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FOMO-AD in AWS fixes 2 #391

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May 22, 2024
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FOMO-AD in AWS fixes 2 #391

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7 changes: 4 additions & 3 deletions image-projects/fomo-ad-in-aws.md
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---
description: >-
Use computer vision and AWS to identify anomalies and ensure the quality of packaged food.
Advanced ML workflow with available Jupyter Notebook using computer vision, AWS SageMaker and MLFlow to benchmark industry visual anomaly models
---

# Optimize a cloud-based Visual Anomaly Detection Model for Edge Deployments

Created By: Mathieu Lescaudron

Public Project Link: [https://studio.edgeimpulse.com/public/376268/latest](https://studio.edgeimpulse.com/public/376268/latest)

GitHub Repo: [https://github.com/emergy-official/anomaly.parf.ai](https://github.com/emergy-official/anomaly.parf.ai)

![](../.gitbook/assets/fomo-ad-in-aws/cover1.png)
Expand All @@ -18,7 +19,7 @@ GitHub Repo: [https://github.com/emergy-official/anomaly.parf.ai](https://github

## Introduction

Let's explore the development and optimization of a cloud-based visual anomaly detection model designed for edge deployments, featuring real-time and serverless inference.
Let's explore the development and optimization of a cloud-based visual anomaly detection model designed for edge deployments, featuring real-time and serverless inference. In this example scenario, we will

We will cover the following topics:

Expand Down Expand Up @@ -74,7 +75,7 @@ We take around five pictures of each cookie, making slight rotations each time.

![](../.gitbook/assets/fomo-ad-in-aws/dataset2.png)

Each picture, taken from a mobile phone in a `1:1` ratio with an original size of 2992 x 2992 pixels, is resized to 1024 x 1024 pixels using [morgify](https://imagemagick.org/script/mogrify.php) command from ImageMagick. It saves computing resources for both the training process and the inference endpoint:
Each picture, taken from a mobile phone in a `1:1` ratio with an original size of 2992 x 2992 pixels, is resized to 1024 x 1024 pixels using [mogrify](https://imagemagick.org/script/mogrify.php) command from ImageMagick. It saves computing resources for both the training process and the inference endpoint:

```
mogrify -resize 1024x1024 *.jpg
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