Omkar2118/datasets huggingface dataset:
RealDOF
Full name: Single Image Defocus deblurring

This dataset consists of 50 high resolution image pairs captured by dual-camera setup for single image defocus deblurring . Please note this is not a training set but  a benchmark assessment.

Introducing paper: Iterative Filter Adaptive Network for Single Image Defocus Deblurring
ConsInv Dataset

ConsInv is a stereo RGB + IMU dataset designed for Dynamic SLAM testing and contains two subsets:

- **ConsInv-Indoors** contains sequences in an office setting where small objects are moved.
- **ConsInv-Outdoors** contains sequences in an urban environment, where cars and/or people move.

The novelty of ConsInv dataset is 1) the controlled degree of difficulty, from easy to very hard, and 2) the fact that the difficulty of the sequences comes only from object motion: relative motion between camera and object, motion ambiguity, challenging points of view when objects move. The difficulty does not come from motion speed, lack of features, lens flare, etc. - typically seen in other SLAM datasets.

Introducing paper: Self-Improving SLAM in Dynamic Environments: Learning When to Mask
DPD (Dual-view)
Full name: Image Defocus Deblurring

DPD dataset has two versions - single view and dual-view. This branch is for dual view benchmark evaluation.

Introducing paper: Defocus Deblurring Using Dual-Pixel Data
MarKG
Full name: Multimodal analogical reasoning Knowledge Graph

The MarKG dataset has 11,292 entities, 192 relations and 76,424 images, including 2,063 analogy entities and 27 analogy relations. The original intention of MarKG is to provide prior knowledge of analogy entities and relations for better multimodal analogical reasoning.

Introducing paper: Multimodal Analogical Reasoning over Knowledge Graphs
MARS (Multimodal Analogical Reasoning dataSet)

Analogical reasoning is fundamental to human cognition and holds an important place in various fields. However, previous studies mainly focus on single-modal analogical reasoning and ignore taking advantage of structure knowledge. We introduce the new task of multimodal analogical reasoning over knowledge graphs, which requires multimodal reasoning ability with the help of background knowledge. Our dataset MARS contains 10,685 training, 1,228 validation and 1,415 test instances.

Introducing paper: Multimodal Analogical Reasoning over Knowledge Graphs
BabyLM

**BabyLM** is a dataset for small scale language modeling, human language acquisition, low-resource NLP, and cognitive modeling. In partnership with CoNLL and CMCL, it provides a platform for approaches to pretraining with a limited-size corpus sourced from data inspired by the input to children. The task has three tracks, two of which restrict the training data to pre-released datasets of 10M and 100M words and are dedicated to explorations of approaches such as architectural variations, self-supervised objectives, or curriculum learning. The final track only restricts the amount of text used, allowing innovation in the choice of the data, its domain, and even its modality (i.e., data from sources other than text is welcome).

Source: [Call for Papers - The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus](https://arxiv.org/pdf/2301.11796v1.pdf)

Introducing paper: Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus