Preprocess and normalize image data, and optionally reduce the color depth.
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
Preprocess and normalize image data, and optionally reduce the color depth.
Edge Impulse
Flatten
Flatten an axis into a single value, useful for slow-moving averages like temperature data, in combination with other blocks.
Edge Impulse
Audio (MFCC)
Extracts features from audio signals using Mel Frequency Cepstral Coefficients, great for human voice.
Edge Impulse
Audio (MFE)
Extracts a spectrogram from audio signals using Mel-filterbank energy features, great for non-voice audio.
Edge Impulse
Spectral Analysis
Great for analyzing repetitive motion, such as data from accelerometers. Extracts the frequency and power characteristics of a signal over time.
Edge Impulse
Spectrogram
Extracts a spectrogram from audio or sensor data, great for non-voice audio or data with continuous frequencies.
Edge Impulse
Audio (Syntiant)
Syntiant only. Compute log Mel-filterbank energy features from an audio signal.
Syntiant
IMU (Syntiant)
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
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
Some processing blocks have been hidden based on the data in your project.
Show all blocks anyway
Fine tune a pre-trained object detection model on your data. Good performance even with relatively small image datasets.
Edge Impulse
Object Detection (Images) - BrainChip Akida™
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
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
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
FOMO-AD (Images)
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
Object Detection (Images)
Fine tune a pre-trained object detection model on your data. Good performance even with relatively small image datasets.
Edge Impulse
Object Detection (Images) - BrainChip Akida™
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
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
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
Classification
Learns patterns from data, and can apply these to new data. Great for categorizing movement or recognizing audio.
Edge Impulse
Transfer Learning (Images)
Fine tune a pre-trained image classification model on your data. Good performance even with relatively small image datasets.
Edge Impulse
Regression
Learns patterns from data, and can apply these to new data. Great for predicting numeric continuous values.
Edge Impulse
Transfer Learning (Keyword Spotting)
Fine tune a pre-trained keyword spotting model on your data. Good performance even with relatively small keyword datasets.
Edge Impulse
Anomaly Detection (GMM)
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)
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™
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™
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)
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
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
Motion Recognition
Enterprise
Motion Recognition demo (time-series data)
Brainchip
Some learning blocks have been hidden based on the data in your project.
Show all blocks anyway
To use an Input block version, connect it to a DSP block version using the
Manage versions dialog on a DSP block page. The starred Input
block version is the one connected to the Primary learn block, via a DSP block.
Note
To use a DSP block version, connect it to a Learn block version using the
Manage versions dialog on a Learn block page. The starred DSP
block version is the one connected to the Primary learn block.
Note
The Primary learn block version is the one that will be used when a project is
deployed. When you set a Learn block version to Primary, any DSP and Input block
versions it connects to will automatically be selected for deployment.
We're always looking for ways to improve Edge Impulse. If you have any feedback, please let us know!
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