Almost there!
DSP Block "Image" has no generated features
Layer type | |
---|---|
Dense
Fully connected layer, the simplest form of a neural network layer.
Use this for processed data, such as the output of a spectral analysis DSP block.
|
|
1D Convolution / pooling
Learn features that take spatial information into account along a single dimension.
Use this for raw data, or for DSP blocks that output spatial data, such as the MFCC block.
|
|
2D Convolution / pooling
Learn features that take spatial information into account along two dimensions.
Use this for raw data, or for DSP blocks that output spatial data, such as the MFCC block.
|
|
Reshape
Turn one-dimensional data from a DSP block into multi-dimensional data.
Use this as an input to a convolutional layer.
Use this for deep learning on raw data, or to process MFCC output.
|
|
Flatten
Flatten multi-dimensional data into a single dimension.
You need to flatten data from a convolutional layer before returning.
|
|
Dropout
Reduce the risk of a model overfitting your dataset by randomly cutting a fraction
of network connections during training. Can be helpful if your model's training performance
is better than its validation performance.
|
Model | Author | |
---|---|---|
LGBM Random Forest Classifier
|
Edge Impulse Inc. | |
XGBoost Random Forest Classifier
|
Edge Impulse Inc. | |
SVM Classifier
|
Edge Impulse Inc. | |
Random Forest Classifier
|
Edge Impulse Inc. | |
Logistic Regression
|
Edge Impulse Inc. | |
Ridge Classifier
|
Edge Impulse Inc. | |
RidgeCV Classifier
|
Edge Impulse Inc. |
Description | Author | |
---|---|---|
EfficientNet V2B0
Uses around 755K RAM based on your input size, and between 240-1675K ROM depending on the number of layers, with default compiler settings. Supports both RGB and grayscale.
|
Edge Impulse |
|
MobileNetV2 0.35
Uses around 636K 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 643K 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 1263K 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 1273K 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 622K RAM based on your input size, and 58K ROM, with default compiler settings. Supports both RGB and grayscale.
|
Edge Impulse |
|
Description | Author | Recommended | |
---|---|---|---|
LGBM Random Forest Classifier
The LightGBM Random Forest Classifier is an efficient, gradient-boosting framework that builds multiple decision trees in parallel for robust and scalable classification tasks.
|
Edge Impulse |
|
|
XGBoost Random Forest Classifier
The XGBoost Random Forest Classifier leverages the strengths of both gradient boosting and random forest methodologies to provide a powerful, efficient, and scalable solution for classification tasks.
|
Edge Impulse |
|
|
SVM Classifier
The scikit-learn SVM classifier is a powerful algorithm that constructs a hyperplane or set of hyperplanes in a high-dimensional space for classification, effectively handling both linear and non-linear data.
|
Edge Impulse |
|
|
Random Forest Classifier
The scikit-learn Random Forest Classifier is a versatile machine learning algorithm that builds multiple decision trees and merges their predictions for robust and accurate classification.
|
Edge Impulse |
|
|
Logistic Regression
The scikit-learn Logistic Regressor is a linear model for classification that estimates probabilities using a logistic function, effectively handling binary and multiclass classification tasks.
|
Edge Impulse |
|
|
Ridge Classifier
The scikit-learn Ridge classifier employs L2 regularization to optimize a linear classification model, mitigating overfitting by penalizing large coefficients, thereby enhancing model generalizability across diverse datasets.
|
Edge Impulse |
|
|
RidgeCV Classifier
The scikit-learn RidgeCV classifier is a linear regression model with built-in cross-validation that applies ridge regularization to prevent overfitting and improve prediction accuracy.
|
Edge Impulse |
|