Omkar2118/datasets huggingface dataset:
E-commerce Product Image Classification Dataset

Description:

<a href="https://gts.ai/dataset-download/e-commerce-product-image-classification-dataset/" target="_blank">👉 Download the dataset here</a>

This dataset is specifically designed for the classification of e-commerce products based on their images, forming a critical part of an experimental study aimed at improving product categorization using computer vision techniques. Accurate categorization is essential for e-commerce platforms as it directly influences customer satisfaction, enhances user experience, and optimizes sales by ensuring that products are presented in the correct categories.

Data Collection and Sources

The dataset comprises a comprehensive collection of e-commerce product images gathered from a diverse range of sources, including prominent online marketplaces such as Amazon, Walmart, and Google, as well as additional resources obtained through web scraping. Additionally, the Amazon Berkeley Objects (ABO) project has been utilized to enhance the dataset in certain categories, though its contribution is limited to specific classes.

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Dataset Composition and Structure

The dataset is organized into 9 distinct classes, primarily reflecting major product categories prevalent on Amazon. These categories were chosen based on a balance between representation and practicality, ensuring sufficient diversity and relevance for training and testing computer vision models. The dataset's structure includes:

18,175 images: Resized to 224x224 pixels, suitable for use in various pretrained CNN architectures.

9 Classes: Representing major e-commerce product categories, offering a broad spectrum of items typically found on online retail platforms.

Train-Val-Check Sets: The dataset is split into training, validation, and check sets. The training and validation sets are designated for model training and hyperparameter tuning, while a smaller check set is reserved for model deployment, providing a visual evaluation of the model's performance in a real-world scenario.

Application and Relevance

E-commerce platforms face significant challenges in product categorization due to the vast number of categories, the variety of products, and the need for precise classification. This dataset addresses these challenges by offering a well-balanced collection of images across multiple categories, allowing for robust model training and evaluation.

This dataset is sourced from kaggle.
Anime vs Cartoon Dataset

Description:

<a href="https://gts.ai/dataset-download/anime-vs-cartoon-dataset/" target="_blank">👉 Download the dataset here</a>

The Anime vs Cartoon Dataset is a specialized collection of images designed to differentiate between two popular forms of animation: anime and cartoons. While both anime and cartoons share similarities in being animated visual media, they possess distinct stylistic and structural characteristics that set them apart. This dataset is intended for use in developing and testing convolutional neural networks (CNNs) or other machine learning models for classification tasks, enabling the automated identification of whether an image belongs to the anime or cartoon category.

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Context and Motivation

Anime and cartoons are both beloved forms of entertainment worldwide, yet they originate from different cultural contexts and exhibit unique artistic styles. Anime, typically associated with Japanese culture, often features detailed backgrounds, unique facial expressions, and a wide range of color palettes, while cartoons, more commonly linked to Western culture, tend to have simpler designs, more exaggerated features, and distinct outlines. These differences, though subtle, provide a rich ground for developing image classification models that can automatically distinguish between the two.

Dataset Content

The dataset consists of images categorized into two distinct classes: Anime and Cartoon. The images were carefully collected and labeled to ensure a balanced representation of both categories. The images span various styles and themes within each category, including different character designs, backgrounds, and color schemes, providing a diverse set of examples for model training.

Anime: Images classified as anime, representing the distinctive characteristics of Japanese animation.

Cartoon: Images classified as cartoons, showcasing the unique style typical of Western animation.

Applications

This dataset is ideal for

Developing convolutional neural networks (CNNs) for image classification tasks.

Fine-tuning pre-trained models on a new domain-specific dataset.

Exploring the stylistic differences between anime and cartoon animations.

Building automated systems for content moderation or media sorting based on animation type.

This dataset is sourced from Kaggle.
Crowd Counting Dataset

Description:

<a href="https://gts.ai/dataset-download/crowd-counting-dataset/" target="_blank">👉 Download the dataset here</a>

The Crowd Counting Dataset is an extensive collection of high-resolution images capturing a wide variety of crowd scenes, with the number of individuals ranging from zero to 5,000 per image. The dataset is meticulously curated to include diverse scenarios such as public events, street gatherings, protests, festivals, and daily commutes, ensuring that it covers a broad spectrum of crowd densities and environmental conditions.

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Key Features:

Diverse Scenarios: The dataset features crowds in various settings, from dense urban environments to open public spaces, providing a comprehensive resource for developing and testing crowd counting algorithms across different contexts.

High-Quality Annotations: Each image in the dataset is paired with a JSON file that includes precise annotations for every individual in the crowd. The labeling details not only the count of people but also includes classification data such as gender, age group, and activity, enabling multi-dimensional analysis.

Versatility in Applications: This dataset is ideal for training and evaluating machine learning models for applications in public safety, event management, urban planning, and retail analysis, where accurate crowd estimation and behavior analysis are critical.

Scalable Data: With a wide range of crowd sizes, the dataset is suitable for both low-density and high-density crowd counting tasks, providing a robust foundation for developing scalable Al solutions.

Real-World Relevance: The images are sourced from real-world environments, ensuring that models trained on this dataset can generalize well to practical applications, enhancing their reliability in real-time deployments.

Dataset Structure:

Images: High-resolution images captured from various angles and lighting conditions.

Annotations: JSON files containing detailed labels for each individual, including:

Person count

Classification attributes (e.g., age group, gender, activity)

Positional information within the image for precise localization

Categories: The dataset is organized into different categories based on crowd density, scene type, and time of day, allowing users to focus on specific aspects of crowd analysis.

This dataset is sourced from Kaggle.
Printed Photos Attacks Dataset

Description:

<a href="https://gts.ai/dataset-download/printed-photos-attacks-dataset/" target="_blank">👉 Download the dataset here</a>

The Printed Photos Attacks Dataset is a specialize resource design for the development and evaluation of liveness detection systems aimed at combating facial spoofing attempts. This dataset includes a comprehensive collection of videos that feature both authentic facial presentations and spoof attempts using print 2D photographs. By incorporating both real and fake faces, it provides a robust foundation for training and testing advanced facial recognition and anti-spoofing algorithms.

This dataset is particularly valuable for researchers and developers focus on enhancing biometric security systems. It introduces a novel method for learning and extracting distinctive facial features to effectively differentiate between genuine and spoofed inputs. The approach leverages deep neural networks (DNNs) and sophisticate biometric techniques, which have been shown to significantly improve the accuracy and reliability of liveness detection in various applications.

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Key features of the dataset include:

Diverse Presentation Methods: The dataset contains a range of facial presentations, including genuine facial videos and spoof videos create using high-quality print photographs. This diversity is essential for developing algorithms that can generalize across different types of spoofing attempts.

High-Resolution Videos: The videos in the dataset are capture in high resolution, ensuring that even subtle facial features and movements are visible, aiding in the accurate detection of spoofing.

Comprehensive Annotations: Each video is meticulously annotate with labels indicating whether the facial presentation is genuine or spoofed. Additionally, the dataset includes metadata such as the method of spoofing and environmental conditions, providing a rich context for algorithm development.

Unseen Spoof Detection: One of the unique aspects of this dataset is its emphasis on detecting unseen spoofing cues. The dataset is design to challenge algorithms to identify and adapt to new types of spoofing methods that may not have been encounter during the training phase.

Versatile Application: The dataset is suitable for a wide range of applications, including access control systems, mobile device authentication, and other security-sensitive environments where facial recognition is deploy.

