Omkar2118/datasets huggingface dataset:
Fruits Dataset for Classification

Fruits Dataset for Classification
About Dataset

(strawberries, peaches, pomegranates) Photo requirements:
1-White background
2-.jpg
3- Image size 300*300
The number of photos required is 250 photos of each fruit when it is fresh and 250 photos of each fruit when it is rotten.
Total 1500 images

Diverse Collection
With a diverse collection of Product images, the files provides an excellent foundation for developing and testing machine learning models designed for image recognition and allocation. Each image is captured under different lighting conditions and backgrounds, offering a realistic challenge for algorithms to overcome.

Real-World Applications
The variability in the dataset ensures that models trained on it can generalize well to real-world scenarios, making them robust and reliable. The dataset includes common fruits such as apples, bananas, oranges, and strawberries, among others, allowing for comprehensive training and evaluation.

Industry Use Cases
One of the significant advantages of using the Fruits Dataset for Classification is its applicability in various fields such as agriculture, retail, and the food industry. In agriculture, it can help automate the process of fruit sorting and grading, enhancing efficiency and reducing labor costs. In retail, it can be used to develop automated checkout systems that accurately identify fruits, streamlining the purchasing process.

Educational Value
The dataset is also valuable for educational purposes, providing students and educators with a practical tool to learn and teach machine learning concepts. By working with this dataset, learners can gain hands-on experience in data preprocessing, model training, and evaluation.

Conclusion
The Fruits Dataset for Classification is a versatile and indispensable resource for advancing the field of image classification. Its diverse and high-quality images, coupled with practical applications, make it a go-to dataset for researchers, developers, and educators aiming to improve and innovate in machine learning and computer vision.

This dataset is sourced from Kaggle.

Introducing paper: Fruit and Vegetable Identification Using Machine Learning for Retail Applications
Weapon Detection Dataset

About Dataset
Context
With increasing unrest, security cams should be armed with advance technology. 

Content
This dataset contains weapon disclosure note in pascal VOC format. Each image has its corresponding notes xml file. They are in xmin, ymin, xmax, ymax.
Categories has knife,Gun and other weapons etc.

The Importance of Weapon Detection in Security: Discussing the significance of accurate weapon detection in add to security measures and avoid potential threats.

Data Composition: Providing insights into the composition of the firearm disclosure data, including the types of fire arms covered, diverse environments, and scenarios depicted.

Annotated Data for Enhanced Analysis: Explaining the importance of annotated data in facilitating object localization, classification tasks, and algorithm training for weapon detection.

Quality Assurance and Validation Processes: Detailing the rigorous quality assurance and validation procedures employed to ensure the accuracy and reliability of the dataset.

Applications and Use Cases: Exploring the various applications and use cases of the firearms disclosure data, including training machine learning models, benchmarking algorithms, and evaluating system performance.

Future Directions and Impact: Discussing the potential impact of the dataset on advancing fire arms disclosure technology, enhancing security protocols, and contributing to safer communities.

This dataset is sourced from Kaggle.

Introducing paper: Weaponizing Unicodes with Deep Learning -- Identifying Homoglyphs with Weakly Labeled Data