Logo
MetaTF 2.3.0
  • Overview
  • Installation
    • Requirements
    • Quick installation
    • Running examples
  • User guide
    • Getting started
      • For beginners
      • For users familiar with deep-learning
    • Akida user guide
      • Introduction
        • Akida layers
        • Input Format
        • A versatile machine learning framework
      • The Sequential model
        • Specifying the model
        • Accessing layer parameters and weights
        • Inference
        • Saving and loading
        • Input layer types
        • Data-Processing layer types
      • Model Hardware Mapping
        • Devices
        • Model mapping
        • Advanced Mapping Details and Hardware Devices Usage
        • Performances measurement
      • Using Akida Edge learning
        • Learning constraints
        • Compiling a layer
    • CNN2SNN toolkit
      • Overview
        • Conversion workflow
        • Typical training scenario
        • Design compatibility constraints
        • Quantization compatibility constraints
        • Command-line interface
      • Layers Considerations
        • Supported layer types
        • CNN2SNN Quantization-aware layers
        • Training-Only Layers
        • First Layers
        • Final Layers
      • Tips and Tricks
    • Akida models zoo
      • Overview
      • Command-line interface for model creation
      • Command-line interface for model training
        • UTK Face training
        • KWS training
        • YOLO training
        • AkidaNet training
      • Command-line interface for model evaluation
      • Command-line interface to evaluate model MACS
      • Layer Blocks
        • conv_block
        • dense_block
        • separable_conv_block
    • Hardware constraints
      • InputConvolutional
      • Convolutional
      • SeparableConvolutional
      • FullyConnected
    • Akida versions compatibility
      • Upgrading models with legacy quantizers
  • API reference
    • Akida runtime
      • Model
      • Layer
        • Layer
        • Mapping
      • InputData
      • InputConvolutional
      • FullyConnected
      • Convolutional
      • SeparableConvolutional
      • Layer parameters
        • LayerType
        • Padding
        • PoolType
      • Optimizers
      • Sequence
        • Sequence
        • BackendType
        • Pass
      • Device
        • Device
        • HwVersion
      • HWDevice
        • HWDevice
        • SocDriver
        • ClockMode
      • PowerMeter
      • NP
      • Tools
        • Sparsity
        • Compatibility
    • CNN2SNN
      • Tool functions
        • quantize
        • quantize_layer
        • convert
        • check_model_compatibility
        • load_quantized_model
        • Transforms
        • Calibration
      • Quantizers
        • WeightQuantizer
        • LinearWeightQuantizer
        • StdWeightQuantizer
        • StdPerAxisQuantizer
        • MaxQuantizer
        • MaxPerAxisQuantizer
      • Quantized layers
        • QuantizedConv2D
        • QuantizedDense
        • QuantizedSeparableConv2D
        • QuantizedActivation
        • ActivationDiscreteRelu
        • QuantizedReLU
    • Akida models
      • Layer blocks
        • conv_block
        • separable_conv_block
        • dense_block
      • Helpers
        • BatchNormalization gamma constraint
      • Knowledge distillation
      • Pruning
      • Training
      • MACS
      • Utils
      • Model zoo
        • AkidaNet
        • Mobilenet
        • DS-CNN
        • VGG
        • YOLO
        • ConvTiny
        • PointNet++
        • GXNOR
  • Examples
    • General examples
    • CNN2SNN tutorials
    • Edge examples
      • General examples
        • GXNOR/MNIST inference
        • AkidaNet/ImageNet inference
        • DS-CNN/KWS inference
        • Regression tutorial
        • Transfer learning with AkidaNet for PlantVillage
        • YOLO/PASCAL-VOC detection tutorial
      • CNN2SNN tutorials
        • CNN conversion flow tutorial
        • Advanced CNN2SNN tutorial
      • Edge examples
        • Akida vision edge learning
        • Akida edge learning for keyword spotting
        • Tips to set Akida learning parameters
  • Model zoo performances
    • Image domain
      • Classification
      • Object detection
      • Regression
      • Face recognition
    • Audio domain
      • Keyword spotting
    • Time domain
      • Fault detection
      • Classification
    • Point cloud
      • Classification
  • Changelog
  • Support
  • License
Akida Examples
  • »
  • Search

© Copyright 2022, BrainChip Holdings Ltd. All Rights Reserved.