Execute models

• Give permissions to script

chmod u+x script_name.sh

• Execute script

./script_name.sh

Algorithm

1. Define the Coarse Classifier (CC) and the Fine Classifier (FC)

CC consists of:

• ResNet Block: First two ConvBlocks of ResNet-50 (Input layer - "conv2_block3_out")
  • Input: Image (32x32x3)
  • Output: RO1 feature matrix
• Attention Block: Create heatmap of the images
  • Input: Output RO1 (8x8x256)
  • Output: Input RI1 (8x8x256)
• Prediction Layer:
  • Input: Output RO1 (8x8x256)
  • Output: 20 softmax units (Coarse labels)

FC consists of:

• ResNet Block: Third to Fifth ConvBlocks of ResNet-50 ("conv3_block1_1_conv" - "conv5_block3_out")
  • Input: RI1 + 20 coarse prediction labels
  • Output: RO2 feature matrix
• Prediction Bloack:
  • Input: RO2
  • Output: 100 softmax units (Fine labels)

2. Train CC

3. Freeze CC, Train FC

4. Fine-tune CC + FC together

ATPRO_HCNN

Implementation of two new versions of Hierarchical convolutional neural networks. These two versions have as upgrades: Implementation of hard attention and Uncertainty Estimation.

This implementatios were built on top of the architechture HD-CNN implemented by Zhicheng Yan et. al 2015 (https://arxiv.org/abs/1410.0736)