Omkar2118/datasets huggingface dataset:
NIR2RGB VCIP Challange Dataset
Full name: VCIP2020 Grand Challenge on the NIR image colorization dataset

#This dataset provides the VCIP 2020 Grand Challenge on the NIR Image Colorization dataset.

##You can refer to https://jchenhkg.github.io/projects/NIR2RGB_VCIP_Challenge/ for a detailed description of this dataset.

## If you think this dataset is helpful, please feel free to cite our paper:

```
@inproceedings{yang2023cooperative,
  title={Cooperative Colorization: Exploring Latent Cross-Domain Priors for NIR Image Spectrum Translation},
  author={Yang, Xingxing and Chen, Jie and Yang, Zaifeng},
  booktitle={Proceedings of the 31st ACM International Conference on Multimedia},
  pages={2409--2417},
  year={2023}
}
```

Introducing paper: Cooperative Colorization: Exploring Latent Cross-Domain Priors for NIR Image Spectrum Translation
Underwater Trash Detection

# Underwater Trash Detection Dataset

## Overview
The **Underwater Trash Detection Dataset** is a custom-annotated dataset designed to address the challenges of underwater trash detection caused by varying environmental features. Publicly available datasets alone are insufficient for training deep learning models due to domain-specific variations in underwater conditions. This dataset offers a cumulative, self-annotated collection of underwater images for detecting and classifying trash, providing a strong foundation for deep learning research and benchmark testing.

---

## Dataset Summary

- **Total Images:** 9,576
- **Annotation Types:** Trash classification (plastic, trash, underwater debris) vs. environmental factors (fish, flora, fauna).
  
---

## Dataset Split

| **Split**   | **Percentage** | **Number of Images** |
|-------------|----------------|-----------------------|
| Train Set   | 76%            | 7,308                |
| Validation  | 19%            | 1,795                |
| Test Set    | 5%             | 473                  |

---

## Preprocessing

- **Image Resize:** All images are resized to **256x256** pixels using stretching for uniform input dimensions.

---

## Purpose
This dataset supports research in underwater trash detection while addressing storage and computational constraints in underwater mobile devices. It enables the development of optimized algorithms for efficient trash detection and classification using minimal resources.

---

## Citation

If you use this dataset in your research, please cite:

```bibtex
@InProceedings{10.1007/978-3-031-43360-3_24,
author="Walia, Jaskaran Singh and Seemakurthy, Karthik",
editor="Iida, Fumiya
and Maiolino, Perla
and Abdulali, Arsen
and Wang, Mingfeng",
title="Optimized Custom Dataset for Efficient Detection of Underwater Trash",
booktitle="Towards Autonomous Robotic Systems",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="292--303",
}

Introducing paper: Optimized Custom Dataset for Efficient Detection of Underwater Trash
SEED-VIG
Full name: SJTU Emotion EEG Dataset

The SEED-VIG dataset is composed of four parts.
EEG features include:
EEG_Feature_2Hz: EEG features (power spectral density: PSD, differential entropy: DE) from the total frequency band (1~50 Hz) with a 2 Hz frequency resolution. The fields "psd_movingAve", "psd_LDS", "de_movingAve", and "de_LDS" indicate PSD with a moving average, PSD with a linear dynamic system, DE with a moving average, and DE with a linear dynamic system, respectively. The data format is channel*sample_number*frequency_bands (17*885*25). The first 1–5 in the first dimension 'channel' correspond to temporal brain areas, and the last 7–17 correspond to posterior brain areas.
EEG_Feature_5Bands: This part is similar to the EEG_feature_2Hz file except that EEG features (PSD, DE) are extracted from five frequency bands: delta (1~4 Hz), theta (4~8 Hz), alpha (8~14 Hz), beta (14~31 Hz), and gamma (31~50 Hz). The data format is channel*sample number*frequency bands (17*885*5).
Forehead EEG feature files have a similar architecture to EEG features, but there are only four channels for the data tensor (4*885*25 and 4*885*5).
EOG features. The fields "features_table_ica", "features_table_minus", and "features_table_icav_minh" indicate forehead EOG features that correspond to different VEO and HEO separation methods using ICA and minus approaches. The data format is sample number*feature dimension (885*36).
The PERCLOS label files contain continuous vigilance labels (ranging from 0 to 1) calculated from eye tracking data.
COVID-19 Lung CT Scans
Full name: Mehrad Aria

This is a large public COVID-19 (SARS-CoV-2) lung CT scan dataset, containing total of 8,439 CT scans which consists of 7,495 positive cases (COVID-19 infection) and 944 negative ones (normal and non-COVID-19). Data is available as 512×512px PNG images and have been collected from real patients in radiology centers of teaching hospitals of Tehran, Iran. The aim of this dataset is to encourage the research and development of effective and innovative methods such as deep CNNs which are able to identify if a person is infected by COVID-19 through the analysis of his/her CT scans. As a baseline for this dataset we used a CNN-based approach inspired by transfer learning which we could achieve an accuracy of 99.61% which is very promising.

Introducing paper: Ctcovid19: Automatic Covid-19 Model for Computed Tomography Scans Using Deep Learning
SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification

Doi: 10.1101/2020.04.24.20078584

Introducing paper: Ctcovid19: Automatic Covid-19 Model for Computed Tomography Scans Using Deep Learning
Extensive COVID-19 X-Ray and CT Chest Images Dataset

This COVID-19 dataset consists of Non-COVID and COVID cases of both X-ray and CT images. The associated dataset is augmented with different augmentation techniques to generate about 17099 X-ray and CT images. The dataset contains two main folders, one for the X-ray images, which includes two separate sub-folders of 5500 Non-COVID images and 4044 COVID images. The other folder contains the CT images. It includes two separate sub-folders of 2628 Non-COVID images and 5427 COVID images.
Large Time Lags Location (LTLL)

We introduce here our Large Time Lags Location (LTLL) dataset containing pictures of 25 locations captured over a range of more than 150 years. Specifically we collected images from several cities and towns in Europe such as Paris, London, Merelbeke, Leuven and ancient cities from Asia such as Agra in India, Colombo and Kandy from Sri Lanka. We chose thirteen locations considering the presence of well known landmarks for which it has been easy to download old and new pictures from the Web. The rest of the twelve locations are located in the municipality of Merelbeke, Flemish Province of East Flanders in Belgium. Ancient images of historical locations dating back to the period 1850s-1950s have been provided by the museum in Merelbeke. We downloaded all the corresponding modern images from Flickr, Google Street-View and the Google-Images search engine. In total the dataset contains 225 historical pictures and 275 modern ones.

Introducing paper: Location Recognition Over Large Time Lags
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Full name: Juliana Duque filtrado: ¿Dónde ver el video viral completo

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FG-OVD
Full name: Fine-Grained Open-Vocabulary object Detection benchmarks

### Benchmark Suite Description for PapersWithCode

**Fine-Grained Open-Vocabulary Detection (FG-OVD) Benchmark Suite**  
The FG-OVD benchmark suite evaluates the ability of open-vocabulary object detectors to discern fine-grained object properties such as color, material, pattern, and transparency. This suite introduces dynamic vocabularies for each object, consisting of one positive caption and several challenging negative captions, crafted using attribute substitution at varying difficulty levels. 

Key features include:  
- **Difficulty-Based Benchmarks**: Trivial, Easy, Medium, and Hard benchmarks challenge detectors with progressively harder negative examples.  
- **Attribute-Based Benchmarks**: Focused evaluation of specific attributes, such as color or material, with negative captions differing only in the targeted attribute.  
- **Metrics**: Mean Average Precision (mAP) and Median Rank are used to measure both localization accuracy and fine-grained caption assignment performance.

The suite provides a comprehensive analysis of state-of-the-art models, highlighting their strengths and limitations in fine-grained object recognition.

Introducing paper: The devil is in the fine-grained details: Evaluating open-vocabulary object detectors for fine-grained understanding
ULP Dataset

Hundreds of clean and poisoned models per dataset for Tiny-ImageNet, CIFAR10

Introducing paper: Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs