Omkar2118/datasets huggingface dataset:
Classical conditioning

The paper introduces three benchmarking tasks inspired by animal learning.

Introducing paper: From Eye-blinks to State Construction: Diagnostic Benchmarks for Online Representation Learning
AQL-22
Full name: Archive Query Log

The Archive Query Log (AQL) is a previously unused, comprehensive query log collected at the Internet Archive over the last 25 years. Its first version includes 356 million queries, 166 million search result pages, and 1.7 billion search results across 550 search providers. Although many query logs have been studied in the literature, the search providers that own them generally do not publish their logs to protect user privacy and vital business data. The AQL is the first publicly available query log that combines size, scope, and diversity, enabling research on new retrieval models and search engine analyses. Provided in a privacy-preserving manner, it promotes open research as well as more transparency and accountability in the search industry.

Introducing paper: The Archive Query Log: Mining Millions of Search Result Pages of Hundreds of Search Engines from 25 Years of Web Archives
Extended Agriculture-Vision

Extended Agriculture-Vision dataset comprises two parts:

1. An improved version of the Agriculture-Vision dataset, including full-field farmland imagery, encourages the exploration of geo-information on a larger scale.

2. Over three terabytes of high-resolution raw images across the US, aiming to inspire research in self-supervised learning in remote sensing and agriculture.

Extended Agriculture-Vision identified 1200 fields from 2019-2020 growing seasons. Each image consists of RGB and Near-infrared (NIR) channels with resolutions as high as 10 cm per pixel.

Introducing paper: Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis
AeBAD
Full name: Aero-engine Blade Anomaly Detection Dataset

Unlike previous datasets that focus on detecting the diversity of defect categories (like MVTec AD and VisA), AeBAD is centered on the diversity of domains within the same data category.

The aim of AeBAD is to automatically detect abnormalities in the blades of aero-engines, ensuring their stable operation. AeBAD consists of two sub-datasets: the single-blade dataset (AeBAD-S) and the video anomaly detection of blades (AeBAD-V). AeBAD-S comprises images of single blades of different scales, with a primary feature being that the samples are not aligned. Furthermore, there is a domain shift between the distribution of normal samples in the test set and the training set, where the domain shifts are mainly caused by the changes in illumination and view. AeBAD-V, on the other hand, includes videos of blades assembled on the blisks of aero-engines, with the aim of detecting blade anomalies during blisk rotation. A distinctive feature of AeBAD-V is that the shooting view in the test set differs from that in the training set.

Introducing paper: Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction
Snow100K

The **Snow100K** dataset consists of 

1) 100k synthesized snowy images

2) corresponding snow-free ground truth images

3) snow masks

4) 1,329 realistic snowy images

The images of 2) and 4) were downloaded via the Flickr api, and were manually divided into snow and snow-free categories, respectively. In addition,  each image are normalized to the size of 640 pixels and retained its original aspect ratio.

Introducing paper: DesnowNet: Context-Aware Deep Network for Snow Removal
SRRS
Full name: Snow Removal in Realistic Scenario

**SRRS (Snow Removal in Realistic Scenario)** contains 15000 synthesized snow images and 1000 snow images in real scenarios downloaded from the Internet.

Source: [JSTASR: Joint Size and Transparency-Aware Snow Removal Algorithm Based on Modified Partial Convolution and Veiling Effect Removal](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660749.pdf)

Introducing paper: JSTASR: Joint Size and Transparency-Aware Snow Removal Algorithm Based on Modified Partial Convolution and Veiling Effect Removal
PINet

We propose a new light field image database called “PINet” inheriting the hierarchical structure from WordNet. It consists of 7549 LIs captured by Lytro Illum, which is much larger than the existing databases. The images are manually annotated to 178 categories according to WordNet, such as cat, camel, bottle, fans, etc. The registered depth maps are also provided. Each image is generated by processing the raw LI from the camera by Light Field Toolbox v0.4 for demosaicing and devignetting.

PINet is the largest light field image dataset.
More details can be found in https://github.com/VincentChandelier/SADN
SCB-dataset
Full name: Student Classroom Behavior dataset

**Student Classroom Behavior dataset (SCB-dataset)** reflects real-life scenarios. The dataset includes 11,248 labels and 4,003 images, with a focus on handraising behavior.

