radar_outdoor_moving_object_test - 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 Processes discrete images for object detection or classification.
Time series data 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 Preprocess and normalize image data, and optionally reduce the color depth.	Edge Impulse
Raw Data Use data without pre-processing. Useful if you want to use deep learning to learn features.	Edge Impulse
Edge Detection Edge Detection for images	Demo Team
Pose estimation Pose estimation for images	Demo Team
FOMO grid Does the same as the Image processing block + adds an extra graph with a grid overlay to help you making sure your objects can fit in the grids	Demo Team
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
Edge Detection Edge Detection for images	Demo Team
Pose estimation Pose estimation for images	Demo Team
FOMO grid Does the same as the Image processing block + adds an extra graph with a grid overlay to help you making sure your objects can fit in the grids	Demo Team

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

Description	Author	Recommended
Transfer Learning (Images) Fine tune a pre-trained image classification model on your data. Good performance even with relatively small image datasets.	Edge Impulse
Yolov5 Yolov5 demo block	Demo Team
EfficientNet a Keras implementation of transfer learning with EfficientNet B0	Demo Team
Akidanet 224 Transfer Learn 224x224 TF for IC	Demo Team
Test GPU Keras Image Using GPU for Image training	Demo Team
EfficientNet B0 Transfer learning model based on efficientnetb0_notop.h5 weights. This is a much larger model than MobileNet for Linux devices and accelerators.	Community blocks
Classification Learns patterns from data, and can apply these to new data. Great for categorizing movement or recognizing audio.	Edge Impulse
Regression Learns patterns from data, and can apply these to new data. Great for predicting numeric continuous values.	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
Transfer Learning (Images) Fine tune a pre-trained image classification model on your data. Good performance even with relatively small image datasets.	Edge Impulse
Yolov5 Yolov5 demo block	Demo Team
EfficientNet a Keras implementation of transfer learning with EfficientNet B0	Demo Team
Akidanet 224 Transfer Learn 224x224 TF for IC	Demo Team
Test GPU Keras Image Using GPU for Image training	Demo Team
EfficientNet B0 Transfer learning model based on efficientnetb0_notop.h5 weights. This is a much larger model than MobileNet for Linux devices and accelerators.	Community blocks
Classification Learns patterns from data, and can apply these to new data. Great for categorizing movement or recognizing audio.	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
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) 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) 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
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 for Renesas DRP-AI Transfer learning model using YOLOv5 v5 branch with yolov5s.pt weights. This block is only compatible with Renesas DRP-AI.	Renesas
YOLOv5 Transfer learning model based on Ultralytics YOLOv5 using yolov5n.pt weights, supports RGB input at any resolution (square images only).	Community blocks
YOLOX for TI TDA4VM TI's EDGEAI YOLOX. https://github.com/TexasInstruments/edgeai-yolox. Outputs ONNX v7 model format both with and without final detect layers using PyTorch 1.7.1. See the implementation https://github.com/edgeimpulse/example-custom-ml-block-ti-yolox/tree/onnx-v7	Texas Instruments

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Input axes

This block does not have any other settings

Image width

Image height

Resize mode

Resize method

For optimal accuracy with transfer learning blocks, use a 96x96 or 160x160 image size.

Window size

Window increase

Frequency (Hz)

Invalid frequency

Zero-pad data

Keras

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Input features

Output features

This block only works with an Audio (MFE) block with default parameters and a window size of 1000ms at 16000Hz, or with an Audio (Syntiant) block with default parameters and a window size of 968ms at 16000Hz.

Title

Custom Block

Keras

Output features

No data collected yet

Description