This dataset is sourced from Kaggle.
VSAI Dataset

Description:

<a href="https://gts.ai/dataset-download/vsai-dataset/" target="_blank">👉 Download the dataset here</a>

The VSAI (Vehicle Surveillance via Aerial Imagery) Dataset is a highly versatile and expansive collection of high-resolution aerial images gathered from multiple drone platforms. Covering a diverse geographical expanse, this dataset is specifically curated to support vehicle detection tasks in various complex real-world environments. It offers a multi-view perspective of different urban and rural areas, captured under a wide range of conditions.

This unique dataset is designed for advancing object detection in aerial imagery by addressing numerous real-world challenges, such as varying camera angles, diverse flight altitudes, changing weather conditions, and fluctuating light intensities. VSAI captures imagery from different times of the day and seasons, making it an essential resource for building more robust vehicle detection models.

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Dataset Composition

The VSAI dataset contains an impressive 49,712 vehicle instances meticulously annotated using oriented bounding boxes and arbitrary quadrilateral bounding boxes. This level of precision in annotation accommodates varying object shapes and positions in aerial views, enhancing the dataset's utility for training advanced object detection algorithms. It includes annotations for:

Small Vehicles: 47,519 instances

Large Vehicles: 2,193 instances

To enhance model robustness, the dataset also includes detailed annotations of occlusion rates, helping algorithms generalize better under challenging scenarios like partial visibility caused by environmental factors or other objects.

Key Features

Diverse Capture Conditions: The images in VSAI are captured across multiple drone platforms, which vary in altitude, camera angles, and flight paths.

Complex Scenarios: The dataset spans a wide range of environments, including urban centers, rural landscapes, industrial areas, and highways, representing real-world complexities.

Varied Temporal and Weather Conditions: Images have been captured during different times of the day and under diverse weather conditions, including clear skies, overcast, rainy, and foggy environments.

High Annotation Accuracy: Vehicle instances are annotated with highly precise bounding boxes, including both oriented and arbitrary quadrilateral shapes, to reflect the true contours of vehicles as seen from an aerial perspective.

Occlusion Annotation: Objects in the dataset are also labeled with their occlusion levels, providing additional challenges for model training and improving detection performance in scenarios with partially visible vehicles.

This dataset is sourced from Kaggle.
LUN

LUN is used for unreliable news source classification, this dataset includes 17,250 articles from satire, propaganda, and hoaxe.

Introducing paper: Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking
L-Eval

Although large language models (LLMs) demonstrate impressive performance for many language tasks, most of them can only handle texts a few thousand tokens long, limiting their applications on longer sequence inputs, such as books, reports, and codebases. Recent works have proposed methods to improve LLMs' long context capabilities by extending context windows and more sophisticated memory mechanisms. However, comprehensive benchmarks tailored for evaluating long context understanding are lacking. In this paper, we introduce LongBench, the first bilingual, multi-task benchmark for long context understanding, enabling a more rigorous evaluation of long context understanding. LongBench comprises 21 datasets across 6 task categories in both English and Chinese, with an average length of 6,711 words (English) and 13,386 characters (Chinese). These tasks cover key long-text application areas including single-doc QA, multi-doc QA, summarization, few-shot learning, synthetic tasks, and code completion. All datasets in LongBench are standardized into a unified format, allowing for effortless automatic evaluation of LLMs. Upon comprehensive evaluation of 8 LLMs on LongBench, we find that: (1) Commercial model (GPT-3.5-Turbo-16k) outperforms other open-sourced models, but still struggles on longer contexts. (2) Scaled position embedding and fine-tuning on longer sequences lead to substantial improvement on long context understanding. (3) Context compression technique such as retrieval brings improvement for model with weak ability on long contexts, but the performance still lags behind models that have strong long context understanding capability. The code and datasets are available at https://github.com/THUDM/LongBench.

Introducing paper: L-Eval: Instituting Standardized Evaluation for Long Context Language Models
Mr. HiSum

Mr. HiSum is a large-scale video highlight detection and summarization dataset, which contains 31,892 videos selected from YouTube-8M dataset and reliable frame importance score labels aggregated from 50,000+ users per video.