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Data in Brief 48 (2023) 109108Contents lists available at ScienceDirectData in Brief journal homepage: www.elsevier.com/locate/dibData ArticleMachine Learning Imagery Dataset for Maize Crop: A Case

How to fill out machine learning imagery dataset

How to fill out machine learning imagery dataset

1. Define the purpose of your dataset: Identify the specific machine learning task (e.g., image classification, object detection).
2. Collect images: Gather a diverse set of images relevant to your task from reliable sources.
3. Organize images: Create folders to categorize images based on labels or classes.
4. Label images: Use annotation tools to tag images with appropriate labels (e.g., bounding boxes for object detection).
5. Ensure quality: Review the images to maintain high quality and remove duplicates or irrelevant photos.
6. Split the dataset: Divide the dataset into training, validation, and test sets to evaluate model performance.
7. Format the dataset: Convert the images and labels into the necessary format required by your machine learning framework.

Who needs machine learning imagery dataset?

1. Data scientists working on computer vision projects.
2. Researchers investigating machine learning and artificial intelligence.
3. Businesses developing applications that leverage image recognition.
4. Educational institutions teaching machine learning concepts.
5. Developers building automated systems for image processing.

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What is machine learning imagery dataset?

A machine learning imagery dataset is a collection of images used to train algorithms for tasks such as recognition, classification, and segmentation in machine learning applications.

Who is required to file machine learning imagery dataset?

Researchers, developers, and organizations working with machine learning projects involving imagery are typically required to file and report their datasets.

How to fill out machine learning imagery dataset?

To fill out a machine learning imagery dataset, one needs to provide information about the dataset, such as the source of images, the format of data, labels for classification, any preprocessing steps, and metadata about the collection.

What is the purpose of machine learning imagery dataset?

The purpose of a machine learning imagery dataset is to provide a structured and annotated collection of images that can be used to train and validate machine learning models for various applications.

What information must be reported on machine learning imagery dataset?

Information that must be reported includes dataset description, number of images, image resolution, annotation details, versioning, usage rights, and any relevant preprocessing information.