19,997 machine learning datasets
19,997 dataset results
The Grand central station dataset includes a video with 50,010 frames which is used for Scene Understanding and Crowd Analysis.
H3D (Humans in 3D) is a dataset of annotated people. The annotations include:
This dataset contains video shots for two different classes: tigers and cars. The shots were collected from 188 car ads (~1–2 min each) and 14 nature documentaries about tigers (~40 min), amounting to roughly 14 h of video. The videos were partitioned into shorter shots, and only those showing at least one instance of the class were kept. This produced 806 shots for the car and 1880 for the tiger class, typically 1–100 sec in length.
The POET (Pascal Objects Eye Tracking) is a dataset that consists of eye tracking data for the complete trainval set of ten objects classes (cat, dog, bicycle, motorbike, boat, aeroplane, horse, cow, sofa, dining table) from Pascal VOC 2012 (6,270 images in total). Each image is annotated with the eye movement record of five participants, whose task was to identify which object class was present in the image.
The automotive multi-sensor (AMUSE) dataset consists of inertial and other complementary sensor data combined with monocular, omnidirectional, high frame rate visual data taken in real traffic scenes during multiple test drives.
Dataset of annotated independently moving objects (IMO). This dataset contains left and right images, stereo images, stereo disparity from SGM, and vehicle labels as well as a ground truth annotations.
The LTIR dataset is a thermal infrared dataset for evaluation of Short-Term Single-Object (STSO) tracking.
The Family101 dataset is the a large-scale dataset of families across several generations. It contains 101 different families with distinct family names, including 206 nuclear families, 607 individuals, with 14,816 images. The dataset are composed of renowned public families.
KinFaceW consists of two kinship datasets: KinFaceW-I and KinFaceW-II. Face images were collected from the internet, including some public figure face images as well as their parents' or children's face images. Face images are captured under uncontrolled environments in two datasets with no restriction in terms of pose, lighting, background, expression, age, ethnicity, and partial occlusion. The difference of KinFaceW-I and KinFaceW-II is that face images with a kin relation were acquired from different photos in KinFaceW-I and the same photo in KinFaceW-II in most cases.
A large vehicle detection dataset with almost two million annotated vehicles for training and evaluating object detection methods for self-driving cars on freeways.
FRIDA and FRIDA2 are databases of numerical images easily usable to evaluate in a systematic way the performance of visibility and contrast restoration algorithms. FRIDA comprises 90 synthetic images of 18 urban road scenes. FRIDA2 comprises 330 synthetic images of 66 diverse road scenes. The view point is closed to the one of the vehicle's driver. To each image without fog is associated 4 foggy images and a depthmap. Different kind of fog are added on each of the 4 associated images: uniform fog, heterogeneous fog, cloudy fog, and cloudy heterogeneous fog. These scenes can be used to test visibility and contrast restoration algorithms intensively and in an objective way, as well as "shape from fog" algorithms. The calibration parameters of the camera are given.
This is a dataset for vehicle detection. It consists of:
FPV-O is a multi-subject first-person vision dataset of office activities. Office activities include person-to-person interactions, such as chatting and handshaking, person-to-object interactions, such as using a computer or a whiteboard, as well as generic activities such as walking. The videos in the dataset present a number of challenges that, in addition to intra-class differences and inter-class similarities, include frames with illumination changes, motion blur, and lack of texture.
The dataset includes recordings from 10 different users teaching the robot different common kitchen objects, that consists of synchronized recordings from three cameras and a microphone mounted on the robot:
RuFa (Ruqaa-Farsi) dataset contains images of text written in one of two Arabic fonts: Ruqaa and Nastaliq (Farsi). The dataset contains 40,000 synthesized image and 516 real one, 40,516 in total. Images are in RGB JPG format at 100×100px. Text in the images has varying number of words, position, size, and opacity.
The medaka (Oryzias latipes) and the zebrafish (Danio rerio) are used as a model organism for a variety of subjects in biomedical research. The presented work aims to study the potential of automated ventricular dimension estimation through heart segmentation in medaka. For more on this, it's time for a closer look on our paper and the supplementary materials.
The MOBIO database consists of bi-modal (audio and video) data taken from 152 people. The database has a female-male ratio or nearly 1:2 (100 males and 52 females) and was collected from August 2008 until July 2010 in six different sites from five different countries. This led to a diverse bi-modal database with both native and non-native English speakers.
Deeply vocal characterizer is a human nonverbal vocalization dataset. This sample dataset consists of about 0.6 hours(56.7 hours in the full set) of audio(16 kHz, 16-bit, mono) across 16 human nonverbal vocalization classes, including throat-clearing, coughing, laughing, panting, and etc. The audio contents are crowdsourced by the general public of South Korea.
Deeply Korean read speech corpus contains pairs of Korean speakers reading a script with 3 distinct text sentiments (negative, neutral, positive), with 3 distinct voice sentiments (negative, neutral, positive), are recorded. The recordings took place in 3 different types of places, which are an anechoic chamber, studio apartment, and dance studio, of which the level of reverberation differs. And in order to examine the effect of the distance of mic from the source and device, every experiment is recorded at 3 distinct distances with 2 types of smartphone, iPhone X, and Galaxy S7.
Deeply Parent-Child Vocal Interaction contains the interaction of 24 pairs of parent and child(total 48 speakers), such as reading fairy tales, singing children’s songs, conversing, and others, is recorded. The recordings took place in 3 different types of places, which are an anechoic chamber, studio apartment, and dance studio, of which the level of reverberation differs. And in order to examine the effect of the distance of mic from the source and device, every experiment is recorded at 3 distinct distances) with 2 types of smartphone, iPhone X, and Galaxy S7.