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International Journal of Engineering Applied Sciences and Technology, 2023 Vol. 8, Issue 02, ISSN No. 24552143, Pages 195202 Published Online June 2023 in IJEAST (http://www.ijeast.com)DATASET TO
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01
Determine the goals and requirements for the image classification task.
02
Collect and prepare a labeled dataset for training the image classification model.
03
Choose an appropriate neural network architecture for image classification such as CNN.
04
Preprocess the input images by resizing, normalizing, and augmenting them if needed.
05
Train the image classification model using the prepared dataset and optimize hyperparameters.
06
Evaluate the performance of the trained model using validation data and adjust as needed.
07
Test the model on unseen data and fine-tune if necessary to improve accuracy.
08
Deploy the optimized image classification model for practical use.

Who needs optimization of image classification?

01
Researchers working on computer vision projects
02
Companies developing image recognition applications
03
Photography or digital media professionals
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Medical professionals analyzing medical images
05
Manufacturing companies implementing quality control with image analysis
06
Government agencies for surveillance and security purposes
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Optimization of image classification refers to the process of improving the accuracy and efficiency of algorithms used to categorize and identify images based on their content.
Individuals or organizations that utilize machine learning models for image classification in commercial applications may be required to file for optimization.
To fill out optimization of image classification, one must gather relevant data, define model parameters, and submit a standardized form that documents improvements and methodologies.
The purpose of optimization of image classification is to enhance the performance of image recognition systems, reducing errors and improving decision-making accuracy.
Information that must be reported includes the algorithms used, performance metrics, datasets employed for training and testing, and any changes made during the optimization process.
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