Omkar2118/datasets huggingface dataset:
Freiburg RGB-D People
Full name: Freiburg RGB-D People

The **Freiburg RGB-D People** dataset contains 3000+ RGB-D frames acquired in a university hall from three vertically mounted Kinect sensors. The data contains mostly upright walking and standing persons seen from different orientations and with different levels of occlusions.

Source: [http://www2.informatik.uni-freiburg.de/~spinello/RGBD-dataset.html](http://www2.informatik.uni-freiburg.de/~spinello/RGBD-dataset.html)
Image Source: [http://www2.informatik.uni-freiburg.de/~spinello/RGBD-dataset.html](http://www2.informatik.uni-freiburg.de/~spinello/RGBD-dataset.html)
Fraunhofer IPA Bin-Picking
Full name: Fraunhofer IPA Bin-Picking

The **Fraunhofer IPA Bin-Picking** dataset is a large-scale dataset comprising both simulated and real-world scenes for various objects (potentially having symmetries) and is fully annotated with 6D poses. A pyhsics simulation is used to create scenes of many parts in bulk by dropping objects in a random position and orientation above a bin. Additionally, this dataset extends the Siléane dataset by providing more samples. This allows to e.g. train deep neural networks and benchmark the performance on the public Siléane dataset

Source: [https://www.bin-picking.ai/en/dataset.html](https://www.bin-picking.ai/en/dataset.html)
Image Source: [https://arxiv.org/abs/1912.12125](https://arxiv.org/abs/1912.12125)

Introducing paper: Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking
PAVIS RGB-D
Full name: PAVIS RGB-D

**PAVIS RGB-D** is a dataset for person re-identification using depth information. The main motivation is that techniques such as  SDALF fail when the individuals change their clothing, therefore they cannot be used for long-term video surveillance. Depth information is the solution to deal with this problem because it stays constant for a longer period of time. The dataset is composed by four different groups of data collected using the Kinect. The first group of data has been obtained by recording 79 people with a frontal view, walking slowly, avoiding occlusions and with stretched arms ("Collaborative"). This happened in an indoor scenario, where the people were at least 2 meters away from the camera. The second ("Walking1") and third ("Walking2") groups of data are composed by frontal recordings of the same 79 people walking normally while entering the lab where they normally work. The fourth group ("Backwards") is a back view recording of the people walking away from the lab.
The dataset creators provide 5 synchronized information for each person: 1) a set of 5 RGB images, 2) the foreground masks, 3) the skeletons, 4) the 3d mesh (ply), 5) the estimated floor.

Source: [https://www.iit.it/research/lines/pattern-analysis-and-computer-vision/pavis-datasets/534-rgb-d-person-re-identification-dataset](https://www.iit.it/research/lines/pattern-analysis-and-computer-vision/pavis-datasets/534-rgb-d-person-re-identification-dataset)
Image Source: [https://www.iit.it/research/lines/pattern-analysis-and-computer-vision/pavis-datasets/534-rgb-d-person-re-identification-dataset](https://www.iit.it/research/lines/pattern-analysis-and-computer-vision/pavis-datasets/534-rgb-d-person-re-identification-dataset)

Introducing paper: Re-identification with RGB-D Sensors
Couples Therapy
Full name: Couples Therapy Corpus

The **Couples Therapy** corpus contains audio, video recordings and manual transcriptions of conversations between 134 real-life couples attending marital therapy. In each session, one person selected a topic that was discussed over 10 minutes with the spouse. At the end of the session, both speakers were rated separately on 33 “behavior codes” by multiple annotators based on the Couples Interaction and Social Support Rating Systems. Each behavior was rated on a Likert scale from 1, indicating absence, to 9, indicating strong presence. A session-level rating was obtained for each speaker by averaging the annotator ratings. This process was repeated for the spouse, resulting in 2 sessions per couple at a time. The total number of sessions per couple varied between 2 and 6.

Source: [Modeling Interpersonal Influence of Verbal Behaviorin Couples Therapy Dyadic Interactions](https://arxiv.org/abs/1805.09436)
Raider
Full name: Raider

The **Raider** dataset collects fMRI recordings of 1000 voxels from the ventral temporal cortex, for 10 healthy adult participants passively watching the full-length movie “Raiders of the Lost Ark”.

Source: [Time-Resolved fMRI Shared Response Model using Gaussian Process Factor Analysis](https://arxiv.org/abs/2006.05572)
Image Source: [https://arxiv.org/abs/1909.12537](https://arxiv.org/abs/1909.12537)

Introducing paper: A common, high-dimensional model of the representational space in human ventral temporal cortex
StarCraft II Learning Environment
Full name: StarCraft II Learning Environment

The **StarCraft II Learning Environment** (S2LE) is a reinforcement learning environment based on the game StarCraft II. The environment consists of three sub-components: a Linux StarCraft II binary, the StarCraft II API and PySC2. The StarCraft II API allows programmatic control of StarCraft II. It can be used to start a game, get observations, take actions, and review replays. PyC2 is a Python environment that wraps the StarCraft II API to ease the interaction between Python reinforcement learning agents and StarCraft II. It defines an action and observation specification, and includes a random agent and a handful of rule-based agents as examples. It also includes some mini-games as challenges and visualization tools to understand what the agent can see and do.

Source: [https://github.com/deepmind/pysc2](https://github.com/deepmind/pysc2)
Image Source: [https://github.com/deepmind/pysc2](https://github.com/deepmind/pysc2)

Introducing paper: StarCraft II: A New Challenge for Reinforcement Learning