CorrosionDetection - Transfer learning

Edge Impulse Experts / CorrosionDetection Public

Target: Cortex-M4F 80MHz Clone this project

Neural Network settings

Training settings

Number of training cycles

Please provide a valid number of training cycles (numeric only)

Use learned optimizer

Learning rate

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Training processor

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Data augmentation

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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import math, requests
 
 
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Input layer (27,648 features)

MobileNetV2 96x96 0.35 (final layer: 16 neurons, 0.1 dropout)

Output layer (2 classes)

Model

Model version:

Last training performance (validation set)

Accuracy

79.2%

Loss

0.48

Confusion matrix (validation set)

	norust	rust
norust	61.5%	38.5%
rust	0%	100%
f1 score	0.76	0.81

Data explorer (full training set)

Error while loading features: {"response":{"readyState":0,"status":0,"statusText":"error"},"errorThrown":""}

On-device performance

Engine:

Inferencing time

2863 ms.

Peak RAM usage

346.9K

Flash usage

585.5K

	norust	rust
norust	84.6%	15.4%
rust	0%	100%
f1 score	0.92	0.92

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
MobileNetV1 96x96 0.25 Officially supported A pre-trained multi-layer convolutional network designed to efficiently classify images. Uses around 105.9K RAM and 301.6K ROM with default settings and optimizations.	Edge Impulse
MobileNetV1 96x96 0.2 Officially supported Uses around 83.1K RAM and 218.3K ROM with default settings and optimizations. Works best with 96x96 input size. Supports both RGB and grayscale.	Edge Impulse
MobileNetV1 96x96 0.1 Officially supported Uses around 53.2K RAM and 101K ROM with default settings and optimizations. Works best with 96x96 input size. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 96x96 0.35 Officially supported Uses around 296.8K RAM and 575.2K ROM with default settings and optimizations. Works best with 96x96 input size. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 96x96 0.1 Officially supported Uses around 270.2K RAM and 212.3K ROM with default settings and optimizations. Works best with 96x96 input size. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 96x96 0.05 Officially supported Uses around 265.3K RAM and 162.4K ROM with default settings and optimizations. Works best with 96x96 input size. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 160x160 1.0 Officially supported Uses around 1.3M RAM and 2.6M ROM with default settings and optimizations. Works best with 160x160 input size. Supports RGB only.	Edge Impulse
MobileNetV2 160x160 0.75 Officially supported Uses around 1.3M RAM and 1.7M ROM with default settings and optimizations. Works best with 160x160 input size. Supports RGB only.	Edge Impulse
MobileNetV2 160x160 0.5 Officially supported Uses around 700.7K RAM and 982.4K ROM with default settings and optimizations. Works best with 160x160 input size. Supports RGB only.	Edge Impulse
MobileNetV2 160x160 0.35 Officially supported Uses around 683.3K RAM and 658.4K ROM with default settings and optimizations. Works best with 160x160 input size. Supports RGB only.	Edge Impulse
EfficientNet Community A collection of high-end image classification models, with transfer learning weights, scaling from 4 to 28M parameters. Great for complex usecases on Linux.	Edge Impulse Inc.

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 both RGB and grayscale.	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
MobileNetV1 96x96 0.25 A pre-trained multi-layer convolutional network designed to efficiently classify images. Uses around 105.9K RAM and 301.6K ROM with default settings and optimizations.	Edge Impulse
MobileNetV1 96x96 0.2 Uses around 83.1K RAM and 218.3K ROM with default settings and optimizations. Works best with 96x96 input size. Supports both RGB and grayscale.	Edge Impulse
MobileNetV1 96x96 0.1 Uses around 53.2K RAM and 101K ROM with default settings and optimizations. Works best with 96x96 input size. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 96x96 0.35 Uses around 296.8K RAM and 575.2K ROM with default settings and optimizations. Works best with 96x96 input size. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 96x96 0.1 Uses around 270.2K RAM and 212.3K ROM with default settings and optimizations. Works best with 96x96 input size. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 96x96 0.05 Uses around 265.3K RAM and 162.4K ROM with default settings and optimizations. Works best with 96x96 input size. Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 160x160 1.0 Uses around 1.3M RAM and 2.6M ROM with default settings and optimizations. Works best with 160x160 input size. Supports RGB only.	Edge Impulse
MobileNetV2 160x160 0.75 Uses around 1.3M RAM and 1.7M ROM with default settings and optimizations. Works best with 160x160 input size. Supports RGB only.	Edge Impulse
MobileNetV2 160x160 0.5 Uses around 700.7K RAM and 982.4K ROM with default settings and optimizations. Works best with 160x160 input size. Supports RGB only.	Edge Impulse
MobileNetV2 160x160 0.35 Uses around 683.3K RAM and 658.4K ROM with default settings and optimizations. Works best with 160x160 input size. Supports RGB only.	Edge Impulse
EfficientNet B0 Transfer learning model based on efficientnetb0_notop.h5 weights. This is a much larger model than MobileNet for Linux devices and accelerators.	Edge Impulse
EfficientNet A collection of high-end image classification models, with transfer learning weights, scaling from 4 to 28M parameters. Great for complex usecases on Linux.	Edge Impulse
NVIDIA TAO Image Classification Image classification model for general purpose use. Configurable backbones optimized for targets from MCU to GPU. Only supports RGB images. Pre-trained weights only support 224x224 resolution. Image width and height must be greater than 32. Training requires GPU.	Edge Impulse

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

	Name
	Wamiq Raza

Neural Network settings

Training settings

Augmentation settings

Advanced training settings

Neural network architecture

Model

Last training performance (validation set)

Accuracy

Loss

Confusion matrix (validation set)

Data explorer (full training set)

Settings

On-device performance

Inferencing time

Peak RAM usage

Flash usage

Last training performance (validation set)

Accuracy

Loss

Confusion matrix (validation set)

Data explorer (full training set)

Settings

On-device performance

Inferencing time

Peak RAM usage

Flash usage