AI-driven HVAC Fault Diagnosis (Thermal) - FOMO-AD

Edge Impulse Experts / AI-driven HVAC Fault Diagnosis (Thermal) Public

Target: Cortex-M4F 80MHz Clone this project

Anomaly detection settings

Training settings

Training processor

Please provide a valid training processor option

Capacity

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

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

738 ms.

Peak RAM usage

238.7K

Flash usage

70.1K

These metrics are approximations, as the anomaly scoring layer is not included.

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
MobileNetV2 0.35 Officially supported Uses around 296.8K RAM and 575.2K ROM with default settings and optimizations. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 0.1 Officially supported Uses around 270.2K RAM and 212.3K ROM with default settings and optimizations. 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 Supports RGB.	Edge Impulse
MobileNetV2 0.35 Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 0.50 Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 0.75 Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 1.0 Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 0.1 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
MobileNetV2 0.35 Uses around 296.8K RAM and 575.2K ROM with default settings and optimizations. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 0.1 Uses around 270.2K RAM and 212.3K ROM with default settings and optimizations. Supports both RGB and grayscale.	Edge Impulse

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

	Name
	Kutluhan Aktar

Anomaly detection settings

Training settings

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

Confusion matrix (validation set)

Data explorer (full training set)

On-device performance

Inferencing time

Peak RAM usage

Flash usage

MobileNetV2 0.35
Officially supported

MobileNetV2 0.1
Officially supported

EfficientNet V2B0

MobileNetV2 0.35

MobileNetV2 0.50

MobileNetV2 0.75

MobileNetV2 1.0

MobileNetV2 0.1

MobileNetV2 0.35

MobileNetV2 0.1

Anomaly detection settings

Training settings

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

Confusion matrix (validation set)

Data explorer (full training set)

Settings

On-device performance

Inferencing time

Peak RAM usage

Flash usage