Omkar2118/datasets huggingface dataset:
NeuralRGBD

RGB-D dataset of synthetic indoor scenes with color, noisy depth map, etc.

Introducing paper: Neural RGB-D Surface Reconstruction
WebCPM

**WebCPM** is a Chinese LFQA dataset. It contains 5,500 high-quality question-answer pairs, together with 14,315 supporting facts and 121,330 web search actions.

Source: [WebCPM: Interactive Web Search for Chinese Long-form Question Answering](https://arxiv.org/pdf/2305.06849v1.pdf)

Image Source: [WebCPM: Interactive Web Search for Chinese Long-form Question Answering](https://arxiv.org/pdf/2305.06849v1.pdf)

Introducing paper: WebCPM: Interactive Web Search for Chinese Long-form Question Answering
MVSep

**MVSep** is a synthetic dataset for the vocal separation task created by combining random vocal and instrumental samples, publicly available on the internet. The sourced samples were separated into two sets (vocal-only and instrumental-only) and then randomly mixed together. The mixtures may not always sound like a real melody, but they allow for testing audio separation methods. Synth MVSep dataset consists of 100 tracks, each with a duration of exactly one minute and a sample rate of 44.1 kHz.

Source: [Benchmarks and leaderboards for sound demixing tasks](https://arxiv.org/pdf/2305.07489v1.pdf)

Introducing paper: Benchmarks and leaderboards for sound demixing tasks
Meta Omnium

**Meta Omnium** is a dataset-of-datasets spanning multiple vision tasks including recognition, keypoint localization, semantic segmentation and regression. Meta Omnium enables meta-learning researchers to evaluate model generalization to a much wider array of tasks than previously possible, and provides a single framework for evaluating meta-learners across a wide suite of vision applications in a consistent manner.

Source: [Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn](https://arxiv.org/pdf/2305.07625v1.pdf)

Image Source: [Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn](https://arxiv.org/pdf/2305.07625v1.pdf)

Introducing paper: Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn
CREMP

**CREMP** is a resource generated for the rapid development and evaluation of machine learning models for macrocyclic peptides. CREMP contains 36,198 unique macrocyclical peptides and their high-quality structural ensembles generated using the Conformer-Rotamer Ensemble Sampling Tool (CREST).

Source: [CREMP: Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning](https://arxiv.org/pdf/2305.08057v1.pdf)

Image Source: [CREMP: Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning](https://arxiv.org/pdf/2305.08057v1.pdf)

Introducing paper: CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning