Skip to content

Latest commit

 

History

History
15 lines (13 loc) · 892 Bytes

README.md

File metadata and controls

15 lines (13 loc) · 892 Bytes

Image-Classifier-VGG19bn

Creating an Image Classifier using transfer learning as a part of the Deep Learning Course. A pretained 19 layer VGG model with Batch Normalization is used. The classifier is modified and the feature layer is kept frozen.

Dataset

The data set consists of 3726 images divided among 8 classes with slight variations in number. All images are of different resolutions and PIL library is used in resizing them to 3 x 224 x 224 as it is the preferred input dim for the vgg modles. The prediciton set consists of 160 images in total. The train test data split is in random with a ratio of 80:20.

Performance stats

Using vgg19 with batch normalization resulted in a prediciton score of 92.5%.
The corresponding model has the below stats,

  • Train Loss: 0.0012
  • Train Acc: 97.58
  • Validation Loss: 0.006552678989820762
  • Validation Acc: 88.73994638069705