Omkar2118/datasets huggingface dataset:
PyBench
Full name: PyBench

Evaluate LLM on Real-world coding tasks.

Introducing paper: PyBench: Evaluating LLM Agent on various real-world coding tasks
ESG-FTSE
Full name: ESG-FTSE corpus

We present ESG-FTSE, the first corpus comprised of news articles with Environmental, Social and Governance (ESG) relevance annotations. In recent years, investors and regulators have pushed ESG investing to the mainstream due to the urgency of climate change. This has led to the rise of ESG scores to evaluate an investment's credentials as socially responsible. While demand for ESG scores is high, their quality varies wildly. Quantitative techniques can be applied to improve ESG scores, thus, responsible investing. To contribute to resource building for ESG and financial text mining, we pioneer the ESG-FTSE corpus. We further present the first of its kind ESG annotation schema. It has three levels: a binary classification (relevant versus irrelevant news articles), ESG classification (ESG-related news articles), and target company. Both supervised and unsupervised learning experiments for ESG relevance detection were conducted to demonstrate that the corpus can be used in different settings to derive accurate ESG predictions.

Introducing paper: ESG-FTSE: A corpus of news articles with ESG relevance labels and use cases
MentalHAD

Collecting data with a HIKVISION USB Camera DS-E11, we build a dataset called MentalHAD with four abnormal actions (climbing walls, hitting windows, climbing, and hitting) and six normal actions (crouching, standing, sitting, hand waving, walking, and running). It includes RGB videos of about 274 minutes (493504 frames) with 30 FPS in three different scenes, five subjects, and seven scene-subject pairs. They are organized into 69 sequences, each containing the data of only one action in about 2-5 minutes.

Introducing paper: SMART: Scene-motion-aware human action recognition framework for mental disorder group
Spike-X4K
Full name: Spike-X4K Dataset

## Overview
The Spike-X4K Dataset is a high-resolution image reconstruction resource tailored for the latest advancements in spike camera technology. It is designed to meet the demands of modern spike cameras with a resolution of 1000×1000 pixels, surpassing the capabilities of previous datasets like spike-REDS, which was limited to a resolution of 250×400 pixels.

## Dataset Characteristics
- **Resolution**: 1000×1000 pixels, aligning with state-of-the-art spike camera imaging standards.
- **Temporal Depth**: The dataset captures the temporal dynamics of scenes with high-speed motion, providing a temporal sequence of spike frames.
- **Content**: It includes both synthetic and real-world datasets, offering a diverse range of high-speed motion scenarios for training and testing image reconstruction models.
- **Pairs**: The dataset comprises 1200 spike stream-ground truth image pairs for training and 45 pairs for testing, ensuring robust model evaluation.

## Motivation and Content Summary
The development of the Spike-X4K Dataset was motivated by the need for a more representative and higher resolution dataset that could effectively train and evaluate image reconstruction models for spike cameras. Spike cameras, with their unique ability to capture photons independently at each pixel and generate binary spike streams, offer high temporal resolution and low latency, which are crucial for high-speed imaging. However, converting these spike streams into high-quality images requires sophisticated algorithms, and the Spike-X4K Dataset provides the necessary data to develop and refine these algorithms.

## Potential Use Cases
1. **Algorithm Development**: For researchers and engineers working on spike image reconstruction algorithms specific to spike camera data.
2. **Benchmarking**: As a standard for benchmarking the performance of various image reconstruction models against state-of-the-art techniques.
3. **Training and Testing**: Providing a large and diverse set of data for training deep learning models to handle high-speed motion imaging.
4. **Feature Extraction**: Enabling the study and improvement of feature extraction techniques from spike streams, including spatial and temporal features.
5. **Spike Image Reconstruction Tasks**: Supporting a wide range of computer vision tasks that can benefit from high-resolution, high-speed imaging, such as motion analysis, object tracking, and event detection.

Introducing paper: SwinSF: Image Reconstruction from Spatial-Temporal Spike Streams