19,997 machine learning datasets
19,997 dataset results
A Dataset to Identify Manipulated Social Media News in Bangla
Data used for the paper SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data
The TII-SSRC-23 dataset offers a comprehensive collection of network traffic patterns, meticulously compiled to support the development and research of Intrusion Detection Systems (IDS). It presents a dual structure: one part provides a tabular representation of extracted features in CSV format, while the other offers raw network traffic data for each type of traffic in PCAP files. This rich dataset captures both benign and malicious network scenarios, serving as an invaluable resource for researchers in the machine learning field.
This is an enhanced Market-1501 dataset labeled with SMPL annotations, ie 3D human shape and pose ground truth.
Dataset of validated OCT and Chest X-Ray images described and analyzed in "Deep learning-based classification and referral of treatable human diseases". The OCT Images are split into a training set and a testing set of independent patients. OCT Images are labeled as (disease)-(randomized patient ID)-(image number by this patient) and split into 4 directories: CNV, DME, DRUSEN, and NORMAL.
Dataset used in the publication of Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocks. The paper is published at npj Computational Materials (https://www.nature.com/articles/s41524-023-01125-1)
Adverbs in Recipes (AIR) is a dataset specifically collected for adverb recognition. AIR is a subset of HowTo100M where recipe videos show actions performed in ways that change according to an adverb (e.g. chop thinly/coarsely). AIR was carefully reviewed to ensure reliable annotations.
KITAB is a challenging dataset and a dynamic data collection approach for testing abilities of Large Language Models (LLMs) in answering information retrieval queries with constraint filters. A filtering query with constraints can be of the form "List all books written by Toni Morrison that were published between 1970-1980".
JamALT is a revision of the JamendoLyrics dataset (80 songs in 4 languages), adapted for use as an automatic lyrics transcription (ALT) benchmark.
MGSM8KInstruct, the multilingual math reasoning instruction dataset, encompassing ten distinct languages, thus addressing the issue of training data scarcity in multilingual math reasoning.
An RDF knowledge graph that provides comprehensive, current information about almost 400,000 machine learning publications. This includes the tasks addressed, the datasets utilized, the methods implemented, and the evaluations conducted, along with their results. Compared to its non-RDF-based counterpart Papers With Code, LPWC not only translates the latest advancements in machine learning into RDF format, but also enables novel ways for scientific impact quantification and scholarly key content recommendation. LPWC is openly accessible and is licensed under CC-BY-SA 4.0. As a knowledge graph in the Linked Open Data cloud, we offer LPWC in multiple formats, from RDF dump files to a SPARQL endpoint for direct web queries, as well as a data source with resolvable URIs and links to the data sources SemOpenAlex, Wikidata, and DBLP. Additionally, we supply knowledge graph embeddings, enabling LPWC to be readily applied in machine learning applications.
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
CoVaxFrames includes 113 Vaccine Hesitancy Framings found on Twitter about the COVID-19 vaccines. Language experts annotated tweets as Relevant or Not Relevant, and then further annotated Relevant tweets with Stance towards each framing.
MMVax-Stance includes 113 Vaccine Hesitancy Framings found on Twitter about the COVID-19 vaccines. Language experts annotated multimodal image-text tweets as Relevant or Not Relevant, and then further annotated Relevant tweets with Stance towards each framing.
A fundamental characteristic common to both human vision and natural language is their compositional nature. Yet, despite the performance gains contributed by large vision and language pretraining, we find that—across 7 architectures trained with 4 algorithms on massive datasets—they struggle at compositionality. To arrive at this conclusion, we introduce a new compositionality evaluation benchmark, CREPE, which measures two important aspects of compositionality identified by cognitive science literature: systematicity and productivity. To measure systematicity, CREPE consists of a test dataset containing over 370K image-text pairs and three different seen-unseen splits. The three splits are designed to test models trained on three popular training datasets: CC-12M, YFCC-15M, and LAION-400M. We also generate 325K, 316K, and 309K hard negative captions for a subset of the pairs. To test productivity, CREPE contains 17K image-text pairs with nine different complexities plus 183K hard neg
The S2-100K dataset is a dataset of 100,000 multi-spectral satellite images and their corresponding locations (latitude / longitude coordinates of the image centroid) sampled from Sentinel-2 via the Microsoft Planetary Computer. Copernicus Sentinel data is captured between Jan 1, 2021 and May 17, 2023. The dataset is sampled approximately uniformly over landmass and only includes images without cloud coverage.
StreetTryOn, the new in-the-wild Virtual Try-On dataset, consists of 12,364 and 2,089 street person images for training and validation, respectively. It is derived from the large fashion retrieval dataset DeepFashion2, from which we filter out over 90% of DeepFashion2 images that are infeasible for try-on tasks (e.g., non-frontal view, large occlusion, dark environment, etc.). Combining with the garment and person images in VITON-HD, we obtain a comprehensive suite of in-domain and cross-domain try-on tasks that have garment and person inputs from various sources, including Shop2Model, Model2Model, Shop2Street, and Street2Street.
CholecTrack20 is a surgical video dataset focusing on laparoscopic cholecystectomy and designed for surgical tool tracking, featuring 20 annotated videos. The dataset includes detailed labels for multi-class multi-tool tracking, offering trajectories for tool visibility within the camera scope, intracorporeal movement within the patient's body, and the life-long intraoperative trajectory of each tool. Annotations cover spatial coordinates, tool class, operator identity, phase, visual conditions (occlusion, bleeding, smoke), and more for tools like grasper, bipolar, hook, scissors, clipper, irrigator, and specimen bag, with annotations provided at 1 frame per second across 35K frames and 65K instance tool labels. The dataset uses official splits, allocating 10 videos for training, 2 for validation, and 8 for testing.
Synthetic OD data to mimic data showed in the application of the paper.
GEMRec-18K is a dense prompt-model interaction dataset that consists of 18K images generated by pairing 200 generative models with 90 prompts collected from real-world usages. This dataset builds the cornerstone for exploring generative model recommendation and can be useful for understanding generative models.