Omkar2118/datasets huggingface dataset:
Sims4Action

* **The Sims4Action Dataset**: a videogame-based dataset for Synthetic→Real domain adaptation for human activity recognition.

* **Goal** : Exploring the concept of constructing training examples for Activities of Daily Living (ADL) recognition by playing life simulation video games.  
* ** ***Sims4Action*  dataset** is created with the commercial game THE SIMS 4**   by executing actions-of-interest within the game in a "top-down" manner. It features ten hours of video material of eight diverse characters and multiple environments.  Ten actions are selected to have a direct correspondence to categories covered in the real-life dataset Toyota Smarthome [2] to enable the research of Synthetic→Real transfer in action recognition.
* **Two benchmarks** :* Gaming→Gaming* (training and evaluation on Sims4Action)  and *Gaming→Real* (training on Sims4Action, evaluation on the real Toyota Smarthome data [2]).
* **Main challenge: *Gaming→Real* domain adaptation**  
  While ADL recognition on gaming data is interesting from a theoretical perspective, the key challenge arises from transferring knowledge learned from simulated data to real-world applications. *Sims4Action* specifically provides a benchmark for this scenario since it describes a *Gaming→Real* challenge, which evaluates models on real videos derived from the existing Toyota Smarthome dataset . 

# References 

[1] [Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games.](http://arxiv.org/abs/2107.05617
)
Alina Roitberg*, David Schneider*, Aulia Djamal, Constantin Seibold, Simon Reiß, Rainer Stiefelhagen,
In *International Conference on Intelligent Robots and Systems (IROS)*, 2021
(* denotes equal contribution.)

[2] [Toyota smarthome: Real-world activities of daily living.](https://arxiv.org/pdf/2010.14982.pdf)
Srijan Das, Rui Dai, Michal Koperski, Luca Minciullo, Lorenzo Garattoni, Francois Bremond, Gianpiero Francesca,
In *International Conference on Computer Vision (ICCV)*, 2019.

Introducing paper: Let's Play for Action: Recognizing Activities of Daily Living by Learning from Life Simulation Video Games
GLIB: image dataset

data/images:

data/images/Base : 132 screenshots of game1 & game2 with UI display issues from 466 test reports.
data/images/Code : 9,412 screenshots of game1 & game2 with UI display issues generated by our Code augmentation method.
data/images/Normal: 7,750 screenshots of game1 & game2 without UI display issues collected by randomly traversing the game scene.
data/images/Rule(F) : 7,750 screenshots of game1 & game2 with UI display issues generated by our Rule(F) augmentation method.
data/images/Rule(R) : 7,750 screenshots of game1 & game2 with UI display issues generated by our Rule(R) augmentation method.
data/images/testDataSet : 192 screenshots with UI display issues from 466 test reports(exclude game1 & game2).

data/data_csv:

data/data_csv/Base : dataset for baseline method.
data/data_csv/Code : dataset for our Code Augmentation method.
data/data_csv/Rule(F) : dataset for our Rule(F) Augmentation method.
data/data_csv/Rule(R) : dataset for our Rule(R) Augmentation method.
data/data_csv/Code_plus_Rule(F) : dataset for our Code&Rule(F) Augmentation method.
data/data_csv/Code_plus_Rule(R) : dataset for our Code&Rule(R) Augmentation method.
data/data_csv/testDataSet : test dataset(normal image and real glitch images from 466 test reports).

Introducing paper: GLIB: Towards Automated Test Oracle for Graphically-Rich Applications