Class EfficientNet<T>

Namespace

AiDotNet.ComputerVision.Detection.Backbones

Assembly

AiDotNet.dll

EfficientNet backbone for efficient feature extraction.

public class EfficientNet<T> : BackboneBase<T>

Type Parameters

T

The numeric type used for calculations.

Inheritance

object

BackboneBase<T>

EfficientNet<T>

Inherited Members

BackboneBase<T>.NumOps

BackboneBase<T>.IsTrainingMode

BackboneBase<T>.IsFrozen

BackboneBase<T>.SetTrainingMode(bool)

BackboneBase<T>.Freeze()

BackboneBase<T>.Unfreeze()

BackboneBase<T>.GetExpectedInputSize()

BackboneBase<T>.ValidateInput(Tensor<T>)

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Remarks

For Beginners: EfficientNet is a family of models that were designed using neural architecture search to find the optimal balance between width, depth, and resolution. It achieves state-of-the-art accuracy with significantly fewer parameters than other architectures.

Key features: - MBConv (Mobile Inverted Bottleneck) blocks - Squeeze-and-Excitation for channel attention - Compound scaling for width, depth, and resolution

Reference: Tan et al., "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks", ICML 2019

Constructors

EfficientNet(EfficientNetVariant, int)

Creates a new EfficientNet backbone.

public EfficientNet(EfficientNetVariant variant = EfficientNetVariant.B0, int inChannels = 3)

Parameters

variant EfficientNetVariant

EfficientNet variant (B0-B7).

inChannels int

Number of input channels (default 3 for RGB).

Properties

Name

Name of this backbone architecture.

public override string Name { get; }

Property Value

string

OutputChannels

Number of output channels for each feature level.

public override int[] OutputChannels { get; }

Property Value

int[]

Remarks

Modern detectors use multi-scale features. This array contains the number of channels at each scale, typically from high resolution (small objects) to low resolution (large objects).

Strides

The stride (downsampling factor) at each feature level.

public override int[] Strides { get; }

Property Value

int[]

Remarks

A stride of 8 means the feature map is 1/8 the size of the input. Common strides are [8, 16, 32] for 3-level feature pyramids.

Methods

ExtractFeatures(Tensor<T>)

Extracts multi-scale features from an input image tensor.

public override List<Tensor<T>> ExtractFeatures(Tensor<T> input)

Parameters

input Tensor<T>

Input image tensor with shape [batch, channels, height, width].

Returns

List<Tensor<T>>

List of feature maps at different scales, from highest to lowest resolution.

Remarks

For Beginners: This method runs the input image through the backbone and returns feature maps at multiple scales. Small objects need high-resolution features, while large objects are detected in low-resolution features.

GetParameterCount()

Gets the total number of parameters in the backbone.

public override long GetParameterCount()

Returns

long

Number of trainable parameters.

ReadParameters(BinaryReader)

Reads parameters from a binary reader for deserialization.

public override void ReadParameters(BinaryReader reader)

Parameters

reader BinaryReader

The binary reader to read from.

WriteParameters(BinaryWriter)

Writes all parameters to a binary writer for serialization.

public override void WriteParameters(BinaryWriter writer)

Parameters

writer BinaryWriter

The binary writer to write to.