Merge pull request #400 from dtischler/main

Fabric
edgeimpulse · Jul 9, 2024 · 1876b15 · 1876b15
2 parents c631746 + 4b422a4
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diff --git a/.gitbook/assets/textile-fabric-anomaly-detection/6060push.png b/.gitbook/assets/textile-fabric-anomaly-detection/6060push.png
diff --git a/.gitbook/assets/textile-fabric-anomaly-detection/anomaly_detection_settings.png b/.gitbook/assets/textile-fabric-anomaly-detection/anomaly_detection_settings.png
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diff --git a/.gitbook/assets/textile-fabric-anomaly-detection/data_aquisition.png b/.gitbook/assets/textile-fabric-anomaly-detection/data_aquisition.png
diff --git a/.gitbook/assets/textile-fabric-anomaly-detection/dataset.png b/.gitbook/assets/textile-fabric-anomaly-detection/dataset.png
diff --git a/.gitbook/assets/textile-fabric-anomaly-detection/deployment.png b/.gitbook/assets/textile-fabric-anomaly-detection/deployment.png
diff --git a/.gitbook/assets/textile-fabric-anomaly-detection/fabric.jpeg b/.gitbook/assets/textile-fabric-anomaly-detection/fabric.jpeg
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diff --git a/.gitbook/assets/textile-fabric-anomaly-detection/hardware_setup_1.jpeg b/.gitbook/assets/textile-fabric-anomaly-detection/hardware_setup_1.jpeg
diff --git a/.gitbook/assets/textile-fabric-anomaly-detection/hardware_setup_2.jpeg b/.gitbook/assets/textile-fabric-anomaly-detection/hardware_setup_2.jpeg
diff --git a/.gitbook/assets/textile-fabric-anomaly-detection/impulse_design.png b/.gitbook/assets/textile-fabric-anomaly-detection/impulse_design.png
diff --git a/.gitbook/assets/textile-fabric-anomaly-detection/new_project.png b/.gitbook/assets/textile-fabric-anomaly-detection/new_project.png
diff --git a/.gitbook/assets/textile-fabric-anomaly-detection/on_device_perf.png b/.gitbook/assets/textile-fabric-anomaly-detection/on_device_perf.png
diff --git a/.gitbook/assets/textile-fabric-anomaly-detection/raspberry_pi5.png b/.gitbook/assets/textile-fabric-anomaly-detection/raspberry_pi5.png
diff --git a/.gitbook/assets/textile-fabric-anomaly-detection/raw_features.png b/.gitbook/assets/textile-fabric-anomaly-detection/raw_features.png
diff --git a/.gitbook/assets/textile-fabric-anomaly-detection/testing.png b/.gitbook/assets/textile-fabric-anomaly-detection/testing.png
diff --git a/README.md b/README.md
@@ -86,6 +86,7 @@ Computer vision projects that make use of image classification, object detection
 * [Motorcycle Helmet Identification and Traffic Light Control - Texas Instruments AM62A](image-projects/motorcycle-helmet-detection-smart-light-ti-am62a.md)
 * [Import a Pretrained Model with "Bring Your Own Model" - Texas Instruments AM62A](image-projects/asl-byom-ti-am62a.md)
 * [Product Inspection with Visual Anomaly Detection (FOMO-AD) - Sony Spresense](image-projects/fomo-ad-product-inspection-spresense.md)
+* [Visual Anomaly Detection in Fabric using FOMO-AD - Raspberry Pi](image-projects/textile-fabric-anomaly-detection.md)
 
 ### Audio Projects
 

diff --git a/SUMMARY.md b/SUMMARY.md
@@ -78,6 +78,7 @@
 * [Motorcycle Helmet Identification and Traffic Light Control - Texas Instruments AM62A](image-projects/motorcycle-helmet-detection-smart-light-ti-am62a.md)
 * [Import a Pretrained Model with "Bring Your Own Model" - Texas Instruments AM62A](image-projects/asl-byom-ti-am62a.md)
 * [Product Inspection with Visual Anomaly Detection (FOMO-AD) - Sony Spresense](image-projects/fomo-ad-product-inspection-spresense.md)
+* [Visual Anomaly Detection in Fabric using FOMO-AD - Raspberry Pi](image-projects/textile-fabric-anomaly-detection.md)
 
 ## Audio Projects
 

diff --git a/image-projects/fomo-ad-product-inspection-spresense.md b/image-projects/fomo-ad-product-inspection-spresense.md
@@ -122,6 +122,6 @@ Check our demo test video for an example of how it works:
 
 {% embed url="https://youtu.be/nLFFdjzscZY" %}
 
-## Conclusion:
+## Conclusion
 
 We have successfully created a Product Inspection with Visual Anomaly Detection project by training a machine learning model with only one class label, "No Anomaly." The model has successfully detected anomalies along with their locations, making it easier for us to identify which part or section has an issue. By using FOMO-AD, the machine learning model can be deployed to a microcontroller. This enables a cost-effective and energy-efficient solution for the manufacturing industry, especially in sorting systems and visual inspection lines.
diff --git a/image-projects/textile-fabric-anomaly-detection.md b/image-projects/textile-fabric-anomaly-detection.md