Omkar2118/datasets huggingface dataset:
OSPtrack: A Labeled Dataset Targeting Simulated Execution of Open-Source Software

capturing features generated duringthe execution of packages and libraries in isolated environments.The dataset includes 9,461 package reports, of which 1,962 are
identified as malicious, and encompasses both static and dynamic
features such as files, sockets, commands, and DNS records.
Each report is labeled with verified information and detailed
sub-labels for attack types, facilitating the identification of
malicious indicators when source code is unavailable. This dataset
supports runtime detection, enhances detection model training,
and enables efficient comparative analysis across ecosystems,
contributing to the strengthening of supply chain security

Introducing paper: OSPtrack: A Labeled Dataset Targeting Simulated Execution of Open-Source Software
Fruit and Vegetable Disease Dataset

Description:

<a href="https://gts.ai/dataset-download/fruit-and-vegetable-disease-dataset/" target="_blank">👉 Download the dataset here</a>

The Fruit and Vegetable Disease Dataset offers an extensive collection of categorize images to aid in building machine learning models designed to identify diseases affecting various fruits and vegetables. This dataset is particularly suited for image classification tasks and deep learning applications, helping to distinguish between healthy and diseased produce.

Dataset Structure

The dataset is organize into 28 distinct directories, with each directory containing images of 14 different fruits and vegetables, divided into healthy and rotten categories. It is specifically structured to assist in the training and testing of machine learning models that aim to detect diseases in fruits and vegetables. The clear categorization and organization of the dataset make it easy for users to implement in image classification and deep learning tasks.

Download Dataset

Data Source and Collection

This dataset was curated from reliable public sources. Every image in the dataset has undergone a manual review to ensure its quality and relevance, making it a valuable resource for developing machine learning models for disease detection in the agricultural sector.

Applications

This dataset is highly adaptable and supports a variety of machine learning tasks, such as:

Image Classification

Transfer Learning

Deep Learning

It is especially well-suited for training models to recognize and differentiate between healthy and diseased produce, making it a valuable tool in agricultural technology and food quality monitoring.

Metadata

Total Classes: 28 (14 fruits and vegetables, with separate categories for healthy and rotten produce)

File Types: JPEG/PNG

Image Dimensions: Varies (It is recommended to standardize image sizes during preprocessing for consistent model training.)

Best Practices

To make the most of this dataset, the following steps are recommend:

Preprocessing: Normalize image sizes to improve model performance.

Augmentation: Apply various data augmentation techniques to increase dataset variability and strengthen model generalization.

Model Evaluation: Reserve a portion of the dataset for validation purposes to better fine-tune and evaluate model accuracy.

This dataset is sourced from Kaggle.
XM 3600
Full name: Crossmodal 3600

Research in massively multilingual image captioning has been severely hampered by a lack of high-quality evaluation datasets. In this paper we present the Crossmodal-3600 dataset (XM3600 in short), a geographically-diverse set of 3600 images annotated with human-generated reference captions in 36 languages. The images were selected from across the world, covering regions where the 36 languages are spoken, and annotated with captions that achieve consistency in terms of style across all languages, while avoiding annotation artifacts due to direct translation. We apply this benchmark to model selection for massively multilingual image captioning models, and show strong correlation results with human evaluations when using XM3600 as golden references for automatic metrics.

Introducing paper: Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset