19,997 machine learning datasets
19,997 dataset results
Doi: 10.1101/2020.04.24.20078584
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Scene Graph Generation (SGG) converts visual scenes into structured graph representations, providing deeper scene understanding for complex vision tasks. However, existing SGG models often overlook essential spatial relationships and struggle with generalization in open-vocabulary contexts. To address these limitations, we propose LLaVA-SpaceSGG, a multimodal large language model (MLLM) designed for open-vocabulary SGG with enhanced spatial relation modeling. To train it, we collect the SGG instruction-tuning dataset, named SpaceSGG. This dataset is constructed by combining publicly available datasets and synthesizing data using open-source models within our data construction pipeline. It combines object locations, object relations, and depth information, resulting in three data formats: spatial SGG description, question-answering, and conversation. To enhance the transfer of MLLMs' inherent capabilities to the SGG task, we introduce a two-stage training paradigm. Experiments show that
The dataset contains 1,200 trained ViT-B-16 models, trained on ImageNet. Half of the models are benign. The other half constitutes Trojan models, each trained with a randomly generated trigger that makes the model predict a specific target class, chosen at random for each Trojan model.
CURE is a retrieval dataset with a monolingual and two cross-lingual conditions, with splits spanning ten medical domains. Queries in CURE are natural language questions formulated by healthcare providers. They express the information needs of practitioners consulting academic literature in the course of their duties. Queries are available in English, French and Spanish. The corpus is constructed by mining an index of english passages extracted from biomedical academic articles.
IllusionMNIST_test Dataset Characteristics IllusionMNIST_test is a generated dataset derived from the MNIST dataset. It introduces a novel element of pareidolia—a phenomenon where patterns, often faces, are perceived in random or abstract stimuli. The dataset contains 11 classes: the original 10 digits from MNIST, and an additional "No Illusion" class. It includes 1,219 samples, all synthetically created rather than real-world images.
IllusionFashionMNIST_test Dataset Characteristics IllusionFashionMNIST_test is a generated dataset derived from the FashionMNIST dataset. It incorporates the concept of pareidolia—a phenomenon where patterns, often faces, are perceived in random or abstract stimuli. The dataset contains 11 classes: the original 10 classes from FashionMNIST, and an additional "No Illusion" class. It includes 1,267 samples, all synthetically created rather than real-world images.
IllusionAnimals_test Dataset Characteristics IllusionAnimals_test is a generated dataset based on a synthetic collection of animal images, including 10 animal classes: cat, dog, pigeon, butterfly, elephant, horse, deer, snake, fish, and rooster. Additionally, it includes a "No Illusion" class, bringing the total number of classes to 11. The dataset contains 1,100 samples, all created synthetically rather than derived from real-world images.
IllusionChar_test Dataset Characteristics IllusionChar_test is a generated dataset containing 3,300 samples of images that feature sequences of 3 to 5 random characters. Unlike classification-focused datasets, this dataset is designed for tasks that require reasoning about patterns, sequences, or illusions within the character sequences. All images are synthetically generated, and no real-world data is included.
The Azerbaijani Sign Language Dataset (AzSLD) is a comprehensive, large dataset designed to facilitate the development and evaluation of machine learning models for the recognition and translation of Azerbaijani Sign Language (AzSL).
This dataset curates quantitative transparency disclosures about the online sexual exploitation of minors. In particular, it focuses on legally mandated reports to the national clearinghouse for the United States, the National Center for Missing and Exploited Children (NCMEC), and captures disclosures by electronic service providers as well as NCMEC.
Dataset contains images of dogs and cats. Specifically 15 breeds of dogs and 8 breeds of cats. Each of the classes has 170 images (except the 'mumbai cat' class) and is separated by folders. All of the images are in .jpg / .jpeg format.
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
We present a benchmark dataset for tactile-based object tracking, featuring 12 distinct objects and 84 tracking trials—7 trials per object, each lasting an average of 10.2 seconds. The dataset includes tactile video, per-frame 6DoF ground truth sensor poses, and pre-processed surface geometry constructed from each tactile video frame.
This is the price data that supports the cost estimation of data center providing flexibility, for the following paper "AI-focused HPC Data Centers Can Provide More Power Grid Flexibility and at Lower Cost".
Lang-8 Preprocessed Dataset (for GED):
The controller area network (CAN) bus has emerged as the de facto standard for in-vehicle networks (IVNs) around the globe. Safety-critical components (e.g., the brakes, the engine, the transmission) depend on the CAN bus for expedient, reliable communication. Unfortunately, while the CAN bus was designed to be resilient under harsh operating conditions, it was not designed to be resilient under adversarial conditions. Standard security practices such as authentication, authorization, and encryption are completely lacking when it comes to the CAN bus. Researchers have since developed authentication, authorization, and encryption specifications for the CAN bus, but retroactive implementation of said security controls would be exorbitantly expensive—in terms of hardware, labor, engineering effort, and monetary cost. Therefore, the automotive intrusion detection system (IDS) has emerged in the literature as a low-cost, low-effort solution to the automotive [in]security problem. However, d
High-resolution early gastric cancer (EGC) detection and analysis: Patient Data：Datasets often include images from patients diagnosed with gastric cancer, specifically distinguishing between early gastric cancer (EGC) and Non -pathogenic gastric cancer (NGC). The study utilized data from 341 patients, with 124 classified as EGC and 217 as NGC. Image Types: High-resolution images are typically obtained from endoscopy image. Data Volume: The size of datasets mentioned a dataset of 1120 images specifically for EGC detection and 2150 images for NGC.
In the context of rapid advancements in industrial automation, vision-based robotic grasping plays an increasingly crucial role. In order to enhance visual recognition accuracy, the utilization of large-scale datasets is imperative for training models to acquire implicit knowledge related to the handling of various objects. Creating datasets from scratch is a time and labor-intensive process. Moreover, existing datasets often contain errors due to automated annotations aimed at expediency, making the improvement of these datasets a substantial research challenge. Consequently, several issues have been identified in the annotation of grasp bounding boxes within the popular Jacquard Grasp. We propose utilizing a Human-In-The-Loop(HIL) method to enhance dataset quality. This approach relies on backbone deep learning networks to predict object positions and orientations for robotic grasping. Predictions with Intersection over Union (IOU) values below 0.2 undergo an assessment by human oper