Source: [SCB-dataset: A Dataset for Detecting Student Classroom Behavior](/paper/scb-dataset-a-dataset-for-detecting-student)

Image Source: [SCB-dataset: A Dataset for Detecting Student Classroom Behavior](/paper/scb-dataset-a-dataset-for-detecting-student)

Introducing paper: SCB-dataset: A Dataset for Detecting Student Classroom Behavior
MoocRadar

**MoocRadar** is a fine-grained and multiaspect knowledge repository that consists of 2,513 exercises, 5,600 concepts, and 14,224 students’ 12,715,126 behavioral records for improving cognitive student modeling in MOOCs.

Source: [MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs](https://arxiv.org/pdf/2304.02205v1.pdf)

Image Source: [MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs](https://arxiv.org/pdf/2304.02205v1.pdf)

Introducing paper: MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs
SA-1B

**SA-1B** consists of 11M diverse, high resolution, licensed, and privacy protecting images and 1.1B high-quality segmentation masks.

Source: [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf)

Image Source: [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf)

Introducing paper: Segment Anything
Large COVID-19 CT scan slice dataset

"We built a large lung CT scan dataset for COVID-19 by curating data from 7 public datasets listed in the acknowledgements. These datasets have been publicly used in COVID-19 diagnosis literature and proven their efficiency in deep learning applications. Therefore, the merged dataset is expected to improve the generalization ability of deep learning methods by learning from all these resources together.

These datasets are made available in different formats. Our goal is to provide a large dataset of COVID-19, Normal, and CAP CT slices together with their corresponding metadata. Some of the datasets consist of categorized CT slices, and some include CT volumes with annotated lesion slices. Therefore, we used the slice-level annotations to extract axial slices from CT volumes. We then converted all the images to 8-bit to have a consistent depth.

To ensure the dataset quality, we have removed the closed lung normal slices that do not carry information about inside lung manifestations. Additionally, we did not include images lacking clear class labels or patient information. In total, we have gathered 7,593 COVID-19 images from 466 patients, 6,893 normal images from 604 patients, and 2,618 CAP images from 60 patients. All of our CAP images are from Afshar et al. dataset, in which 25 cases are already annotated. Our radiologist has annotated the remaining 35 CT scan volumes. This is the largest COVID-19 lung CT dataset so far, to the best of our knowledge." - Source: [A Robust Ensemble-Deep Learning Model for COVID-19 Diagnosis based on an Integrated CT Scan Images Database](https://www.researchgate.net/publication/352296409_A_Robust_Ensemble-Deep_Learning_Model_for_COVID-19_Diagnosis_based_on_an_Integrated_CT_Scan_Images_Database)

Acknowledgements

- J. Zhao, Y. Zhang, X. He, and P. Xie, "COVID-CT-Dataset: a CT scan dataset about COVID-19," arXiv preprint arXiv:2003.13865, 2020.
  
- P. Afshar et al., "COVID-CT-MD: COVID-19 Computed Tomography (CT) Scan Dataset Applicable in Machine Learning and Deep Learning," arXiv preprint arXiv:2009.14623, 2020.
  
- J. P. Cohen, P. Morrison, L. Dao, K. Roth, T. Q. Duong, and M. Ghassemi, "Covid-19 image data collection: Prospective predictions are the future," arXiv preprint arXiv:2006.11988, 2020.
  
- S. Morozov et al., "MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset," arXiv preprint arXiv:2005.06465, 2020.
  
- M. Rahimzadeh, A. Attar, and S. M. Sakhaei, "A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset," medRxiv, 2020.
  
- M. Jun et al., "COVID-19 CT Lung and Infection Segmentation Dataset," Zenodo, Apr, vol. 20, 2020.
  
- "COVID-19." 2020. [Online] http://medicalsegmentation.com/covid19/ [Accessed 23 December, 2020].
PKU (License Plate Detection)

The PKU dataset has almost 4,000 images categorized into five groups (G1-G5) that show different situations. For example, G1 has images of highways during the day with only one car in them. On the other hand, G5 has images of crosswalks during the day or at night with multiple cars and license plates (LPs).

It can be used to train and test LP detectors, as the authors labeled the position of each visible LP on each image.