Image Classification - Deck of Cards - BrainChip Akida - Edge Learning - Create impulse

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An impulse takes raw data, uses signal processing to extract features, and then uses a learning block to classify new data.

Description	Recommended
Images Officially Supported Processes discrete images for object detection or classification.
Time series data Officially Supported Operates on time series sensor data like vibration or audio data. Lets you slice up data into windows.

Did you know? You can bring your own DSP code.

Description	Author	Recommended
Image Officially Supported Preprocess and normalize image data, and optionally reduce the color depth.	Edge Impulse
Raw Data Officially Supported Use data without pre-processing. Useful if you want to use deep learning to learn features.	Edge Impulse
MFCC - Normalized Enterprise Extracts features from audio signals using Mel Frequency Cepstral Coefficients, great for human voice.	Brainchip
Spectral Absolute Values Enterprise Great for analyzing repetitive motion, such as data from accelerometers. Extracts the frequency and power characteristics of a signal over time.	Brainchip
Image Officially Supported Preprocess and normalize image data, and optionally reduce the color depth.	Edge Impulse
Flatten Officially Supported Flatten an axis into a single value, useful for slow-moving averages like temperature data, in combination with other blocks.	Edge Impulse
Audio (MFCC) Officially Supported Extracts features from audio signals using Mel Frequency Cepstral Coefficients, great for human voice.	Edge Impulse
Audio (MFE) Officially Supported Extracts a spectrogram from audio signals using Mel-filterbank energy features, great for non-voice audio.	Edge Impulse
Spectral Analysis Officially Supported Great for analyzing repetitive motion, such as data from accelerometers. Extracts the frequency and power characteristics of a signal over time.	Edge Impulse
Spectrogram Officially Supported Extracts a spectrogram from audio or sensor data, great for non-voice audio or data with continuous frequencies.	Edge Impulse
Audio (Syntiant) Officially Supported Syntiant only. Compute log Mel-filterbank energy features from an audio signal.	Syntiant
IMU (Syntiant) Officially Supported Syntiant only. Great for analyzing repetitive motion, such as data from accelerometers. Extracts the frequency and power characteristics of a signal over time.	Syntiant
Raw Data Officially Supported Use data without pre-processing. Useful if you want to use deep learning to learn features.	Edge Impulse
MFCC - Normalized Enterprise Extracts features from audio signals using Mel Frequency Cepstral Coefficients, great for human voice.	Brainchip
Spectral Absolute Values Enterprise Great for analyzing repetitive motion, such as data from accelerometers. Extracts the frequency and power characteristics of a signal over time.	Brainchip

Did you know? You can bring your own model in PyTorch, Keras or scikit-learn.

