Omkar2118/datasets huggingface dataset:
PACE 2016 Feedback Vertex Set
Full name: PACE 2016 Track B, Feedback Vertex Set

This is the dataset used in the [PACE 2016 challenge](https://pacechallenge.org/2016/), Track B, which was computing minimal Feedback Vertex Set. This competition focused on exact solutions, i.e. provably minimal feedback vertex sets (and no heuristic solutions). This should not be confused with the PACE 2022 challenge, which focused on _directed_ feedback vertex set, and has its own entries on PapersWithCode ([exact](https://paperswithcode.com/dataset/pace-2022-exact) and [heuristic](https://paperswithcode.com/dataset/pace-2022-heuristic)).

The dataset can be downloaded [here](https://github.com/ckomus/PACE-fvs), and includes 100 instances that were released for practice (the `public/` folder) and 100 instances that were kept private (`hidden/`) until the competition evaluation. All 200 were used in the final evaluation. Each instance is an undirected graph, one edge per line, in the format `a b` indicating an edge between vertices `a` and `b`. Vertices are 1-indexed.

Final results of the competition were reported [in the PACE report](https://doi.org/10.4230/LIPIcs.IPEC.2016.30), and additional analysis of some top solutions was done [independently](https://arxiv.org/abs/1803.00925) by Kiljan and Pilipczuk.
Lot-insts
Full name: Long-Tailed instituition names

LoT-insts contains over 25k classes whose frequencies are naturally long-tail distributed. Its test set from four different subsets: many-, medium-, and few-shot sets, as well as a zero-shot open set. To our best knowledge, this is the first natural language dataset that focuses on this long-tailed and open classification problem.

Introducing paper: Text Classification in the Wild: a Large-scale Long-tailed Name Normalization Dataset
Weather2K

A multivariate spatio-temporal benchmark dataset for meteorological forecasting based on real-time observation data from ground weather stations.

Introducing paper: Weather2K: A Multivariate Spatio-Temporal Benchmark Dataset for Meteorological Forecasting Based on Real-Time Observation Data from Ground Weather Stations
ImageNet C-OOD (class-out-of-distribution)

This dataset was presented as part of the ICLR 2023 paper 𝘈 𝘧𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬 𝘧𝘰𝘳 𝘣𝘦𝘯𝘤𝘩𝘮𝘢𝘳𝘬𝘪𝘯𝘨 𝘊𝘭𝘢𝘴𝘴-𝘰𝘶𝘵-𝘰𝘧-𝘥𝘪𝘴𝘵𝘳𝘪𝘣𝘶𝘵𝘪𝘰𝘯 𝘥𝘦𝘵𝘦𝘤𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘪𝘵𝘴 𝘢𝘱𝘱𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘵𝘰 𝘐𝘮𝘢𝘨𝘦𝘕𝘦𝘵.

It is a framework that, based on this dataset (a subset of the ImageNet-21k dataset) is able to generate a C-OOD (AKA open-set recognition) benchmark that covers a variety of difficulty levels. these benchmarks are tailored to the evaluated model. This approach provides a more accurate representation of the model’s own performance.

The resulting difficulty levels of our framework allow benchmarking with respect to the difficulty levels most relevant to the task. For example, for a task with a high tolerance for
risk (e.g., a task for an entertainment application), the performance of a model on a median difficulty level might be more important than on the hardest difficulty level (severity 10). 
The opposite might be true for some applications with a low tolerance for risk (e.g., medical applications), for which one requires the best performance to be attained even if the OOD is very hard to detect (severity 10).
The paper in which the framework was introduced showed that detection algorithms do not always improve performance on all inputs equally, and could even hurt performance for specific difficulty levels and models. Choosing the combination of (model, detection algorithm) based only on the detection performance on all data may yield sub-optimal results for our specific desired level of difficulty.
Performance Improving Code Edits (PIE)
Full name: Performance Improving Code Edits

**PIE** stands for Performance Improving Code Edits. PIE contains trajectories of programs, where a programmer begins with an initial, slower version and iteratively
makes changes to improve the program’s performance.

Source: [Learning Performance-Improving Code Edits](https://arxiv.org/pdf/2302.07867v3.pdf)

Image Source: [https://arxiv.org/pdf/2302.07867v3.pdf](https://arxiv.org/pdf/2302.07867v3.pdf)

Introducing paper: Learning Performance-Improving Code Edits
OPT
Full name: Object Pose Tracking

Accurately tracking the six degree-of-freedom pose of an object in real scenes is an important task in computer vision and augmented reality with numerous applications. Although a variety of algorithms for this task have been proposed, it remains difficult to evaluate existing methods in the literature as oftentimes different sequences are used and no large benchmark datasets close to real-world scenarios are available. In this paper, we present a large object pose tracking benchmark dataset consisting of RGB-D video sequences of 2D and 3D targets with ground-truth information. The videos are recorded under various lighting conditions, different motion patterns and speeds with the help of a programmable robotic arm. We present extensive quantitative evaluation results of the state-of-the-art methods on this benchmark dataset and discuss the potential research directions in this field.
VEDAI
Full name: Vehicle Detection in Aerial Imagery

VEDAI is a dataset for Vehicle Detection in Aerial Imagery, provided as a tool to benchmark automatic target recognition algorithms in unconstrained environments. The vehicles contained in the database, in addition of being small, exhibit different variabilities such as multiple orientations, lighting/shadowing changes, specularities or occlusions. Furthermore, each image is available in several spectral bands and resolutions. A precise experimental protocol is also given, ensuring that the experimental results obtained by different people can be properly reproduced and compared. We also give the performance of some baseline algorithms on this dataset, for different settings of these algorithms, to illustrate the difficulties of the task and provide baseline comparisons.