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Tuning different Pytorch/Tensorflow pre-trained models like ResNet50 , Wide ResNet_50.2 , VGG16 and a custom CNN model to classify a dog image among 120 breeds

Jupyter Notebook 92.46% Python 7.54%
deep-learning computer-vision neural-networks cnn-classification pytorch stanford-dogs-dataset tensorflow2

dog-breed-classifier's Introduction

Dog Breed Classifier

I tuned different Pytorch/Tensorflow pre trained models like ResNet50 , Wide ResNet_50.2 , VGG16 and a custom CNN model to classify a dog image among 120 breeds. The training dataset can be collected from here.

Results :

Wide ResNet_50.2 :

Test Accuracy : 82.88978494623656
Test Loss : 0.6641809101546964

  • Loss :

    wide_resnet50_loss
  • Accuracy :

    wide_resnet50_accuracy

ResNet50 :

Test Accuracy : 80.26881720430107 / 0.9375 (Tensorflow) Test Loss : 0.6438697055783323

  • Loss :

    resnet_50_loss

  • Accuracy :

    resnet_50_accuracy

VGG16 :

Test Accuracy : 80.8736559139785
Test Loss : 0.6329619951385964

  • Loss :

    vgg16_loss

  • Accuracy :

    vgg16_accuracy

custom_CNN :

Test Accuracy : 0.1548913043478262 / 0.6875 (Tensorflow) Test Loss : 4.78740421585414

  • Loss :

    custom_CNN_loss
  • Accuracy :

    custom_CNN_accuracy

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