Thermostatic valve (FOMO-AD 96x96) - dataset collected by Edge Impulse - FOMO-AD

Developer Relations / Thermostatic valve (FOMO-AD 96x96) - dataset collected by Edge Impulse Public

Target: Nvidia Jetson Orin NX Clone this project

Anomaly detection settings

Training settings

Training processor

Please provide a valid training processor option

Capacity

Backbone hyperparameters

Number of layers

Advanced training settings

Validation set size

Please provide a valid number for the train/validate split (between 0 and 1)

Split train/validation set on metadata key

Batch size

Auto-weight classes

Profile int8 model

Neural network architecture

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Input layer (27,648 features)

MobileNetV2 0.35

Gaussian Mixture Model (GMM)

Output layer (Anomaly score)

Model

Model version:

By definition there should be as few as possible anomalies in the training dataset, and thus accuracy is not calculated during training. Run Model testing to learn more about the model performance.

On-device performance

Engine:

Inferencing time

2 ms.

Peak RAM usage

434.7K

Flash usage

99.1K

Did you know? You can customize your model through the Expert view (click on to switch),
or can even bring your own model (in PyTorch, Keras or scikit-learn).

Model	Author
Gaussian Mixture Model (GMM) Officially supported Visual anomaly detection for low-dimensional images. Works best with images below 160x160 pixels in size or when defects are above 16x16 pixels in size. Lower memory footprint. Supports both RGB and grayscale.	Edge Impulse
Patchcore Officially supported Visual anomaly detection for high-dimensional or complex images, while also performing well with smaller images. Works better than GMM for images above 160x160 pixels in size or when defects are below 16x16 pixels in size. Higher memory footprint. Supports both RGB and grayscale.	Edge Impulse

The backbone is the foundational element of a neural network model, responsible for extracting meaningful features from input data, enabling subsequent parts to perform anomaly scoring or classification.

Description	Author
EfficientNet V2B0 Uses around 339K RAM based on your input size, and between 240-1675K ROM depending on the number of layers, with default compiler settings. Supports RGB.	Edge Impulse
MobileNetV2 0.35 Uses around 249K RAM based on your input size, and between 69-134K ROM depending on the number of layers, with default compiler settings. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 0.50 Uses around 256K RAM based on your input size, and between 78-168K ROM depending on the number of layers, with default compiler settings. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 0.75 Uses around 489K RAM based on your input size, and between 109-266K ROM depending on the number of layers, with default compiler settings. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 1.0 Uses around 499K RAM based on your input size, and between 126-372K ROM depending on the number of layers, with default compiler settings. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 0.1 Uses around 238K RAM based on your input size, and 58K ROM, with default compiler settings. Supports both RGB and grayscale.	Edge Impulse

The scoring function operates on the features extracted by the backbone, and returns anomaly scores for each segment of the image.

Description	Author	Recommended
Gaussian Mixture Model (GMM) Visual anomaly detection for low-dimensional images. Works best with images below 160x160 pixels in size or when defects are above 16x16 pixels in size. Lower memory footprint. Supports both RGB and grayscale.	Edge Impulse
Patchcore Visual anomaly detection for high-dimensional or complex images, while also performing well with smaller images. Works better than GMM for images above 160x160 pixels in size or when defects are below 16x16 pixels in size. Higher memory footprint. Supports both RGB and grayscale.	Edge Impulse

These users will be notified over email when this job finishes.

	Name
	Jenny Plunkett Edge Impulse staff
	Louis Moreau Edge Impulse staff

Anomaly detection settings

Training settings

Backbone hyperparameters

Augmentation settings

Advanced training settings

Neural network architecture

Model

Confusion matrix (validation set)

Data explorer (full training set)

On-device performance

Inferencing time

Peak RAM usage

Flash usage

Gaussian Mixture Model (GMM)
Officially supported

Patchcore
Officially supported

EfficientNet V2B0

MobileNetV2 0.35

MobileNetV2 0.50

MobileNetV2 0.75

MobileNetV2 1.0

MobileNetV2 0.1

Gaussian Mixture Model (GMM)

Patchcore

Anomaly detection settings

Training settings

Backbone hyperparameters

Augmentation settings

Advanced training settings

Neural network architecture

Model

Confusion matrix (validation set)

Data explorer (full training set)

Settings

On-device performance

Inferencing time

Peak RAM usage

Flash usage