Omkar2118/datasets huggingface dataset:
Classic ECN AQM Fall-Back

Clickable heat-map visualizations of the experiments run to quantify the Classic ECN AQM problem and to evaluate the success of the Classic AQM Detection and Fall-back algorithm.

Clicking through gives access to whisker-plot summary results, more detailed clickable heat-maps and time-series plots of all the variables in each experiment run.

Introducing paper: TCP Prague Fall-back on Detection of a Classic ECN AQM
iMet Collection

A dataset for fine-grained art attribute recognition introduced in the 6th FGVC Workshop at CVPR 2019. It is a high-quality artwork image dataset with professional photographs of artworks from The Metropolitan Museum of Art and attribute labels curated or verified by experts.
FED
Full name: Fine-grained Evaluation of Dialog

The FED dataset is constructed by annotating a set of human-system and human-human conversations with eighteen fine-grained dialog qualities.

Introducing paper: Unsupervised Evaluation of Interactive Dialog with DialoGPT
BugClassify

Dataset of 5,591 labeled issue tickets. Originally created by Herzig et al. in : "It’s Not a Bug, It’s a Feature: How Misclassification Impacts Bug Prediction" ([paper](https://www.microsoft.com/en-us/research/wp-content/uploads/2013/05/icse2013-bugclassify.pdf))
EQA
Full name: Embodied Question Answering

The **EQA** (Embodied Question Answering) dataset is a dataset of visual questions and answers grounded in House3D. For this dataset an agent is spawned at a random location in a 3D environment and asked a question (for e.g. "What color is the car?"). In order to answer, the agent must first intelligently navigate to explore the environment, gather necessary visual information through first-person vision, and then answer the question ("orange").

Introducing paper: Embodied Question Answering