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
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
HR and HRV features
Officially Supported
Process PPG or ECG data into heart rate and heart rate variability features.
This block is available for testing but requires an Enterprise license to deploy.
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
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™
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
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
Visual Anomaly Detection - FOMO-AD
Enterprise
Detect visual anomalies. Extracts visual features using a pre-trained backbone, and applies a scoring function to evaluate how anomalous a sample is by comparing the extracted features to the learned model. Does not require anomalous data.
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
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
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
Officially Supported
Learns patterns from data, and can apply these to new data. Great for categorizing movement or recognizing audio.
Edge Impulse
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
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)
Professional
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
Visual Anomaly Detection - FOMO-AD
Enterprise
Detect visual anomalies. Extracts visual features using a pre-trained backbone, and applies a scoring function to evaluate how anomalous a sample is by comparing the extracted features to the learned model. Does not require anomalous data.
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
We're always looking for ways to improve Edge Impulse. If you have any feedback, please let us know!
This field is required
This field is required
Almost there!
You'll need a free Edge Impulse
account to clone this project.
Creating an account lets you add your own data,
modify models, and join a community of thousands of
embedded machine learning developers!
Configure your target device and application budget
Target device
Define your target device requirements to inform model optimizations and performance calculations.
No device yet? Use the default settings which you can change at any time.
| MHz
Max
Application budget
Specify the available RAM and ROM for the model's operation, along with the maximum allowed latency
for your specific application. Not sure yet? Start with the defaults and modify them later on.
| KB
Max
| KB
Max
| ms
Max
Choose your pricing
YEARLY
$400
/month
billed annually
SAVE 15%
MONTHLY
$475
/month
billed monthly
Additional usage
Professional Plan includes 1,000 compute minutes per month. Additional usage will be charged at $0.10 per minute, billed monthly.
