Omkar2118/datasets huggingface dataset:
SG-NLG
Full name: Schema-Guided Natural Language Generation

The SG-NLG dataset is a pre-processed version of the [DSTC8 Schema-Guided Dialogue SGD dataset](https://paperswithcode.com/dataset/sgd), designed specifically for data-to-text Natural Language Generation (NLG). The original DSTC8 SGD contains ~20,000 dialogues spanning across ~20 domains.

This SG-NLG dataset is designed to make it easier to conduct NLG experiments on the SGD data. It consists of pre-processed SGD data by pairing the schema for each system turn with the corresponding set of natural language strings that realize it. It also “delexicalizes” the prompts (replace related values with fixed names) to convert them into templates that make them more generic for use within a dialog system.

The final SG-NLG dataset is composed of nearly 4K MRs and over 140K templates. 

Source: [The Schema-Guided Natural Language Generation (SG-NLG) Dataset](https://github.com/alexa/schema-guided-nlg)

Introducing paper: Schema-Guided Natural Language Generation
ShapenetRender

**ShapenetRender**er is an extension of the ShapeNet Core dataset which has more variation in camera angles. For each mesh model, the dataset provides 36 views with smaller variation and 36 views with larger variation. The resolution of the newly rendered images is 224x224 in contrast to the 137x137 original resolution. Additionally, each RGB image is paired with a depth image, a normal map and an albedo image.

Source: [https://github.com/Xharlie/ShapenetRender_more_variation](https://github.com/Xharlie/ShapenetRender_more_variation)
Image Source: [https://github.com/Xharlie/ShapenetRender_more_variation](https://github.com/Xharlie/ShapenetRender_more_variation)

Introducing paper: DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
ShapeNet-Skeleton

The **ShapeNet-Skeleton** dataset has ground-truth skeleton point sets and skeletal volumes for object instances in the ShapeNet dataset.

Source: [https://arxiv.org/pdf/2008.05742.pdf](https://arxiv.org/pdf/2008.05742.pdf)

Introducing paper: SkeletonNet: A Topology-Preserving Solution for Learning Mesh Reconstruction of Object Surfaces from RGB Images
3D Shapes Dataset

3dshapes is a dataset of 3D shapes procedurally generated from 6 ground truth independent latent factors. These factors are floor colour, wall colour, object colour, scale, shape and orientation.

Source: [3D Shapes Dataset ](https://github.com/deepmind/3d-shapes)

Introducing paper: Disentangling by Factorising
ShEMO
Full name: Sharif Emotional Speech Database

The database includes 3000 semi-natural utterances, equivalent to 3 hours and 25 minutes of speech data extracted from online radio plays. The ShEMO covers speech samples of 87 native-Persian speakers for five basic emotions including anger, fear, happiness, sadness and surprise, as well as neutral state.

Source: [ShEMO -- A Large-Scale Validated Database for Persian Speech Emotion Detection](/paper/shemo-a-large-scale-validated-database-for)

Introducing paper: ShEMO -- A Large-Scale Validated Database for Persian Speech Emotion Detection
SHIDC-BC-Ki-67

Benchmark for BC Ki-67 stained cell detection and further annotated classification of cells.

Source: [PathoNet: Deep learning assisted evaluation of Ki-67 and tumor infiltrating lymphocytes (TILs) as prognostic factors in breast cancer; A large dataset and baseline](/paper/pathonet-deep-learning-assisted-evaluation-of)

Introducing paper: PathoNet: Deep learning assisted evaluation of Ki-67 and tumor infiltrating lymphocytes (TILs) as prognostic factors in breast cancer; A large dataset and baseline
ShopSign

A newly developed natural scene text dataset of Chinese shop signs in street views. 

Source: [ShopSign: a Diverse Scene Text Dataset of Chinese Shop Signs in Street Views](/paper/shopsign-a-diverse-scene-text-dataset-of)

Introducing paper: ShopSign: a Diverse Scene Text Dataset of Chinese Shop Signs in Street Views