Omkar2118/datasets huggingface dataset:
ChaosBench

We propose ChaosBench, a large-scale, multi-channel, physics-based benchmark for subseasonal-to-seasonal (S2S) climate prediction. 
It is framed as a high-dimensional video regression task that consists of 45-year, 60-channel observations 
for validating physics-based and data-driven models, and training the latter. 
Physics-based forecasts are generated from 4 national weather agencies with 44-day lead-time and serve as baselines to data-driven forecasts. 
Our benchmark is one of the first to incorporate physics-based metrics to ensure physically-consistent and explainable models. 
We establish two tasks: full and sparse dynamics prediction. 

🔗: [https://leap-stc.github.io/ChaosBench/](https://leap-stc.github.io/ChaosBench/)

📚: [https://arxiv.org/abs/2402.00712](https://arxiv.org/abs/2402.00712)

## Getting Started
**Step 1**: Clone the [ChaosBench](https://github.com/leap-stc/ChaosBench) Github repository

**Step 2**: Install package dependencies
```
cd ChaosBench
pip install -r requirements.txt
```

**Step 3**: Initialize the data space by running
```
cd data/
wget https://huggingface.co/datasets/LEAP/ChaosBench/resolve/main/process.sh
chmod +x process.sh
```
**Step 5**: Download the data 

```
# NOTE: you can also run each line one at a time to retrieve individual dataset

./process.sh era5            # Required: For input ERA5 data
./process.sh climatology     # Required: For climatology
./process.sh ukmo            # Optional: For simulation from UKMO
./process.sh ncep            # Optional: For simulation from NCEP
./process.sh cma             # Optional: For simulation from CMA
./process.sh ecmwf           # Optional: For simulation from ECMWF
```

Introducing paper: ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction
Google Local review
Full name: Google Local Data

## Description
This Dataset contains review information on Google map (ratings, text, images, etc.), business metadata (address, geographical info, descriptions, category information, price, open hours, and MISC info), and links (relative businesses) up to Sep 2021 in the United States.

## Citation
Please cite the following papers if you use the data in any way:

UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining
Jiacheng Li, Jingbo Shang, Julian McAuley
Annual Meeting of the Association for Computational Linguistics (ACL), 2022

Personalized Showcases: Generating Multi-Modal Explanations for Recommendations
An Yan, Zhankui He, Jiacheng Li, Tianyang Zhang, Julian Mcauley
The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2023
FFHQH
Full name: Flickr-Faces-HQ-Harmonization

A new dataset for portrait harmonization based on the [FFHQ](https://github.com/NVlabs/ffhq-dataset).
It contains real images, foreground masks, and synthesized composites.

Generating composite images has two steps: alpha mask generation and color augmentation.

[StyleMatte](https://arxiv.org/abs/2305.02981) - portrait matting network was used for mask generation.

Color augmentations: brightness, contrast, hue, sharpness adjustment.

FFHQH includes 60,000 triplets for train, 5000 for validation and 5000 for test. Triplet stands for <real image, mask, composite image>.

Introducing paper: PHNet: Patch-based Normalization for Portrait Harmonization
CstomStudio

- This dataset is composed of 69 individual objects and 57 meaningful pairs. 
- The objects cover a wide range of categories, including decor item, food, furniture, instrument, jewelry, luggage, person, pet, plant, plushie, scene, thing, toy, transportation, and wearable item.

Introducing paper: CustomVideo: Customizing Text-to-Video Generation with Multiple Subjects
MSU SR-QA Dataset
Full name: MSU Super-Resolution Quality Assessment Dataset

Our dataset was made of videos from MSU Video Upscalers Benchmark Dataset, MSU Video Super-Resolution Benchmark Dataset and MSU Super-Resolution for Video Compression Benchmark Dataset. Dataset consists of real videos (were filmed with 2 cameras), video games footages, movies, cartoons, dynamic ads. 

How we brought our dataset closer to completeness?
* The dataset covers a large number of use cases in the field of SR due to the large number of content types
* The dataset contains videos with completely different resolutions, FPS values: 8, 24, 25, 30, 60, as well as high and low spatio-temporal complexity
* Distorted videos were obtained using 46 SR methods, some of them were preprocessed with 5 codecs: aomenc, vvenc, x264, x265, uavs3es with different bitrates and qp values
* The dataset was manually checked for redundancy

Videos from benchmarks are FullHD video crops, since the subjective comparison was made on crops. Therefore, the resolution of all videos in the received dataset is low.