Adiuvo_BrainChip_VWW - Transfer learning

Edge Impulse Experts / Adiuvo_BrainChip_VWW Public

Target: BrainChip AKD1000 MINI PCIe Board Clone this project

Transfer Learning (Images) - BrainChip Akida™ settings

Training settings

Number of training cycles

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Learning rate

Please provide a valid number for the learning rate (between 0 and 1)

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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Input layer (27,648 features)

adam_bc / akidanet96

Output layer (2 classes)

Model

Model version:

Last training performance (validation set)

Accuracy

81.1%

Loss

6.11

Confusion matrix (validation set)

Data explorer (full training set)

Akida Performance

Retrain this model to reveal additional performance metrics

This model is not supported for your current target.

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
AkidaNet (alpha=0.25) @160x160x3 Officially supported A pre-trained multi-layer convolutional network designed to efficiently classify images.	BrainChip
AkidaNet (alpha=0.50) @160x160x3 Officially supported A pre-trained multi-layer convolutional network designed to efficiently classify images.	BrainChip
AkidaNet (alpha=1.00) @160x160x3 Officially supported A pre-trained multi-layer convolutional network designed to efficiently classify images.	BrainChip
AkidaNet (alpha=0.25) @224x224x3 Officially supported A pre-trained multi-layer convolutional network designed to efficiently classify images.	BrainChip
AkidaNet (alpha=0.50) @224x224x3 Officially supported A pre-trained multi-layer convolutional network designed to efficiently classify images.	BrainChip
AkidaNet (alpha=1.00) @224x224x3 Officially supported A pre-trained multi-layer convolutional network designed to efficiently classify images.	BrainChip
EfficientNet B0 Community Transfer learning model based on efficientnetb0_notop.h5 weights. This is a much larger model than MobileNet for Linux devices and accelerators.	Community blocks
akidanet96 Community vkwakidanet96	adam_bc
NVIDIA TAO Image Classification Professional Enterprise 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 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 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
AkidaNet (alpha=0.25) @160x160x3 A pre-trained multi-layer convolutional network designed to efficiently classify images.	Edge Impulse
AkidaNet (alpha=0.50) @160x160x3 A pre-trained multi-layer convolutional network designed to efficiently classify images.	Edge Impulse
AkidaNet (alpha=1.00) @160x160x3 A pre-trained multi-layer convolutional network designed to efficiently classify images.	Edge Impulse
AkidaNet (alpha=0.25) @224x224x3 A pre-trained multi-layer convolutional network designed to efficiently classify images.	Edge Impulse
AkidaNet (alpha=0.50) @224x224x3 A pre-trained multi-layer convolutional network designed to efficiently classify images.	Edge Impulse
AkidaNet (alpha=1.00) @224x224x3 A pre-trained multi-layer convolutional network designed to efficiently classify images.	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
akidanet96 vkwakidanet96	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
	Joshua Buck Edge Impulse staff

Transfer Learning (Images) - BrainChip Akida™ 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

Akida Performance

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