Omkar2118/datasets huggingface dataset:
Real Blur Dataset

The dataset consists of 4,738 pairs of images of 232 different scenes including reference pairs. All images were captured both in the camera raw and JPEG formats, hence generating two datasets: RealBlur-R from the raw images, and RealBlur-J from the JPEG images. Each training set consists of 3,758 image pairs, while each test set consists of 980 image pairs.

The deblurring result is first aligned to its ground truth sharp image using a homography estimated by the enhanced correlation coefficients method, and PSNR or SSIM is computed in sRGB color space.

Introducing paper: Real-World Blur Dataset for Learning and Benchmarking Deblurring Algorithms
IG-3.5B-17k
Full name: IG-3.5B-17k

**IG-3.5B-17k** is an internal Facebook AI Research dataset for training image classification models. It consists of hashtags for up to 3.5 billion public Instagram images.

Introducing paper: Exploring the Limits of Weakly Supervised Pretraining
IG-1B-Targeted
Full name: IG-1B-Targeted

**IG-1B-Targeted** is an internal Facebook AI Research dataset that consists of 940 million public images with 1.5K hashtags matching with 1000 ImageNet1K synsets.

Introducing paper: Billion-scale semi-supervised learning for image classification
DAGM2007
Full name: DAGM2007

This is a synthetic dataset for defect detection on textured surfaces. It was originally created for a competition at the 2007 symposium of the DAGM (Deutsche Arbeitsgemeinschaft für Mustererkennung e.V., the German chapter of the International Association for Pattern Recognition). The competition was hosted together with the GNSS (German Chapter of the European Neural Network Society).

After the competition, the dataset has been used as a test dataset in multiple projects and research papers. It is publicly available from the University of Heidelberg website (Heidelberg Collaboratory for Image Processing).

The data is artificially generated, but similar to real world problems. The first six out of ten datasets, denoted as development datasets, are supposed to be used for algorithm development. The remaining four datasets, which are referred to as competition datasets, can be used to evaluate the performance. Researchers should consider not using or analyzing the competition datasets before the development is completed as a code of honour.
DSTC 8 Track 2
Full name: Dialog System Technology Challenges 8 Track 2

Dialog System Technology Challenges 8 (DSTC) Track 2 builds on the success of DSTC 7 Track 1 (NOESIS: Noetic End-to-End Response Selection Challenge). It proposes an extension of the task, incorporating new elements that are vital for the creation of a deployed task-oriented dialogue system. Specifically, three new dimensions are added to the challenge:

- Conversations with more than 2 participants
- Predicting whether a dialogue has solved the problem yet,
- Handling multiple simultaneous conversations. Each of these adds an exciting new dimension and brings the task closer to the creation of systems able to handle the complexity of real-world conversation.
- This challenge is offered with two goal oriented dialog datasets, used in 4 subtasks. 

Source: [Dialog System Technology Challenges 8 (DSTC 8) - Track 2](https://github.com/dstc8-track2/NOESIS-II/)

Image source: [DSTC 8 Track 2](https://github.com/dstc8-track2/NOESIS-II/)
Ubuntu Chat Corpus

The **Ubuntu Chat Corpus** (**UCC**) is composed of archived chat logs from Ubuntu's Internet Relay Chat technical support channels. Ubuntu uses IRC as one of many modes of technical support -- it offers real-time problem solving. The authors have taken some of the archived messages (which are in the public domain), reorganized the file structure, removed some unnecessary system messages, and compressed them to make it easier to obtain.

Source: [Ubuntu Chat Corpus](https://daviduthus.org/UCC/)

Image Source: [Ubuntu Chat Corpus](https://daviduthus.org/UCC/)
Liu et al. Corpus
Full name: Liu et al. Corpus

The **Liu et al. Corpus** is a pretraining dataset for large language models. It consists of 160Gb of news, books, stories, and web text.

Introducing paper: Unified Language Model Pre-training for Natural Language Understanding and Generation