Omkar2118/datasets huggingface dataset:
Sample Lowlight Images Dataset

Description:

<a href="https://gts.ai/dataset-download/sample-lowlight-images-dataset/" target="_blank">👉 Download the dataset here</a>

This dataset has been meticulously curated to support research in lowlight image enhancement, object detection, and various computer vision tasks that involve challenging lighting conditions. The images were collected during late evening and nighttime, across diverse urban and rural environments. Each image captures real-world lowlight scenes that are prone to noise, blurring, and loss of detail due to insufficient ambient lighting. These samples are ideal for training and testing algorithms that aim to enhance visibility in such conditions.

Download Dataset

Data Collection Process

The dataset consists of images captured using DSLR and smartphone cameras with different exposure settings, ISO, and shutter speeds. Each image is tagged with metadata including the time of capture, camera settings, and environmental factors like street lighting, moon visibility, and weather conditions.

The dataset is divided into five categories to provide a broad range of lowlight environments:

Urban Lowlight: Captured in city streets, alleys, and building exteriors where some artificial lighting like street lamps or vehicle headlights are present.

Rural Lowlight: Taken in less-developed areas with limited to no artificial light sources, capturing natural night environments.

Indoor Lowlight: Captures images inside dimly lit rooms, homes, and spaces, including scenes lit by candles or low-wattage bulbs.

Night Sky Lowlight: Includes images of the sky at night, starry skies, moonlit nights, and various degrees of cloud cover.

Weather-Affected Lowlight: Images taken during rain, fog, or mist, further reducing visibility and introducing additional complexity for enhancement algorithms.

Key Features

Resolution & Variety: The images vary in resolution, ranging from 720p to 4K, offering flexibility for different research needs. Multiple angles and viewpoints of the same scenes are 
captured, ensuring diversity in each category.

Lowlight Severity: The images vary in light intensity, with some having minimal visible light sources, allowing for a more robust analysis of lowlight image processing techniques.

Noise Levels: Some images naturally exhibit high levels of noise, providing excellent training data for denoising algorithms.

Metadata-Driven: Each image includes metadata detailing the specific camera settings use, enabling researchers to understand and adjust for different photography techniques in lowlight conditions.

Dataset Organization

The dataset is organize base on the severity of lighting conditions and noise levels. Images with extremely low light and high noise are separate for advance testing of denoising and image enhancement models. Each image is provide in both RAW and JPEG formats, allowing researchers flexibility in preprocessing.

This dataset is sourced from Kaggle.
MF
Full name: Mathematical Formulas

Mathematical dataset containing formulas based on the [AMPS](https://drive.google.com/file/d/1hQsua3TkpEmcJD_UWQx8dmNdEZPyxw23) Khan dataset and the [ARQMath](https://drive.google.com/drive/folders/1YekTVvfmYKZ8I5uiUMbs21G2mKwF9IAm) dataset V1.3. Based on the retrieved LaTeX formulas, more equivalent versions have been generated by applying randomized LaTeX printing with this [SymPy fork](https://github.com/jdrechsel13/sympy-random-LaTeX). The formulas are intended to be well applicable for MLM. For instance, a masking for a formula like `(a+b)^2 = a^2 + 2ab + b^2` makes sense (e.g., `(a+[MASK])^2 = a^2 + [MASK]ab + b[MASK]2` -> masked tokens are deducable by the context), in contrast, formulas such as `f(x) = 3x+1` are not (e.g., `[MASK](x) = 3x[MASK]1` -> [MASK] tokens are ambigious).

Introducing paper: MAMUT: A Novel Framework for Modifying Mathematical Formulas for the Generation of Specialized Datasets for Language Model Training
MT
Full name: Mathematical Texts

Mathematical dataset containing mathematical texts, i.e., texts containing LaTeX formulas, based on the [AMPS](https://drive.google.com/file/d/1hQsua3TkpEmcJD_UWQx8dmNdEZPyxw23) Khan dataset and the [ARQMath](https://drive.google.com/drive/folders/1YekTVvfmYKZ8I5uiUMbs21G2mKwF9IAm) dataset V1.3. Based on the retrieved LaTeX texts, more mathematically equivalent versions have been generated by applying randomized LaTeX printing with this [SymPy fork](https://github.com/jdrechsel13/sympy-random-LaTeX). A positive `id` corresponds to the ARQMath post id of the generated text version, a negative `id` indicates an AMPS text.

Introducing paper: MAMUT: A Novel Framework for Modifying Mathematical Formulas for the Generation of Specialized Datasets for Language Model Training