Omkar2118/datasets huggingface dataset:
UT-Zappos50K

UT Zappos50K (UT-Zap50K) is a large shoe dataset consisting of 50,025 catalog images collected from Zappos.com. The images are divided into 4 major categories — shoes, sandals, slippers, and boots — followed by functional types and individual brands. The shoes are centered on a white background and pictured in the same orientation for convenient analysis.
This dataset is created in the context of an online shopping task, where users pay special attentions to fine-grained visual differences. For instance, it is more likely that a shopper is deciding between two pairs of similar men's running shoes instead of between a woman's high heel and a man's slipper. GIST and LAB color features are provided. In addition, each image has 8 associated meta-data (gender, materials, etc.) labels that are used to filter the shoes on Zappos.com.
We introduced this dataset in the context of a pairwise comparison task, where the goal is to predict which of two images more strongly exhibits a visual attribute. When given a novel image pair, we want to answer the question, “Does Image A contain more or less of an attribute than Image B?” Both training and evaluation are performed using pairwise labels.
However, the usefulness of this dataset extends beyond the comparison task that we’ve demonstrated. The meta-data labels and the large size of the dataset makes it suitable for other tasks as well, such as:

category/brand classification
fine-grained attribute learning with rationales
gender-specific style matching
zero-shot learning
C-GQA
Full name: Compositional GQA

We propose a split built on top of Stanford GQA dataset originally proposed for VQA and name it Compositional GQA (C-GQA) dataset (see supplementary for the details). CGQA contains over 9.5k compositional labels making it the most extensive dataset for CZSL. With cleaner labels and a larger label space, our hope is that this dataset will inspire further research on the topic.

Introducing paper: Learning Graph Embeddings for Compositional Zero-shot Learning
Wind Speed Forecasting

Air pollution management through wind speed forecasting: the time series exhibits a daily cyclical behavior and a long-term seasonality.

Introducing paper: Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach
Random Hierarchy Model

Artificial hierarchical datasets to study how neural networks learn hierarchical tasks. See papers for details.

Introducing paper: How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model
Small ImageNet 150

This new dataset represents a subset of the ImageNet1k. It consists of 99000 images and 150 classes. 90000 of them are for training, 600 images for each class. The validation test size is 7500. For testing, we add 1500 images from the ImageNetV2 Top-Images dataset to the validation.

Introducing paper: Interpretable Computer Vision Models through Adversarial Training: Unveiling the Robustness-Interpretability Connection
EBD

A large-scale benchmark with 1605 high-resolution, well-annotated images, featuring more
complex scenes and a wider range of DOF settings.

Introducing paper: Depth and DOF Cues Make A Better Defocus Blur Detector
SUDOER
Full name: System/User Dataset for Obedience Evaluation in Responses

The dataset aims to provide system prompts and user prompts for assistant. You should make random pairs and compute human preference for both system prompt obedience and user prompt relevance through A/B testing.

Introducing paper: Stay on topic with Classifier-Free Guidance