Omkar2118/datasets huggingface dataset:
CLOTH3D
Full name: CLOTH3D: Clothed 3D Humans

This work presents CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightness and fabric. Clothes are simulated on top of thousands of different pose sequences and body shapes, generating realistic cloth dynamics. We provide the dataset with a generative model for cloth generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on graph convolutions (GCVAE) to learn garment latent spaces. This allows for realistic generation of 3D garments on top of SMPL model for any pose and shape.
Fashion-MNIST-H

We provide multiple human annotations for each test image in Fashion-MNIST. This can be used as soft labels or probabilistic labels instead of the usual hard (single) labels.

Introducing paper: Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification
MGTAB
Full name: Multi-Relational Graph-Based Twitter Account Detection Benchmark

**MGTAB** is the first standardized graph-based benchmark for stance and bot detection. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. For more details, please refer to the MGTAB paper.

Source: [https://github.com/GraphDetec/MGTAB](https://github.com/GraphDetec/MGTAB)

Introducing paper: MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark