In the article, the author discusses the journey of selecting an image classification model and emphasizes the importance of data labeling and curation. The author has chosen Inception V4 for its capacity to handle large images and quick inference times. They advocate for rigorous training of the chosen model while analyzing performance statistics to justify the efforts involved. Each part of the series progressively builds on these foundational elements, showcasing how to optimize the modeling process and the value of well-prepared data for achieving high classification accuracy.
It's crucial to focus on the quality of your labeled data and the careful selection of your model, which ultimately drives performance in image classification.
After settling on Inception V4, I found its large input capacity and fast inference made it a suitable choice, proving consistent six years post-deployment.
The process of labeling and curating images should come before the intricate details of model architecture, as they form the foundation of a successful classification project.
By diligently training a selected model and analyzing performance, you can justify your efforts with meaningful usage and performance statistics that validate your image classification work.
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