Omkar2118/datasets huggingface dataset:
Paimon Dataset YOLO Detection

Description:

<a href="https://gts.ai/dataset-download/paimon-dataset-yolo-detection/" target="_blank">👉 Download the dataset here</a>

This dataset consists of a diverse collection of images featuring Paimon, a popular character from the game Genshin Impact. The images have been sourced from in-game gameplay footage and capture Paimon from various angles and in different sizes (scales), making the dataset suitable for training YOLO object detection models.

The dataset provides a comprehensive view of Paimon in different lighting conditions, game environments, and positions, ensuring the model can generalize well to similar characters or object detection tasks. While most annotations are accurately labeled, a small number of annotations may include minor inaccuracies due to manual labeling errors. This is ideal for researchers and developers working on character recognition, object detection in gaming environments, or other AI vision tasks.

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Dataset Features:

Image Format: .jpg files in 640×320 resolution.

Annotation Format: .txt files in YOLO format, containing bounding box data with:

class_id

x_center

y_center

width

height

Use Cases:

Character Detection in Games: Train YOLO models to detect and identify in-game characters or NPCs.

Gaming Analytics: Improve recognition of specific game elements for AI-powered game analytics tools.

Research: Contribute to academic research focused on object detection or computer vision in animated and gaming environments.

Data Structure:

Images: High-quality .jpg images captured from multiple perspectives, ensuring robust model training across various orientations and lighting scenarios.

Annotations: Each image has an associated .txt file that follows the YOLO format. The annotations are structured to include class identification, object location (center coordinates), and 

bounding box dimensions.

Key Advantages:

Varied Angles and Scales: The dataset includes Paimon from multiple perspectives, aiding in creating more versatile and adaptable object detection models.

Real-World Scenario: Extracted from actual gameplay footage, the dataset simulates real-world detection challenges such as varying backgrounds, motion blur, and changing character scales.

Training Ready: Suitable for training YOLO models and other deep learning frameworks that require object detection capabilities.

This dataset is sourced from Kaggle.
LigBoundConf 2.0

LigBoundConf is a set of high-quality drug-like bound ligand structures in the Protein Data Bank (PDB). The original set contains three sets of structures: "raw” structures taken directly from the PDB, "bound” structures, obtained by performing a local minimization of the ligand in the presence of the protein pocket with an empirical force field, and “global minimum” structures, obtained by finding the lowest energy conformation of the ligand with an empirical force field and implicit water solvation model.

LigBoundConf 2.0 is a neutral subset of LigBoundConf in which we have recalculated the "bound" and "global minimum" structures in vaccuo without an implicit solvent. To obtain the "bound” structures we sought to relieve any artificial high-energy artifacts from the deposited PDB structures. We did this by running a local minimization with the semi-empirical quantum chemistry method GFN2-xTB using the “crude” convergence criteria. This ensured that artifacts were removed without significantly altering the bound pose of the ligand. We then performed single point energy (SPE) density fucntional theory (DFT) calculations at the ωB97M-D3(BJ)/def2-TZVPPD level of theory.

To obtain the "global minimum” structures, we used the CREST enumeration method with 50 conformers, which uses the same GFN2-xTB energy function to search for low energy gas phase structures. We took the lowest energy structure as the ``global minimum” conformation and performed a SPE DFT calculation at the same ωB97M-D3(BJ)/def2-TZVPPD level of theory.

The LigBoundConf 2.0 neutral dataset ended up with 3132 ligands.

Introducing paper: Strain Problems got you in a Twist? Try StrainRelief: A Quantum-Accurate Tool for Ligand Strain Calculations
V2 Balloon Detection Dataset

Description:

<a href="https://gts.ai/dataset-download/v2-balloon-detection-dataset/" target="_blank">👉 Download the dataset here</a>

This dataset was created to serve as an easy-to-use image dataset, perfect for experimenting with object detection algorithms. The main goal was to provide a simplified dataset that allows for quick setup and minimal effort in exploratory data analysis (EDA). This dataset is ideal for users who want to test and compare object detection models without spending too much time navigating complex data structures. Unlike datasets like chest x-rays, which require expert interpretation to evaluate model performance, the simplicity of balloon detection enables users to visually verify predictions without domain expertise.

