68 machine learning datasets
68 dataset results
This dataset contains a large set (~3.2 Million) of high quality expert trajectories generated from a geometrically consist hybrid planner in a wide variety of environment (~575,000 environments). We created this dataset to explore the capabilities of neural networks to learn complex robotic motion, mimicking a traditional planner.
Situated Dialogue Navigation (SDN) is a navigation benchmark of 183 trials with a total of 8415 utterances, around 18.7 hours of control streams, and 2.9 hours of trimmed audio. SDN is developed to evaluate the agent's ability to predict dialogue moves from humans as well as generate its own dialogue moves and physical navigation actions.
UESTC-MMEA-CL is a new multi-modal activity dataset for continual egocentric activity recognition, which is proposed to promote future studies on continual learning for first-person activity recognition in wearable applications. Our dataset provides not only vision data with auxiliary inertial sensor data but also comprehensive and complex daily activity categories for the purpose of continual learning research. UESTC-MMEA-CL comprises 30.4 hours of fully synchronized first-person video clips, acceleration stream and gyroscope data in total. There are 32 activity classes in the dataset and each class contains approximately 200 samples. We divide the samples of each class into the training set, validation set and test set according to the ratio of 7:2:1. For the continual learning evaluation, we present three settings of incremental steps, i.e., the 32 classes are divided into {16, 8, 4} incremental steps and each step contains {2, 4, 8} activity classes, respectively.
This package provides utilities for generation, filtering, solving, visualizing, and processing of mazes for training ML systems. Primarily built for the maze-transformer interpretability project. You can find our paper on it here: http://arxiv.org/abs/2309.10498
VATEX Adverbs is a subset from VATEX with extracted verb-adverb annotations. VATEX Adverbs contains 34 adverbs appearing across 135 actions, forming 1,550 unique action-adverb pairs in 14,617 video clips.
ActivityNet Adverbs is a subset from the ActivityNet dataset with extracted verb-adverb annotations. ActivityNet Adverbs contains 20 adverbs appearing across 114 actions, forming 643 unique action-adverb pairs in 3,099 video clips.
MSR-VTT Adverbs is a subset from MSR-VTT with extracted verb-adverb annotations. MSR-VTT Adverbs contains 18 adverbs appearing across 106 actions, forming 464 unique action-adverb pairs in 1,824 video clips.
StarData is a StarCraft: Brood War replay dataset, with 65,646 games. The full dataset after compression is 365 GB, 1535 million frames, and 496 million player actions. The entire frame data was dumped out at 8 frames per second.
RL Unplugged is suite of benchmarks for offline reinforcement learning. The RL Unplugged is designed around the following considerations: to facilitate ease of use, we provide the datasets with a unified API which makes it easy for the practitioner to work with all data in the suite once a general pipeline has been established. This is a dataset accompanying the paper RL Unplugged: Benchmarks for Offline Reinforcement Learning.
Thumb Index 1000 (TI1K) is a dataset of 1000 hand images with the hand bounding box, and thumb and index fingertip positions. The dataset includes the natural movement of the thumb and index fingers making it suitable for mixed reality (MR) applications.
Existing image/video datasets for cattle behavior recognition are mostly small, lack well-defined labels, or are collected in unrealistic controlled environments. This limits the utility of machine learning (ML) models learned from them. Therefore, we introduce a new dataset, called Cattle Visual Behaviors (CVB), that consists of 502 video clips, each fifteen seconds long, captured in natural lighting conditions, and annotated with eleven visually perceptible behaviors of grazing cattle. By creating and sharing CVB, our aim is to develop improved models capable of recognizing all important cattle behaviors accurately and to assist other researchers and practitioners in developing and evaluating new ML models for cattle behavior classification using video data. The dataset is presented in the form of following three sub-directories. 1. raw_frames: contains 450 frames in each sub folder representing a 15 second video taken at a frame rate of 30 FPS. 2. annotations: contains the json file
MiniWob++ is a suite of web-browser based tasks introduced in Liu et al. (2018) (an extension of the earlier MiniWob task suite (Shi et al., 2017)). Tasks range from simple button clicking to complex form-filling, for example, to book a flight when given particular instructions (Fig. 1a). Programmatic rewards are available for each task, permitting standard reinforcement learning techniques.
3DYoga90 is organized within a three-level label hierarchy. It stands out as one of the most comprehensive open datasets, featuring the largest collection of RGB videos and 3D skeleton sequences among publicly available resources.
The Robot House Multi-View dataset (RHM) contains four views: Front, Back, Ceiling, and Robot Views. There are 14 classes with 6701 video clips for each view, making a total of 26804 video clips for the four views. The lengths of the video clips are between 1 to 5 seconds. The videos with the same number and the same classes are synchronized in different views.
This dataset is composed of paired videos of people dancing 3 different music styles: Ballet, Michael Jackson and Salsa. It contains multimodal data (visual data, temporal-graphs and audio) careful-selected from publicly available videos of dancers performing representative movements of the music style and audio data from the respective styles.
This data contains about 2500 trajectories (with images and actions) of a Sawyer robot interacting with various objects.
This dataset contains the bus trajectory dataset collected by 6 volunteers who were asked to travel across the sub-urban city of Durgapur, India, on intra-city buses (route name: 54 Feet). During the travel, the volunteers captured sensor logs through an Android application installed on COTS smartphones.
LARa is the first freely accessible logistics-dataset for human activity recognition. In the ’Innovationlab Hybrid Services in Logistics’ at TU Dortmund University, two picking and one packing scenarios with 14 subjects were recorded using OMoCap, IMUs, and an RGB camera. 758 minutes of recordings were labeled by 12 annotators in 474 person-hours. The subsequent revision was carried out by 4 revisers in 143 person-hours. All the given data have been labeled and categorised into 8 activity classes and 19 binary coarse-semantic descriptions, also called attributes.
Context of the data sets The Zooniverse platform (www.zooniverse.org) has successfully built a large community of volunteers contributing to citizen science projects. Galaxy Zoo and the Milky Way Project were hosted there.
We present a new simulated dataset for pedestrian action anticipation collected using the CARLA simulator. To generate this dataset, we place a camera sensor on the ego-vehicle in the Carla environment and set the parameters to those of the camera used to record the PIE dataset (i.e., 1920x1080, 110° FOV). Then, we compute bounding boxes for each pedestrian interacting with the ego vehicle as seen through the camera's field of view. We generated the data in two urban environments available in the CARLA simulator: Town02 and Town03.