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In this case study, I combine several subcategories of data not based on item color but based on item type. These types will be classified into types of clothing and shoes. The result of merging these subcategories is that there are several main categories which are divided into 5 categories, namely Dress, Pants, Shirt, Shoes, Shorts. I'm trying to learn and build a deep learning approach with CNN Architecture using the pre-trained model "InceptionResNetV2" variant and the RMSprob optimizer to fit the data and then used to predict the image data. And in the last part, the model will be saved in TF-LITE format for development purposes (such as mobile applications).

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image-classification deep-learning image-classfication deployment pretrained-models

image-classification-using-deep-learning-pretrained-model's Introduction

Hi there

  • ๐Ÿ“Œ Currently working as Analytics Data Engineer
  • ๐Ÿ“ Highly interested in the field of machine learning, and data-driven analysis when solving problems that leads to optimum performance.
  • ๐Ÿ“‘ Specialized in Python, RDBMS (SQL (MySQL, PostgreSQL, Oracle, SQL Server)), Bigdata Area (Apache Spark), Text Mining, and NLP.
  • ๐ŸŽฏ Familiar tools such as VSCode, Jupyter Notebook, DBeaver, HUE.
  • ๐Ÿ‘จโ€๐Ÿ’ป All of my projects are available at https://github.com/nikenaml
  • ๐Ÿ”— How to reach me and Know about my experiences https://withkoji.com/@Niken

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