Comparing-CNN-InceptionV3-on-Intel-Image-Classification

This repo is a comparative study of performance between CNN from scratch and using transfer learning :

Data Table Summary

Properties	Details
Name	Intel Image Classificaiton
Source	Kaggle
Author	PUNEET BANSAL
Data Type	Images
Problem Type	Image Classificaiton
Files	3 Folders (14K Training , 3K Testing , 7K Prediction)
Classes	6 Classes (Buildings, Forest, Glacier, Mountain, Sea, Street)

Preprocessing Steps :

• Image Resizing : Image has been resized to (150,150)
• Image Normalisation : Normalising pixel values from 0-255 to between 0-1
• Encoding Image Labels : Transforming labels from categorical format to numerical using Label Encoder

Modelling :

In this part we will go through the different CNN version that we created from scratch and using transfer learning with different versions compare the results including fine-tuning :

• Simple CNN :
  • 1 convolutional layer (32 Units)
  • 1 MaxPooling layer
  • 2 Dense Layers ( 128 Units , 6 )
  • 10 Epochs
• Deep CNN :
  • 4 convolutional layers (2 * 32 Units + 64 Units + 128 Units)
  • 4 Maxpooling Layers
  • 3 Dense Layers (128 Units + 64 Units , 6 )
  • 10 Epochs
• Deep CNN + Fine-tuning :
  • Same previous Architecure
  • Change optimizer to Adamax
  • Added Callbacks (early stopping + LR Scheduler)
  • 20 Epochs
• InceptionV3 I :
  • Excluding Original Classification layers (include_top = False)
  • Using ImageNet Weights (weights='imagenet')
  • Unfreezing the first 25 layers (inception1.layers[-25:])
  • Adding Costume classificaiton head :
    • Dense layer (1024 units)
    • Dropout layer Dropping 20% of units (0.2)
    • Classification layer (last dense layer) (6)
  • Optimizer Adamax with starting learning rate of 0.0001
  • Callbacks (early stopping + retreive best weights)
  • 20 Epochs
• InceptionV3 II :
  • Same Previous Architecture
  • Unfreezing the last 25 layers instead (inception2.layers[25:])
  • 20 Epochs
• InceptionV3 III :
  • Using the full architecture (48 Layers)
  • Different Costume classificaiton head :
    • Batch Normalization Layer
    • 2 Dense Layers (256 Units + 128 Units)
    • 1 Classification layer (6)
  • Same Callbacks
  • 20 Epochs

Results :

I - Simple CNN :

• Training : The Simple CNN did a horrible job with evaluation of 68% Accuracy & 140% Loss

• Prediciton : The bad training explains the bad predictions done by the simple CNN failing to classify a lot of image

II - Deep CNN :

• Training : The Deep CNN did a better job than the normal CNN with evaluation of 81% Accuracy but still having errors with 82% Loss

• Prediciton : The defective training explains the wrong predictions done by the Deep CNN failing to classify some images

III - Deep CNN + Fine-tuning :

• Training : The Fine-tubned Deep CNN did a better than the previous models with evaluation of 82% Accuracy but still having errors with 49% Loss (still high loss)

• Prediciton : The improvement in the training results can be seen in the predictions done by the fine-tuned CNN

IV - InceptionV3 (ver 1) :

• Training : We can already see the supremacy of transfer learning here with evaluation of 90% Accuracy but still having errors with 27% Loss

• Prediciton : The difference cannot be really seen here , but overall it's doing way better than previous models

IV - InceptionV3 (ver 2) :

• Training : Freezing the first 25 layers insead showed difference with 91% Accuracy but still having errors with 25% Loss

• Prediciton : The difference cannot be really seen here , but overall it's doing way better than previous models

IV - InceptionV3 (ver 3) :

• Training : Using the whole model made the performance inferrior than previous versions of inception with evaluation of 89% Accuracy but having more errors with 30% Loss

• Prediciton : The difference cannot be really seen here , but overall it's doing a bit worse than the other inception versions

Comparing Model Performance :

• The Transfer learning models are doing exponentionally better than the best from-scratch CNN model ( the fine-tuned version)

Bonus :

• This is the confusion matrices of each models giving better context at the overall performances