Image-Classification-using-Caltech-UCSD-Birds-200-Dataset

Dataset

Using https://www.vision.caltech.edu/datasets/cub_200_2011/ dataset

• Dataset info
  • Number of categories: 200
  • Number of images: 11,788
  • Annotations per image: 15 Part Locations, 312 Binary Attributes, 1 Bounding Box

Main topics covered in methodology

• Modeling
  • implement a fully connected neural network (multi-layer perceptron):
    • Resize the images to be no larger than 32x32.
    • Use the sequential model API in keras to build your network using dense layers
  • ResNet-101
    • Use the pretrained ResNet-101 network available in Keras.
  • Visualization
    • t-SNE
    • filters learned by each model

Conclusion

• Using 64 * 64 as image size as a step for handle overfitting from the last step

• step_1

  • Using ResNet101 pre-trained model when trainable = False (freeze the layers of the model )
  • using flattened layer then adding the output layer with the number of classes and SoftMax as an activation function

• step_2

  • Using ResNet101 pre-trained model when trainable = False but choose 10 last layers to be unfrozeed
  • Using a flattened layer then adding the output layer with the number of classes and SoftMax as an activation function with sparse_categorical_crossentropy for the optimizer

contact

https://www.linkedin.com/in/%D9%90%D9%90alaa-elkhashap/