Description	Author	Recommended
Transfer Learning (Images) Officially Supported Fine tune a pre-trained image classification model on your data. Good performance even with relatively small image datasets.	Edge Impulse
AkidaNet 160x160 0.5 Enterprise Uses Brainchip's AkidaNet for spiking neuromrophic processing. Works best with 160x160 size. Supports RGB	Brainchip
Akidanet 224 Transfer Learning Enterprise 224x224 Trasnfer Learning for Image Classification	Brainchip
Classification Officially Supported Learns patterns from data, and can apply these to new data. Great for categorizing movement or recognizing audio.	Edge Impulse
Regression Officially Supported Learns patterns from data, and can apply these to new data. Great for predicting numeric continuous values.	Edge Impulse
Classification - BrainChip Akida™ Officially Supported Learns patterns from data, and can apply these to new data. Great for categorizing movement or recognizing audio. Only works with BrainChip AKD1000 MINI PCIe board.	BrainChip
Transfer Learning (Images) - BrainChip Akida™ Officially Supported Fine tune a pre-trained image classification model on your data. Good performance even with relatively small image datasets. Only works with BrainChip AKD1000 MINI PCIe board.	BrainChip
FOMO-AD (Images) Enterprise Visual anomaly detection. Find outliers in new data. Extracts visual features using a pre-trained model on your data, and a Gaussian mixture model (GMM) models the shape of the features using a probability distribution. New data that is unlikely according to this model can be considered anomalous.	Edge Impulse
Motion Recognition Enterprise Motion Recognition demo (time-series data)	Brainchip
Transfer Learning (Images) Officially Supported Fine tune a pre-trained image classification model on your data. Good performance even with relatively small image datasets.	Edge Impulse
AkidaNet 160x160 0.5 Enterprise Uses Brainchip's AkidaNet for spiking neuromrophic processing. Works best with 160x160 size. Supports RGB	Brainchip
Akidanet 224 Transfer Learning Enterprise 224x224 Trasnfer Learning for Image Classification	Brainchip
Classification Officially Supported Learns patterns from data, and can apply these to new data. Great for categorizing movement or recognizing audio.	Edge Impulse
Object Detection (Images) Officially Supported Fine tune a pre-trained object detection model on your data. Good performance even with relatively small image datasets.	Edge Impulse
Regression Officially Supported Learns patterns from data, and can apply these to new data. Great for predicting numeric continuous values.	Edge Impulse
Transfer Learning (Keyword Spotting) Officially Supported Fine tune a pre-trained keyword spotting model on your data. Good performance even with relatively small keyword datasets.	Edge Impulse
Anomaly Detection (GMM) Enterprise Find outliers in new data. A Gaussian mixture model (GMM) models the shape of data using a probability distribution. New data that is unlikely according to this model can be considered anomalous.	Edge Impulse
Anomaly Detection (K-means) Officially Supported Find outliers in new data. Good for recognizing unknown states, and to complement classifiers. Works best with low dimensionality features like the output of the spectral features block.	Edge Impulse
Classification - BrainChip Akida™ Officially Supported Learns patterns from data, and can apply these to new data. Great for categorizing movement or recognizing audio. Only works with BrainChip AKD1000 MINI PCIe board.	BrainChip
Transfer Learning (Images) - BrainChip Akida™ Officially Supported Fine tune a pre-trained image classification model on your data. Good performance even with relatively small image datasets. Only works with BrainChip AKD1000 MINI PCIe board.	BrainChip
Object Detection (Images) - BrainChip Akida™ Officially Supported Fine tune a pre-trained object detection model on your data. Good performance even with relatively small image datasets. Only works with BrainChip AKD1000 MINI PCIe board.	BrainChip
FOMO-AD (Images) Enterprise Visual anomaly detection. Find outliers in new data. Extracts visual features using a pre-trained model on your data, and a Gaussian mixture model (GMM) models the shape of the features using a probability distribution. New data that is unlikely according to this model can be considered anomalous.	Edge Impulse
yolov5 Enterprise does yolov5 object detection	Brainchip
FOMO Akidanet Enterprise FOMO using Akidanet	Brainchip
FOMO Akida Mobilenet Enterprise FOMO, but with Mobilnet base from Akida libraries	Brainchip
Motion Recognition Enterprise Motion Recognition demo (time-series data)	Brainchip
yolov2-josh Enterprise josh-yolov2	Brainchip
Akida YOLOv2 Pretrained Enterprise Block just delivers pretrained YOLOv2 model from BrainChip, without doing actual training or retraining	Brainchip

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Classification - BrainChip Akida™

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Output features

53 (10C, 10D, 10H, 10S, 2C, 2D, 2H, 2S, 3C, 3D, 3H, 3S, 4C, 4D, 4H, 4S, 5C, 5D, ...

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Output features

53 (10C, 10D, 10H, 10S, 2C, 2D, 2H, 2S, 3C, 3D, 3H, 3S, 4C, 4D, 4H, 4S, 5C, 5D, 5H, 5S, 6C, 6D, 6H, 6S, 7C, 7D ...

Show all features

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Classification - BrainChip Akida™

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Description