/Analysis-Optimization-Single-Object-Image-Classification-Model-Tensorflow-2

Provide Single Object Image Classification Model Accuracy Optimization Solution using tensorflow 2.

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How To Optimize Single Object Image Classification Model Accuracy using Tensorflow 2

Last updated: 01/12/2021

Table : Comparative Analysis of Classification Accuracy

activation function loss function Optimizer learning rate learning rate dacay epoch total loss learning time final accuracy
Relu MSE Gradient descent 0.01 every 50, *=0.96 100 0.001 2h 98%

Table : Procedure

index 1 2 3
1 layer normalization activation function
2 normalization activation function convolution layer



Table : Data Augmentation

Method notation code
Random erase
Cutout
MixUp
CutMix
Style transfer GAN
Mosaic
Random Croping tf.keras.layers.experimental.preprocessing.RandomCrop()
tf.image.random_crop()

Table : Loss Functin and Optimizer Equation

activation function activation equation loss function loss equation Optimizer Optimizer notation
Sigmoid
Relu MSE Gradient descent
Leacky Relu
ELU
tanh
maxout

Table : Weight Initialization

Weight Initialization notation code
Random Normal tf.keras.initializers.RandomNormal()
Xavier (=Glorot) tf.keras.glorot_uniform()
He (for Relu) tf.keras.initializers.he_uniform()

Table : Regularization

Regularization notation code
Dropout Random Node Turn off tf.keras.layers.Dropout(rate) (0.0<rate<1.0)
GaussianDropout sqrt(rate / (1 - rate)) tf.keras.layers.GaussianDropout(rate)
DropBlock (for CNN) drop range of features
Spatial Dropout tf.keras.layers.SpatialDropout2D()
L1 Regularization
L2 Regularization
Early stoppoing stop epoch training tf.keras.callbacks.EarlyStopping()

Table : Normalization - Internal Covariate Shift Solution

Method notation code
Batch Normalization(BN) [Not use Dropout] tf.keras.layers.BatchNormalization()
Layer Normalization(LN) tf.keras.layers.LayerNormalization()
Instance Normalization(IN)
Group Normalization(GN) [for small batch sizes]
Switchable Normalization(SN) [for small batch sizes]