Coffee Bean Dataset Resized
Coffee Bean Dataset Resized
Datasets
Coffee Bean Dataset Resized
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Coffee Bean Dataset Resized
Use Case
Coffee Bean Dataset Resized
Description
Coffee Bean Dataset Resized: Discover 4800 high-resolution images of coffee beans at four roasting levels. Ideal for machine learning, quality control, and research.
Description:
This dataset is a resized version of the original Coffee Bean Dataset Version 1, offering high-quality images of roasted coffee beans, meticulously captured to aid in various machine learning and image recognition tasks.
Dataset Details:
- Roasting Levels: The dataset includes coffee beans roasted at four distinct levels:
- Green (Unroasted) Coffee Beans: Laos Typica Bolaven (Coffea arabica)
- Lightly Roasted Beans: Laos Typica Bolaven (Coffea arabica)
- Medium Roasted Beans: Doi Chaang (Coffea arabica)
- Dark Roasted Beans: Brazil Cerrado (Coffea arabica)
- Photography Details:
- The images were captured using an iPhone 12 Mini, equipped with a 12-megapixel back camera, featuring Ultra-wide and WideCamera capabilities.
- The camera setup was designed to ensure a plane parallel to the object’s path during image capture, guaranteeing consistency and clarity.
- Photographs were taken under various lighting conditions, including both LED light from a lightbox and natural light, to simulate different real-world environments and enhance the dataset’s versatility.
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Image Specifications:
- Format: PNG
- Resolution: Each image measures 3024×3032 pixels, ensuring high-resolution details suitable for detailed analysis and machine learning tasks.
- Quantity: The dataset comprises a total of 4800 images, divided evenly across the four roasting levels, with 1200 images per level.
Additional Features:
- Noise Enhancement: To simulate real-world scenarios, images include enhanced noise by placing each variety of coffee beans in a container. This feature aims to improve the robustness of machine learning models trained using this dataset.
- Diverse Settings: The dataset includes images captured in a variety of settings, providing a broad spectrum of inputs to validate and train image recognition systems effectively.
Applications:
This comprehensive dataset is ideal for:
- Machine Learning: Training models for image classification, object detection, and other AI tasks.
- Quality Control: Automated inspection systems in coffee production and roasting facilities.
- Research: Studies related to food processing, quality analysis, and agricultural research.
- Educational Purposes: Teaching materials for courses on machine learning, image processing, and computer vision.
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