Omkar2118/datasets huggingface dataset:
ForgeryNet
Full name: ForgeryNet

We construct the ForgeryNet dataset, an extremely large face forgery dataset with unified annotations in image- and video-level data across four tasks: 1) Image Forgery Classification, including two-way (real / fake), three-way (real / fake with identity-replaced forgery approaches / fake with identity-remained forgery approaches), and n-way (real and 15 respective forgery approaches) classification. 2) Spatial Forgery Localization, which segments the manipulated area of fake images compared to their corresponding source real images. 3) Video Forgery Classification, which re-defines the video-level forgery classification with manipulated frames in random positions. This task is important because attackers in real world are free to manipulate any target frame. and 4) Temporal Forgery Localization, to localize the temporal segments which are manipulated. ForgeryNet is by far the largest publicly available deep face forgery dataset in terms of data-scale (2.9 million images, 221,247 videos), manipulations (7 image-level approaches, 8 video-level approaches), perturbations (36 independent and more mixed perturbations) and annotations (6.3 million classification labels, 2.9 million manipulated area annotations and 221,247 temporal forgery segment labels). We perform extensive benchmarking and studies of existing face forensics methods and obtain several valuable observations.
ECG Heartbeat Categorization Dataset

This dataset is composed of two collections of heartbeat signals derived from two famous PhysioNet datasets in heartbeat classification, the MIT-BIH Arrhythmia Dataset and the PTB Diagnostic ECG Database. The number of samples in both collections is large enough for training a deep neural network.

This dataset has been used in exploring heartbeat classification using deep neural network architectures, and observing some of the capabilities of transfer learning on it. The signals correspond to electrocardiogram (ECG) shapes of heartbeats for the normal case and the cases affected by different arrhythmias and myocardial infarction. These signals are preprocessed and segmented, with each segment corresponding to a heartbeat.

Introducing paper: ECG Heartbeat Classification: A Deep Transferable Representation
3D Datasets of Broccoli in the Field
Full name: 3D Datasets of Broccoli in the Field

This work was undertaken by members of the Lincoln Centre for Autonomous Systems, University of Lincoln, UK. The four data collection sessions were conducted at three different sites in Lincolnshire, UK and one in Murcia, Spain (see Fig. 1). The sessions were conducted at the beginning and towards the end of harvesting season in UK and at the end of the harvest in Spain. The variety of broccoli plants grown in UK is called Iron Man whilst the variety grown in Spain is called Titanium.The weather during UK data capture included a mixture of different conditions including sunny, overcast and raining with broccoli varying in maturity levels from small to larger to already harvested, while the conditions for data capture in Spain included strong sunlight and mature plants at the very end of the harvesting season. The tractor was driven through the broccoli field at a slow walking speed with two rows of broccoli plants being imaged by the RGB-D sensor.
ShadowLink

ShadowLink dataset is designed to evaluate the impact of entity overshadowing on the task of entity disambiguation. Paper: "Robustness Evaluation of Entity Disambiguation Using Prior Probes: the Case of Entity Overshadowing" by Vera Provatorova, Svitlana Vakulenko, Samarth Bhargav, Evangelos Kanoulas. EMNLP 2021.

Introducing paper: Robustness Evaluation of Entity Disambiguation Using Prior Probes:the Case of Entity Overshadowing
MFA
Full name: Many Faces of Anger

The **MFA (Many Faces of Anger)** dataset includes 200 in-the-wild videos from North American and Persian cultures with fine-grained labels of: 'annoyed', 'anger', 'disgust', 'hatred' and 'furious' and 13 related emojis.

Image source: [https://arxiv.org/pdf/2112.05267.pdf](https://arxiv.org/pdf/2112.05267.pdf)

Introducing paper: The Many Faces of Anger: A Multicultural Video Dataset of Negative Emotions in the Wild (MFA-Wild)
The RoboCup Rescue Dataset

In this paper, we introduce a victim dataset for the RoboCup Rescue competitions. The RoboCup Rescue robots have to collect points within several disciplines, e.g. a search task within an area to survey simulated baby doll (victim).

When a robot comes across a victim, a heat detector does not completely proof if this is a living being and not just a heat emitting somewhat else. Further investigations are necessary so that a face detection could prove the existence of a victim. Lots of face detection approaches can be found in literature, which manly are used for human face recognition. These cannot be straightforward used for victim faces which are, in case of the RoboCup Rescue competitions, typically dolls. Thus we present the results of standard approaches and developed an own approach via bag-of-visual-words (BoVW).
PHANTOM
Full name: Physical Anomalous Trajectory or Motion (PHANTOM)

To evaluate the presented approaches, we created the Physical Anomalous Trajectory or Motion (PHANTOM) dataset consisting of six classes featuring everyday objects or physical setups, and showing nine different kinds of anomalies. We designed our classes to evaluate detection of various modes of video abnormalities that
are generally excluded in video AD settings.

The train and test sets of each class contain approximately 30 videos of varying lengths. The train set contains only normal videos, while the test set is evenly balanced between normal and anomalous videos. The classes were designed to be of varying difficulties and to feature different types of anomalies. For example, the window class was filmed in multiple lighting scenarios to increase variance. The normal videos include motion that follows an expected trajectory (pendulum, keyboard) or an expected movement (window). The sushi class features procedural motion, while candle and magnets feature more subtle motion that only appears locally.
The anomalous videos can feature an interference of the regular motion (window, candle, magnets), an added or removed step in the usual procedure (sushi), motion that follows a different trajectory (pendulum, keyboard), or contains a different object (pendulum).

Introducing paper: Approaches Toward Physical and General Video Anomaly Detection