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Image Recognition EDA Guide

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Manage episode 463013233 series 3620285
Indhold leveret af David Such. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af David Such eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

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This episode talks about the essentials of exploratory data analysis (EDA) for image recognition. We discuss key techniques—descriptive, diagnostic, and predictive EDA—and outline recommended steps such as image visualization, statistical analysis, anomaly removal, and feature engineering, along with ethical considerations in the process.

We also explore how EDA enhances model accuracy, focusing on the person detection model MCUNet-VWW2 and the Wake Vision dataset. Learn how label correction, data augmentation, and preprocessing improved performance while addressing dataset features, limitations, and the impact of EDA in real-world applications. Join us for an insightful guide to mastering EDA in image recognition!

If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!

  continue reading

20 episoder

Artwork
iconDel
 
Manage episode 463013233 series 3620285
Indhold leveret af David Such. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af David Such eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

Send us a text

This episode talks about the essentials of exploratory data analysis (EDA) for image recognition. We discuss key techniques—descriptive, diagnostic, and predictive EDA—and outline recommended steps such as image visualization, statistical analysis, anomaly removal, and feature engineering, along with ethical considerations in the process.

We also explore how EDA enhances model accuracy, focusing on the person detection model MCUNet-VWW2 and the Wake Vision dataset. Learn how label correction, data augmentation, and preprocessing improved performance while addressing dataset features, limitations, and the impact of EDA in real-world applications. Join us for an insightful guide to mastering EDA in image recognition!

If you are interested in learning more then please subscribe to the podcast or head over to https://medium.com/@reefwing, where there is lots more content on AI, IoT, robotics, drones, and development. To support us in bringing you this material, you can buy me a coffee or just provide feedback. We love feedback!

  continue reading

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