19,997 machine learning datasets
19,997 dataset results
A real-world stereo video dataset, containing 1200 frame pairs with real-world color and sharpness mismatches caused by beam splitter.
The laparoscopic surgery dataset is associated with our International Journal of Computer Assisted Radiology and Surgery (IJCARS) publication titled “DeSmoke-LAP: Improved Unpaired Image-to-Image Translation for Desmoking in Laparoscopic Surgery”. The training model of the proposed method is available as an open source on Github. We propose DeSmoke-LAP, a new method for removing smoke from real robotic laparoscopic hysterectomy videos. The proposed method is based on the unpaired image-to-image cycle-consistent generative adversarial network in which two novel loss functions, namely, inter-channel discrepancies and dark channel prior.
AI-based digital twins are at the leading edge of theIndustry 4.0 revolution, which are technologically empowered bythe Internet of Things and real-time data analysis. Information collected from industrial assets is produced in a continuous fashion, yielding data streams that must be processed under stringent timing constraints. Such data streams are usually subject to non-stationary phenomena, causing that the data distribution of the streams may change, and thus the knowledge captured by models used for data analysis may become obsolete (leading to the so-called concept drift effect). The early detection of thechange (drift) is crucial for updating the model’s knowledge, which is challenging especially in scenarios where the ground truth associated to the stream data is not readily available. Among many other techniques, the estimation of the model’s confidence has been timidly suggested in a few studies as a criterion for detecting drifts in unsupervised settings. The goal of this m
Microscopy images of shrub cross sections for instance segmentation of tree rings.
In AISIA-VN-Review-S and AISIA-VN-Review-F datasets, we first collect 450K customer reviewing comments from various e–commerce websites. Then, we manually label each review to be either positive or negative, resulting in 358,743 positive reviews and 100,699 negative reviews. We named this dataset the sentiment classification from reviews collected by AISIA, the full version (AISIA-VN-Review-F). However, in this work, we are interested in improving the model’s performance when the training data are limited; thus, we only consider a subset of up to 25K training reviews and evaluate the model on another 170K reviews. We refer to this subset from the full dataset as AISIA-VN-Review-S. It is important to emphasize that our team spends a lot of time and effort to manually classify each review into positive or negative sentiments.
The dataset includes the synthetic data generated from rendering the 3D meshes of LM objects and several household objects in Blender for training 6D pose estimation algorithms. The whole dataset contains synthetic data for 18 objects (13 from LM and 5 from household objects), with 20,000 data samples for each object. Each data sample includes an RGB image in .png format and a depth image in .exr format. Each sample has the annotations of mask labels in .png format and the ground truth pose labels saved in .json files. Apart from the training data, the 3D meshes of the objects and the pre-trained models of the 6D pose estimation algorithm are also included. The whole dataset takes approximately ~1T of storage memory.
The datasets of "Towards Lightweight Cross-domain Sequential Recommendation via External Attention-enhanced Graph Convolution Network" (DASFAA 2023)
The Bio-ML dataset provides five ontology pairs for both equivalence and subsumption ontology matching.
3D Computer Graphics is leveraged to generate a large and diverse dataset for training bike rotation estimators in bike parking assessment. By using 3D graphics software (Blender), the algorithm is able to accurately annotate the rotations of bikes with respect to the parking spot area in two axes y and z , which is crucial for training models for visual object-to-spot rotation estimation. Additionally, the ease of building the algorithm in Python made the generated dataset diverse with a wide range of variations in terms of parking space, lighting conditions, backgrounds, material textures, and colors, as well as objects and camera angles, to improve the generalization of the trained model. Overall, the use of 3D computer graphics allows for the efficient and precise generation of visual data for this task as well as for many potential tasks in computer vision.
"The Chicago Face Database was developed at the University of Chicago by Debbie S. Ma, Joshua Correll, and Bernd Wittenbrink. The CFD is intended for use in scientific research. It provides high-resolution, standardized photographs of male and female faces of varying ethnicity between the ages of 17-65. Extensive norming data are available for each individual model. These data include both physical attributes (e.g., face size) as well as subjective ratings by independent judges (e.g., attractiveness).
https://ega-archive.org/studies/EGAS00000000083
Monitoring and evaluating of driving behavior is the main goal of this paper that encourage us to develop a new system based on Inertial Measurement Unit (IMU) sensors of smartphones. In this system, a hybrid of Discrete Wavelet Transformation (DWT) and Adaptive Neuro Fuzzy Inference System (ANFIS) is used to recognize overall driving behaviors. The behaviors are classified into the safe, the semi-aggressive, and the aggressive classes that are adopted with Driver Anger Scale (DAS) self-reported questionnaire results. The proposed system extracts four features from IMU sensors in the forms of time series. They are decomposed by DWT in two levels and their energies are sent to six ANFISs. Each ANFIS models the different perception about driving behavior under uncertain knowledge and returns the similarity or dissimilarity between driving behaviors. The results of these six ANFISs are combined by three different decision fusion approaches. Results show that Coiflet-2 is the most suitable
Microscopy Images of the Drosophila Wing dataset are divided into two folders, Tumor/ No Tumor. The tumor folder has images of different stages of cancer, including both early and late stages. The organization of images was done in a way that the Tumor Folder has images that already have a Tumor or is going to develop cancer in the next few days. In contrast, the No Tumor Folder has images with no sign of cancer or a tiny tumor percentage that will be suppressed the following day.
In one round of sequencing, 5 fecal pellets from 2 pro-inflammatory environments (Harvard BRI/Johns Hopkins) and 2 pro-survival environments (Broad Institute/Jackson Labs) were sequenced at the 16s rDNA locus. In a second round of sequencing, 9 fecal pellets from Harvard BRI, 9 fecal pellets from Broad Institute, 6 fecal pellets from Harvard BRI mice transplanted with Harvard BRI feces, and 6 pellets from Harvard BRI mice transplanted with Broad feces were sequenced at the 16S rDNA locus
Over 20,000 annotated synthetic images and web-scraped images of bicyclists with bounding box annotations in Pascal VOC format.
Caselaw4 is a dataset of 350k common law judicial decisions from the U.S. Caselaw Access Project, of which 250k have been automatically annotated with binary outcome labels of AFFIRM and REVERSE.
This database is a database of backdoored neural networks intended for face recognition. The networks are of the FaceNet architecture and are trained on Casia-WebFace, with and without additional samples (which are the source of the backdoor). More information regarding backdoors and the project within which this fits can be found in the public release of the source code : https://gitlab.idiap.ch/bob/bob.paper.backdoored_facenets.biosig2022.
DocRED-FE is the DocRED with Fine-Grained Entity Type
The dataset contains 73 satellite images of different forests damaged by wildfires across Europe with a resolution of up to 10m per pixel. Data were collected from the Sentinel-2 L2A satellite mission and the target labels were generated from the Copernicus Emergency Management Service (EMS) annotations, with five different severity levels, ranging from undamaged to completely destroyed.
The ShapeIt dataset introduced by Alper et al. (2023) consists of 109 nouns and noun phrases along with the basic shape normally associated with that item, chosen from the set {circle, rectangle, triangle}.