, consisting of 1,344, 150, and 1,001 images, respectively.
Introduction This dataset was gathered during the Vid2RealHRI study of humans’ perception of robots' intelligence in the context of an incidental Human-Robot encounter. The dataset contains participants' questionnaire responses to four video study conditions, namely Baseline, Verbal, Body language, and Body language + Verbal. The videos depict a scenario where a pedestrian incidentally encounters a quadruped robot trying to enter a building. The robot uses verbal commands or body language to try to ask for help from the pedestrian in different study conditions. The differences in the conditions were manipulated using the robot’s verbal and expressive movement functionalities.
This dataset contains two types of intercepted network packets: "normal" network traffic packets (i.e. a variety of non-malicious traffic types) and "attack" packets from attacks against a 5G Core implemented with free5GC. The captures were collected using tshark or Wireshark on 4 different network interfaces within the 5G core. Those interfaces and where they sit within the system are outlined in the 5GNetworkDiagram figure. Files that start with "allcap" contain packets that were recorded on all four interfaces simultaneously; other *.pcapng files represent the same data that has been broken out into one of the four interfaces.
This benchmark hypergraph dataset, Twitter-HyDrug-UR, is derived from Twitter-HyDrug by HyGCL-DC. Twitter-HyDrug-UR is a real-world hypergraph data that describes the drug trafficking on Twitter. Unlike HyGCL-DC, which targets a drug trafficking community detection task (a multi-label node classification), we aim to identify drug user roles in drug trafficking activities on social media. To this end, we categorize node labels into four distinct roles: drug seller, drug buyer, drug user, and drug discussant, and each node is assigned to one and only one label. Consequently, we frame the problem for Twitter-HyDrug-UR as a multi-class node classification task.
The Linguistic Benchmark (JSON), consisting of 30 questions was developed to be easy for human adults to answer but challenging for LLMs. It is designed to assess the well-documented limitations of LLMs across domains such as spatial reasoning, linguistic understanding, relational thinking, mathematical reasoning, knowledge of basic scientific concepts, and common sense. This benchmark is a useful tool to gauge the current capabilities capabilities of LLMs. The questions serve as a linguistic benchmark to examine model performance in several key domains where they have known limitations.
The MoToMQA (Multi-Order Theory of Mind Question & Answer) benchmark is a test suite introduced to examine the extent to which large language models (LLMs) have developed higher-order theory of mind (ToM); the human ability to reason about multiple mental and emotional states in a recursive manner¹.
Inpatient claims, Outpatient claims and Beneficiary details of each provider.
A large-scale reference dataset for bioacoustics. MeerKAT is a 1068h large-scale dataset containing data from audio-recording collars worn by free-ranging meerkats (Suricata suricatta) at the Kalahari Research Centre, South Africa, of which 184h are labeled with twelve time-resolved vocalization-type ground truth target classes, each with millisecond resolution. The labeled 184h MeerKAT subset exhibits realistic sparsity conditions for a bioacoustic dataset (96% background-noise or other signals and 4% vocalizations), dispersed across 66398 10-second samples, spanning 251562 labeled events and showcasing significant spectral and temporal variability, making it the first large-scale reference point with real-world conditions for benchmarking pretraining and finetune approaches in bioacoustics deep learning.
fruit-SALAD is a synthetic image dataset with 10,000 generated images of fruit depictions. This combined semantic category and style benchmark comprises 100 instances each of 10 easily recognizable fruit categories and 10 easy distinguishable styles.
The growing importance of person re-identification in computer vision has highlighted the need for more extensive and diverse datasets. In response, we introduce the ENTIRe-ID dataset, an extensive collection comprising over 4.45 million images from 37 different cameras in varied environments. This dataset is uniquely designed to tackle the challenges of domain variability and model generalization, areas where existing datasets for person re-identification have fallen short. The ENTIRe-ID dataset stands out for its coverage of a wide array of real-world scenarios, encompassing various lighting conditions, angles of view, and diverse human activities. This design ensures a realistic and robust training platform for ReID models.
A collection of images of simple food items with various ground-truths
After defining a taxonomy of the main stone deterioration patterns and anomalies, we selected 354 highly representative images of stone-built heritage, offering them a careful selection of labels to choose from.
As a first step towards building models that can recognise immune cells in WSIs, we introduce Immunocto, a high-resolution (40 x magnification) massive database of 2,310,257 immune cells distributed across 4 immune cell subtypes (CD4 T-cells, CD8+ T-cells, B-cells, and macrophages). To our knowledge, Immunocto is the largest available dataset of immune cells extracted from H\&E WSIs by an order of magnitude. All models trained with this database can be tried at www.octopath.ai