3,275 machine learning datasets
3,275 dataset results
“How do I get a full refund from Expedia? How do I get a full refund from Expedia? – Call ☎️ +【１^８０５^３３０^４０５６】 for Quick Help & Exclusive Travel Deals!Have a question about your booking? Call ☎️ +【１^８０５^３３０^４０５６】 now to get live, expert support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Get clear answers fast and access limited-time travel offers that make your next trip easier, cheaper, and stress-free. Don’t wait—call today and save!
“How do I get a full refund from Expedia? How do I get a full refund from Expedia? – Call ☎️ +【１^８０５^３３０^４０５６】 for Quick Help & Exclusive Travel Deals!Have a question about your booking? Call ☎️ +【１^８０５^３３０^４０５６】 now to get live, expert support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Get clear answers fast and access limited-time travel offers that make your next trip easier, cheaper, and stress-free. Don’t wait—call today and save!
“How do I get a full refund from Expedia? How do I get a full refund from Expedia? – Call ☎️ +【１^８０５^３３０^４０５６】 for Quick Help & Exclusive Travel Deals!Have a question about your booking? Call ☎️ +【１^８０５^３３０^４０５６】 now to get live, expert support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Get clear answers fast and access limited-time travel offers that make your next trip easier, cheaper, and stress-free. Don’t wait—call today and save!
“How do I get a full refund from Expedia? How do I get a full refund from Expedia? – Call ☎️ +【１^８０５^３３０^４０５６】 for Quick Help & Exclusive Travel Deals!Have a question about your booking? Call ☎️ +【１^８０５^３３０^４０５６】 now to get live, expert support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Get clear answers fast and access limited-time travel offers that make your next trip easier, cheaper, and stress-free. Don’t wait—call today and save!
“How do I get a full refund from Expedia? How do I get a full refund from Expedia? – Call ☎️ +【１^８０５^３３０^４０５６】 for Quick Help & Exclusive Travel Deals!Have a question about your booking? Call ☎️ +【１^８０５^３３０^４０５６】 now to get live, expert support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Get clear answers fast and access limited-time travel offers that make your next trip easier, cheaper, and stress-free. Don’t wait—call today and save!
“How do I get a full refund from Expedia? How do I get a full refund from Expedia? – Call ☎️ +【１^８０５^３３０^４０５６】 for Quick Help & Exclusive Travel Deals!Have a question about your booking? Call ☎️ +【１^８０５^３３０^４０５６】 now to get live, expert support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Get clear answers fast and access limited-time travel offers that make your next trip easier, cheaper, and stress-free. Don’t wait—call today and save!
The WASABI Song Corpus is a large corpus of songs enriched with metadata extracted from music databases on the Web, and resulting from the processing of song lyrics and from audio analysis. More specifically, given that lyrics encode an important part of the semantics of a song, the authors focus on the description of the methods they proposed to extract relevant information from the lyrics, such as their structure segmentation, their topics, the explicitness of the lyrics content, the salient passages of a song and the emotions conveyed. The corpus contains 1.73M songs with lyrics (1.41M unique lyrics) annotated at different levels with the output of the above mentioned methods. Such corpus labels and the provided methods can be exploited by music search engines and music professionals (e.g. journalists, radio presenters) to better handle large collections of lyrics, allowing an intelligent browsing, categorization and segmentation recommendation of songs.
The ISI_Bengali_Character dataset contains 158 classes of Bengali numerals, characters or their parts. 19,530 Bengali character samples are available. Most of the images in the dataset are synthesized.
Extended BBC Pose is a pose estimation dataset which extends the BBC Pose dataset with 72 additional training videos. Combined with the original BBC TV dataset, the dataset contains 92 videos (82 training, 5 validation and 5 testing), i.e. around 7 million frames. The frames of the new 72 videos are automatically assigned joint locations (used as ground truth for training) with the tracker of Charles et al. IJCV'13.
ChaLearn Pose is a subset of the ChaLearn 2013 Multi-modal gesture dataset from Escalera et al. ICMI'13, which contains 23 hours of Kinect data of 27 persons performing 20 Italian gestures. The data includes RGB, depth, foreground segmentations and full body skeletons. In this dataset, both the training and testing labels are noisy (from Kinect).
