Omkar2118/datasets huggingface dataset:
SemTabNet

# Dataset Card for SemTabNet

This dataset accompanies the following [paper](https://arxiv.org/abs/2406.19102):

```
Title: Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs
Authors: Lokesh Mishra, Sohayl Dhibi, Yusik Kim, Cesar Berrospi Ramis, Shubham Gupta, Michele Dolfi, Peter Staar
Venue: Accepted at the NLP4Climate workshop in the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) 
```

In this paper, we propose **STATEMENTS** as a new knowledge model for storing quantiative information in a domain agnotic, uniform structure. The task of converting a raw input (table or text) to Statements is called Statement Extraction (SE). The statement extraction task falls under the category of universal information extraction.

- **Code Repository:** [SemTabNet repository](https://github.com/DS4SD/SemTabNet)
- **Arxiv Paper:** [Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs](https://arxiv.org/abs/2406.19102)
- **Point of Contact:** [IBM Research DeepSearch Team](https://ds4sd.github.io)


### Data Splits

There are three tasks supported by this dataset. The data for each three task is split in training, validation, and testing set. Additionally, we also provide the original annotations of the raw tables which are used to construct all other data.

|Task | Train   | Test | Valid |
| ----- | ------ | ----- | ---- |
| SE Direct | 103455 | 11682 | 5445 |
|SE Indirect 1D | 72580 | 8489 | 3821 |
|SE Indirect 2D |  93153 | 22839 | 4903 |

### Languages

The text in the dataset is in English.

### Source and Annotations

The source of this dataset and the annotation strategy is described in the paper.


### Citation Information

Arxiv: [https://arxiv.org/abs/2406.19102](https://arxiv.org/abs/2406.19102)

Introducing paper: Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs
CVCS
Full name: Cross-View Cross-Scene Multi-View Crowd Counting Dataset

CVCS is a synthetic multi-view people dataset, containing 31 scenes, where 23 are for training and the rest 8 for testing. The scene size varies from about 10m∗20m to 90m∗80m. Each scene contains 100 multi-view frames. The ground plane map resolution is 900×800, where each grid stands for 0.1 meters in the real world. In training, 5 views are randomly selected 5 times in each iteration per scene frame, and the same view number is randomly selected 21 times in evaluation.

Introducing paper: Cross-View Cross-Scene Multi-View Crowd Counting
CityStreet

Datasets for multi-view crowd counting in wide-area scenes. Includes our CityStreet dataset, as well as the counting and metadata for multi-view counting on PETS2009 and DukeMTMC.
CityStreet is a real-world city scene dataset collected around the intersection of a crowded street. The scene size of the dataset is around 58m×72m. The ground plane map resolution is 320×384.

Introducing paper: Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs
Deep Fashion3D V2
Full name: Deep Fashion3D V2

We present a major update upon the original version of Deep Fashion 3D dataset . Compared with the previous version of Deep Fashion3D dataset, Deep Fashion3D V2 is futher equipped with: (1) detailed registered garment meshes with category-specific triangulation, (2) high-resolution texture maps (2048 X 2048 px), (3) more precise and accurate feature line annotations, and (4) garment SMPL pose parameters. We believe this update will further boost the research in high-quality, real-world garment modeling and generation.

Introducing paper: Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images
DF40

Forgery Diversity: DF40 comprises 40 distinct deepfake techniques (both representive and SOTA methods are included), facilitating the detection of nowadays' SOTA deepfakes and AIGCs. We provide 10 face-swapping methods, 13 face-reenactment methods, 12 entire face synthesis methods, and 5 face editing.

Forgery Realism: DF40 includes realistic deepfake data created by highly popular generation software and methods, e.g., HeyGen, MidJourney, DeepFaceLab, to simulate real-world deepfakes. We even include the just-released DiT, SiT, PixArt-$\alpha$, etc.

Forgery Scale: DF40 offers million-level deepfake data scale for both images and videos.

Data Alignment: DF40 provides alignment between fake methods and data domains. Most methods (31) are generated under the FF++ and CDF domains. Using our fake data, you can further expand your evaluation (training on FF++ and testing on CDF).

Introducing paper: DF40: Toward Next-Generation Deepfake Detection