Omkar2118/datasets huggingface dataset:
FaceVerse
Full name: FaceVerse-High Quality 3D Face Dataset

FaceVerse-High Quality 3D Face Dataset contains 2,688 high-quality head scans (21 expressions from 128 identities) captured by a dense DLSR rig. For each scan, we provide the 3D model (.obj), the corresponding texture map (.jpeg) and the FaceVerse fitted model (.ply) with the same topology.

Introducing paper: FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset
EmoDB Dataset
Full name: Berlin Database of Emotional Speech

The EMODB database is the freely available German emotional database. The database is created by the Institute of Communication Science, Technical University, Berlin, Germany. Ten professional speakers (five males and five females) participated in data recording. The database contains a total of 535 utterances. The EMODB database comprises of seven emotions: 1) anger; 2) boredom; 3) anxiety; 4) happiness; 5) sadness; 6) disgust; and 7) neutral. The data was recorded at a 48-kHz sampling rate and then down-sampled to 16-kHz.

Citation:  
Felix Burkhardt, Astrid Paeschke, Miriam Rolfes, Walter Sendlmeier und Benjamin Weiss
A Database of German Emotional Speech
Proceedings Interspeech 2005, Lissabon, Portugal
Construction Vehicle Image Dataset |Trucks|Tractor etc.

This dataset is an extremely challenging set of over 20,000+ original Construction vehicle images captured and crowdsourced from over 600+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs.

- Dataset Features
- Dataset size : 20,000+
- Captured by : Over 1000+ crowdsource contributors
- Resolution : 100% of the images are HD and above (1920x1080 and above)
- Location : Captured with 600+ cities accross India
- Diversity : Various lighting conditions like day, night, varied distances, view points etc.
- Device used : Captured using mobile phones in 2020-2022
- Usage : Construction site object detection, workplace safety monitoring, self driving systems, etc.


- Available Annotation formats
- COCO, YOLO, PASCAL-VOC, Tf-Record

**To download full datasets or to submit a request for your dataset needs, please drop a mail on sales@datacluster.ai . Visit www.datacluster.ai to know more.**  

This dataset is collected by DataCluster Labs. To download full dataset or to submit a request for your new data collection needs, please drop a mail to: sales@datacluster.ai
SWAT A7
Full name: Secure Water Treatment (SWaT)

11 days of continuous operation: 7 under normal operation and 4 days with attack scenarios:
+ Collected network traffic & all the values obtained from all the 51 sensors and actuators 
+ Data labelled according to normal and abnormal behaviours
+ Attack Scenarios: Derived through the attack models developed by our research team. The attack model considers the intent space of a CPS as an attack model. 41 attacks were launched during the 4 days and are described in the PDF.
DigiLeTs
Full name: Digit- and Letter Trajectories

A dataset with $23\,870$ digital trajectories (i.e. time series) of handwritten lower- and uppercase Latin letters and Arabic numbers ($a$-$z$, $A$-$Z$, $0$-$9$), generated by $77$ experts using a Wacom Pen Tablet. An expert is considered a proficient user of the recorded symbols, in this case adult native German speakers.

DigiLetTs was created to extend the [Omniglot dataset](https://github.com/brendenlake/omniglot) and contains five variants per character per subject to allow the quantification of intra-subject variability and to assess and account for individual writing styles. The determination and imitation of subject-dependent writing styles is introduced as a new task in [this paper](link-to-paper).

For more information about the dataset, please refer to the repository (Homepage button below).