Omkar2118/datasets huggingface dataset:
COCO-O

COCO-O(ut-of-distribution) contains 6 domains (sketch, cartoon, painting, weather, handmake, tattoo) of COCO objects which are hard to be detected by most existing detectors. The dataset has a total of 6,782 images and 26,624 labelled bounding boxes.
grobid-quantities-holdout
Full name: grobid-quantities holdout dataset

The dataset is described here: 

https://grobid-quantities.readthedocs.io/en/latest/guidelines.html
SOEval

SOEVAL is created by us by mining questions from StackOverflow. Our goal was to create a prompt dataset that reflects the real-life needs of software developers. To build this dataset, we first collected 500 popular and recent questions with Python and Java tags for each. From these 1,000 questions, we applied a set of inclusion and exclusion criteria. The inclusion criteria were: the question has to (1) explicitly ask “how to do X” in Python or Java; (2) include code in its body; (3) have an accepted answer that includes code. We excluded questions that were (1) open-ended and asking for best practices/guidelines for a specific problem in Python/Java; (2) related to finding a specific API/module for a given task; (3) related to errors due to environment configuration (e.g., missing dependency library); (4) related to configuring libraries/API; (5) syntax specific types of questions. By applying the criteria above to these 1K questions, we obtained 28 and 42 prompts for Java and Python, respectively.

Introducing paper: FRANC: A Lightweight Framework for High-Quality Code Generation
Inria building dataset

**Inria building dataset** contains 360 images (5120×5120) collected from 5 cities (Austin, Chicago, Kitsap, Tyrol, and Vienna)
SK-VG

**SK-VG** is a dataset for Scene Knowledge-guided Visual Grounding, where the image content and referring expressions are not sufficient to ground the target objects, forcing the models to have a reasoning ability on the long-form scene knowledge. To perform this task, SK-VG is the first dataset of the fourth type, where for each image, we provide human-written knowledge to describe its content.

Introducing paper: Advancing Visual Grounding with Scene Knowledge: Benchmark and Method