The original Balloon dataset was more complex, as it was split into separate training and testing sets, with annotations stored in two separate JSON files. To streamline the experience, this updated version of the dataset merges all images into a single folder and replaces the JSON annotations with a single, easy-to-use CSV file. This new format ensures that the dataset can be loaded seamlessly with tools like Pandas, simplifying the workflow for researchers and developers.

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The dataset contains a total of 74 high-quality JPG images. Each featuring one or more balloons in different scenes and contexts. Accompanying the images is a CSV file that provides annotation data.  Such as bounding box coordinates and labels for each balloon within the images. This structure makes the dataset easily accessible for a range of machine learning and computer vision tasks.  Including object detection and image classification. The dataset is versatile and can be used to test algorithms like YOLO, Faster R-CNN, SSD, or other popular object detection models.

Key Features:

Image Format: 74 JPG images, ensuring high compatibility with most machine learning frameworks.

Annotations: A single CSV file that contains structure data. Including bounding box coordinates, class labels, and image file names, which can be load with Python libraries like Pandas.

Simplicity: Design for users to quickly start training object detection models without needing to preprocess or deeply explore the dataset.

Variety: The images feature balloons in various sizes, colors, and scenes, making it suitable for testing the robustness of detection models.

This dataset is sourced from Kaggle.
Geolocated Imagery Dataset (Scotland)

Description:

<a href="https://gts.ai/dataset-download/geolocated-imagery-dataset-scotland/" target="_blank">👉 Download the dataset here</a>

This dataset offers a comprehensive collection of geolocated images captured in Scotland by the Defence Science and Technology Laboratory (Dstl) in February 2019. The dataset is designed to support the development and testing of algorithms that can infer an image’s geographical location based on its visual content rather than relying solely on metadata. This capability is particularly valuable in defense, security, and emergency scenarios, where images may lack precise metadata or timestamps but still need to be geolocated.

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Key Use Cases

One of the primary defense applications for this dataset is to assist in locating hostages or other persons of interest from visual media such as photos or videos sent by captors. In such critical cases, intelligence analysts may only have limited visual data, such as one or two images, and may lack accurate metadata regarding the time or location of the capture. This dataset aids in the creation of machine learning algorithms capable of analyzing the content within the images—such as landscapes, buildings, or natural features—to predict the location.

Dataset Composition and Structure

The dataset consists of a variety of images representing both urban and rural environments, providing diverse visual cues such as buildings, streets, landscapes, and natural elements (e.g., forests, hills). Images were captured using a mobile phone, resulting in mixed quality, a deliberate decision to simulate real-world conditions where image quality may vary. These variations in quality and orientation challenge algorithm development, ensuring robustness across different scenarios.

Geolocation Accuracy

Each image in this dataset is accompanied by geolocation data, which has undergone manual inspection and corrections to mitigate inaccuracies caused by mobile GPS lag. The dataset boasts an accuracy within 100 meters for all images, with most achieving significantly higher accuracy. This ensures that the dataset is suitable for high-precision geolocation tasks, providing a reliable foundation for model training and evaluation.

Privacy and Data Modifications

To safeguard privacy, all identifiable faces and vehicle license plates in the images have been blurred. Apart from these modifications, the images have been kept in their original form to preserve the authenticity and realism of the dataset. The images span a range of angles, orientations, and times of day, making them ideal for training models that must function under variable conditions.

Data Quality and Variety

The dataset emphasizes realistic image conditions, offering a diverse range of visual content:

Urban and rural settings.

Varied lighting and weather conditions.

Different image orientations and angles to simulate real-world data variability.

This dataset is sourced from Kaggle.