Omkar2118/datasets huggingface dataset:
BTS3.1
Full name: Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset

Large, multimodal biometric dataset: It contains still images and videos of over 1,000 people captured at various ranges (up to 1,000 meters) and elevations (up to 400 meters) using a diverse set of cameras (commercial, military-grade, specialized).

Focus on challenging scenarios: Designed to address limitations of existing datasets, it includes data with extreme poses, low resolutions, and atmospheric distortions to push the boundaries of biometric recognition algorithms.

Rich annotations and diversity: Each data point is accompanied by detailed metadata (sensor details, weather conditions) and manual annotations (bounding boxes, yaw/pitch angles) for face and whole-body regions. The dataset also ensures demographic diversity for robust algorithm development.
DroneSURF
Full name: DroneSURF: Benchmark Dataset for Drone-based Face Recognition

Drone Surveillance of Faces, is a large-scale drone dataset intended to facilitate research for face recognition using drones.

Data has been captured at two outdoor locations: (i) at the ground level, where subjects are asked to walk in a park-like environment, and (ii) at the terrace of a building. For each location and surveillance scenario, data is captured twice: (i) during the morning and (ii) during the evening, before sunset.

For each combination of location, surveillance use case, and time of capture, there exist 25 videos featuring 58 subjects. Each subject belongs to the age bracket of (18, 40) years, and each video contains subjects appearing in groups of 2-3. For a particular setting combination, one group appears in only one video. Since there exist eight combinations of location, surveillance scenario, and time of capture, each group of subjects occur in eight videos, thus resulting in a total of 200 videos. The subjects and pairings remain consistent across different settings.

The directory structure represents the combination of location, use case and time of capture.
Object HalBench

Object HalBench is a benchmark used to evaluate the performance of Language Models, particularly those that are multimodal (i.e., they can process and generate both text and images). It's designed to test how well these models can avoid "hallucinations" - generating text that is not factually grounded in the images they're processing¹.

For instance, the OmniLMM-12B model, which is a state-of-the-art open-source Language Model, has been reported to outperform GPT-4V on the Object HalBench¹. This model is aligned via a technique called multimodal RLHF (Reinforcement Learning from Human Feedback) for trustworthy behavior¹. This means it's designed to generate outputs that are more reliable and factually accurate, particularly when dealing with multimodal inputs¹. 

(1) openbmb/OmniLMM-12B · Hugging Face. https://huggingface.co/openbmb/OmniLMM-12B.
(2) OmniLMM：准确、高效的开源多模态大模型 - 知乎. https://zhuanlan.zhihu.com/p/681251797.
(3) RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from - arXiv.org. https://arxiv.org/html/2312.00849v2.
(4) README.md · openbmb/MiniCPM-V-2 at main - Hugging Face. https://huggingface.co/openbmb/MiniCPM-V-2/blob/main/README.md.
(5) undefined. https://github.com/OpenBMB/OmniLMM.git.

Introducing paper: Object Hallucination in Image Captioning
MathBench
Full name: MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark

MathBench is an All in One math dataset for language model evaluation, with:

A Sophisticated Five-Stage Difficulty Mechanism: Unlike the usual mathematical datasets that can only evaluate a single difficulty level or have a mix of unclear difficulty levels, MathBench provides 3709 questions with a gradient difficulty division by education stages, ranging from basic arithmetic to primary, middle, high school, and college levels, allowing you to get a clear overview of the comprehensive difficulty evaluation results.

Bilingual Gradient Evaluation: Apart from the basic calculation part which is language-independent, MathBench provides questions in both Chinese and English for the four-stage difficulty datasets from primary to college.

Implementation of the Robust Circular Evaluation (CE) Method: MathBench use CE as the main evaluation method for questions. Compared to the general Accuracy evaluation method, CE requires the model to answer the same multiple-choice question multiple times, with the order of the options changing each time. The model is considered correct on this question only if all answers are correct. The results of CE can reflect the model's capabilities more realistically, providing more valuable evaluation results.

Support for Basic Theory Questions: For every stage, MathBench provides questions that cover the basic theory knowledge points of the corresponding stage, to ascertain whether the model has genuinely mastered the fundamental concepts of each stage or merely memorized the answers.

Introducing paper: MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark