Omkar2118/datasets huggingface dataset:
CODA

A novel road corner case dataset for object detection in autonomous driving which contains ~10000 carefully selected road driving scenes with high-quality bounding box annotation for 43 representative road object categories.
ASVspoof 2021
Full name: ASVspoof 2021 Dataset

Benchmarking initiatives support the meaningful comparison of competing solutions to prominent problems in speech and language processing. Successive benchmarking evaluations typically reflect a progressive evolution from ideal lab conditions towards to those encountered in the wild. ASVspoof, the spoofing and deepfake detection initiative and challenge series, has followed the same trend. This article provides a summary of the ASVspoof 2021 challenge and the results of 54 participating teams that submitted to the evaluation phase. For the logical access (LA) task, results indicate that countermeasures are robust to newly introduced encoding and transmission effects. Results for the physical access (PA) task indicate the potential to detect replay attacks in real, as opposed to simulated physical spaces, but a lack of robustness to variations between simulated and real acoustic environments. The Deepfake (DF) task, new to the 2021 edition, targets solutions to the detection of manipulated, compressed speech data posted online. While detection solutions offer some resilience to compression effects, they lack generalization across different source datasets. In addition to a summary of the top-performing systems for each task, new analyses of influential data factors and results for hidden data subsets, the article includes a review of post-challenge results, an outline of the principal challenge limitations and a road-map for the future of ASVspoof.
Calandra Dataset

The Calandra dataset provides the data from a pair of tactile sensors attached to a jaw gripper (left and right) alongside the RGB images. A triplet of samples was captured ’before’, ’during’, and ’after’ grasping a plethora of objects. The objective is to determine the success or the failure of the grasp attempt.

Introducing paper: More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch
Touch and Go

This dataset encompasses a diverse range of tactile features that are instrumental in bifurcating various material properties. Three downstream tasks are considered: 1) categorization of materials, 2) distinction between hard and soft surfaces, and 3) distinction between smooth and textured surfaces.

Introducing paper: Touch and Go: Learning from Human-Collected Vision and Touch
EUROPA

### Dataset Description

EUROPA is a dataset designed for training and evaluating multilingual keyphrase generation models in the legal domain. It consists of legal judgments from the Court of Justice of the European Union (EU) and includes instances in all 24 official EU languages.

**Key Features**:
**Multilingual:** Covers 24 official EU languages.
**Domain-Specific:** Focuses on legal documents.
**Source:** Derived from Court of Justice of the European Union judgments.

- **Curated by:** N3 team
- **Languages:** French, German, English, Italian, Dutch, Greek, Danish, Portuguese, Spanish, Swedish, Finnish, Lithuanian, Estonian, Czech, Hungarian, Latvian, Slovenian, Polish, Maltese, Slovak, Romanian, Bulgarian, Croatian, Irish
- **License:** MIT License

### Dataset Sources

- **Paper:** https://arxiv.org/abs/2403.00252

## Dataset Structure

- **celex_id:** CELEX identifier inherited from CJEU. Different translated versions of the same judgment share the same celex_id. If you wish to set a unique identifier for each instance, you can concatenate `lang` and `celex_id` values;
- **lang:** ISO 639-1 language code;
- **input:** judgment transcription or translation;
- **keyphrases:** reference keyphrases drafted by the CJEU.

As explained in our paper, the dataset is split chronologically for assessing temporal generalization of models:
- **training set**: 1957 to 2010 (131 076 instances);
- **validation set**: 2011 to 2015 (63 373 instances);
- **test set**: 2016 to 2023 (90 508 instances).

## Citation

```
@article{salaun2024europa,
  title={EUROPA: A Legal Multilingual Keyphrase Generation Dataset},
  author={Sala{\"u}n, Olivier and Piedboeuf, Fr{\'e}d{\'e}ric and Le Berre, Guillaume and Hermelo, David Alfonso and Langlais, Philippe},
  journal={arXiv preprint arXiv:2403.00252},
  year={2024}
}
```

Introducing paper: EUROPA: A Legal Multilingual Keyphrase Generation Dataset
TAL-SCQ5K

TAL-SCQ5K-EN/TAL-SCQ5K-CN are high-quality mathematical competition datasets in English and Chinese language created by TAL Education Group, each consisting of 5K questions(3K training and 2K testing). The questions are in the form of multiple-choice and cover mathematical topics at the primary, junior high, and high school levels. In addition, detailed solution steps are provided to facilitate CoT training and all the mathematical expressions in the questions have been presented as standard text-mode Latex.
MathEval

MathEval is a benchmark dedicated to a comprehensive evaluation of the mathematical capabilities of large models. It encompasses over 20 evaluation datasets across various mathematical domains, with over 30,000 math problems. The goal is to thoroughly evaluate the performance of large models in tackling problems spanning a wide range of difficulty levels and diverse mathematical subfields (i.e. arithmetic, elementary mathematics, middle and high school competition topics, advanced mathematical, etc.). It serves as a trustworthy reference for cross-model comparisons of mathematical abilities among large models at the current stage and guides how to further enhance the mathematical capabilities of these models in the future.
Ego4D-HCap

Ego4D-HCap is a hierarchical video captioning dataset comprised of a three-tier hierarchy of captions: short clip-level captions, medium-length video segment descriptions, and long-range video-level summaries. To construct Ego4D-HCap, we leverage Ego4D, the largest publicly available egocentric video dataset. While Ego4D comes with time-stamped atomic captions and video-segment descriptions spanning up to 5 minutes, it lacks video-level summaries for longer video durations. To address this issue, we annotate a subset of 8,267 Ego4D videos with long-range video summaries, each spanning up to two hours. This enhancement provides a three-level hierarchy of captions.