The UBIRIS.v2 iris dataset contains 11,102 iris images from 261 subjects with 10 images each subject. The images were captured under unconstrained conditions (at-a-distance, on-the-move and on the visible wavelength), with realistic noise factors.
Collection of various motion capture recordings (walking, dancing, sports, and others) performed by over 140 subjects. The database contains free motions which you can download and use. There is a zip file of all asf/amc's on the FAQs page.
The KIT Whole-Body Human Motion Database is a large-scale dataset of whole-body human motion with methods and tools, which allows a unifying representation of captured human motion, and efficient search in the database, as well as the transfer of subject-specific motions to robots with different embodiments. Captured subject-specific motion is normalized regarding the subject’s height and weight by using a reference kinematics and dynamics model of the human body, the master motor map (MMM). In contrast with previous approaches and human motion databases, the motion data in this database consider not only the motions of the human subject but the position and motion of objects with which the subject is interacting as well. In addition to the description of the MMM reference model, See the paper for procedures and techniques used for the systematic recording, labeling, and organization of human motion capture data, object motions as well as the subject–object relations.
The Berkeley Motion Segmentation Dataset (BMS-26) is a dataset for motion segmentation, which consists of 26 video sequences with pixel-accurate segmentation annotation of moving objects. A total of 189 frames is annotated. 12 of the sequences are taken from the Hopkins 155 dataset and new annotation is added.
Plant Centroids is a dataset for stem emerging points (SEP) detection in RGB and NIR image data. The dataset is meant to aid the construction of agricultural robots, where detecting SEPs is an important perception task (to position weeding or fertilizing tools at the plant’s center and finding natural landmarks in the field environment). The dataset contains annotations for ~2000 image sets with a broad variance of plant species and growth stages.
Freiburg Across Seasons captures long-term perceptual changes across a span of 3 years. Image sequences were recorded with a forward facing bumblebee stereo camera mounted on a car. During summer, the camera was mounted outside the car where as during winters the camera was inside the car. The image sequences are recorded at relatively low frame rates of 1Hz and 4Hz. All the images have a resolution of 1024 × 768 (width×height) and are JPEG compressed. In total, there are ground truth matchings for 8,133 images for localization based on GPS position.
Freiburg Block Tasks is a dataset for robot skill learning. It consists of two datasets. The first data set consisted of three simulated robot tasks: stacking (A), color pushing (B) and color stacking (C). The data set contains 300 multi-view demonstration videos per task. The tasks are simulated with PyBullet. Of these 300 demonstrations, 150 represent unsuccessful executions of the different tasks. The authors found it helpful to add unsuccessful demonstrations in the training of the embedding to enable training RL agents on it. Without fake examples, the distances in the embedding space for states not seen during training might be noisy. The test set contains the manipulation of blocks. Within the validation set, the blocks are replaced by cylinders of different colors. The second data set includes real-world human executions of the simulated robot tasks (A, B and C), as well as demonstrations for a task where one has to first separate blocks in order to stack them (D). For each tas
The Cityscapes-Motion dataset is a supplement to the semantic annotations provided by the Cityscapes dataset, containing 2975 training images and 500 validation images. The dataset creators provide manually annotated motion labels for the category of cars. The images are of resolution 2048×1024 pixels. The task to learn is not just semantic segmentation but also the motion status of the objects.
The KITTI-Motion dataset contains pixel-wise semantic class labels and moving object annotations for 255 images taken from the KITTI Raw dataset. The images are of resolution 1280×384 pixels and contain scenes of freeways, residential areas and inner-cities. The task is not just to semantically segment objects but also to identify their motion status.
Freiburg Lighting Adaptable Map Tracking is a dataset for camera trajectory estimation. The dataset consists of two subdatasets, each consisting of a Lighting Adaptable Map and three camera trajectories recorded under varying lighting conditions. The map meshes are stored in PLY format with custom properties and elements. The trajectories contain synchronized RGB-D images, exposure times and gains, ground-truth light settings and camera poses, as well as the camera tracking results presented in the paper.