Introducing paper: Video ReCap: Recursive Captioning of Hour-Long Videos
ComTQA

The **ComTQA dataset** is a visual table question answering benchmark. It includes images collected from **FinTabNet** and **PubTables-1M**, comprising a total of **9,070 QA pairs** with **1,591 images**. The dataset is designed to address tasks related to table question answering and is available in English. It falls under the size category of **1K<n<10K** and is licensed under **cc-by-nc-4.0**¹.

This dataset is particularly useful for developing and testing algorithms that can interpret and answer questions based on tabular data. It's a valuable resource for researchers and practitioners in the field of machine learning and natural language processing, especially those focusing on the intersection of visual data interpretation and question answering systems¹.

(1) ByteDance/ComTQA · Datasets at Hugging Face. https://huggingface.co/datasets/ByteDance/ComTQA.
(2) ComQA Dataset | Papers With Code. https://paperswithcode.com/dataset/comqa.
(3) COMETA: the corpus of online medical entities - GitHub. https://github.com/cambridgeltl/cometa.
(4) COMETA Dataset | Papers With Code. https://paperswithcode.com/dataset/cometa.

Introducing paper: TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy
Deep Blending

The Deep Blending Dataset comprises 19 diverse scenes, offering comprehensive resources for free-viewpoint image-based rendering (IBR). Each scene includes input images, COLMAP reconstructions (SfM & MVS), global textured meshes from RealityCapture, refined depth maps, per-view meshes, and camera poses. This dataset is ideal for training and evaluating novel view synthesis and blending algorithms in both indoor and outdoor environments.
MMT-Bench

MMT-Bench is a comprehensive benchmark designed to evaluate Large Vision-Language Models (LVLMs) across a wide array of multimodal tasks that require expert knowledge as well as deliberate visual recognition, localization, reasoning, and planning¹. It includes **31,325 meticulously curated multi-choice visual questions** from various scenarios such as vehicle driving and embodied navigation, covering **32 core meta-tasks** and **162 subtasks** in multimodal understanding¹.

The benchmark aims to be comprehensive enough to assess the multitask performance of LVLMs using a task map, which helps in identifying both in-domain and out-of-domain tasks. This extensive coverage allows for a thorough evaluation of the models' capabilities in multimodal understanding¹.

(1) MMT-Bench. https://mmt-bench.github.io/.
(2) OpenGVLab/MMT-Bench: ICML'2024 - GitHub. https://github.com/OpenGVLab/MMT-Bench.
(3) OpenGVLab/MMT-Bench - Giters. https://giters.com/OpenGVLab/MMT-Bench.

Introducing paper: MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI
Duke Lung Nodule Dataset 2024
Full name: Duke Lung Nodule Dataset 2024

Background: Lung cancer risk classification is an increasingly important area of research as low-dose thoracic CT screening programs have become standard of care for patients at high risk for lung cancer. There is limited availability of large, annotated public databases for the training and testing of algorithms for lung nodule classification.

Methods: Screening chest CT scans done between January 1, 2015 and June 30, 2021 at Duke University Health System were considered for this study. Efficient nodule annotation was performed semi-automatically by using a publicly available deep learning nodule detection algorithm trained on the LUNA16 dataset to identify initial candidates, which were then accepted based on nodule location in the radiology text report or manually annotated by a medical student and a fellowship-trained cardiothoracic radiologist.

Results: The dataset contains 1613 CT volumes with 2487 annotated nodules. Radiologist spot-checking confirmed the semi-automated annotation had an accuracy rate of >90%.

Conclusions: The Duke Lung Nodule Dataset is the first large dataset for CT screening for lung cancer reflecting the use of current CT technology. This represents a useful resource of lung cancer risk classification research, and the efficient annotation methods described for its creation may be used to generate similar databases for research in the future.

Introducing paper: AI in Lung Health: Benchmarking Detection and Diagnostic Models Across Multiple CT Scan Datasets
SafeBench

SafeBench is a benchmarking platform designed for the safety evaluation of autonomous vehicles (AVs) in safety-critical scenarios¹. It aims to provide a unified platform that integrates various types of safety-critical testing scenarios, scenario generation algorithms, and other variations such as driving routes and environments¹. The platform implements four deep reinforcement learning-based AV algorithms with four types of input to perform fair comparisons on SafeBench¹.

The creators of SafeBench have observed that machine intelligence-enabled systems are vulnerable to test cases resulting from either adversarial manipulation or natural distribution shifts, especially in safety-critical domains like autonomous driving¹. Traditional AD testing requires extensive driving miles due to the high dimensionality and rarity of safety-critical scenarios in the real world. SafeBench addresses this challenge by providing a large-scale and effective testing environment that encourages the development of new testing scenario generation and safe AD algorithms¹.

(1) SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous .... https://arxiv.org/abs/2206.09682.
(2) GitHub - trust-ai/SafeBench: A Benchmark for Evaluating Autonomous .... https://github.com/trust-ai/SafeBench.
(3) SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous .... https://arxiv.org/pdf/2206.09682v1.
(4) undefined. https://doi.org/10.48550/arXiv.2206.09682.
(5) undefined. https://bing.com/search?q=.

Introducing paper: SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles