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Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data

The source code of the ECCV 2018 paper: Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data. Paper available at: https://arxiv.org/abs/1807.10916

The code is used to train a MetaFGNet with L-Bird Subset and CUB-200-2011 dataset as the source and target dataset respectively. The extention to other source and target datasets is direct.

It concludes five parts:

  1. L_Bird_pretrain: Train a model for the classification task of L-Bird Subset based on the model that pre-trained on the ImageNet.
  2. MetaFGNet_without_Sample_Selection: Train the MetaFGNet without sample selection of L_Bird Subset
  3. Sample_Selection: Select the target-related samples from L_Bird Subset
  4. MetaFGNet_with_Sample_Selection: Train the MetaFGNet with sample selection of L_Bird Subset
  5. Fine_tune_for_final_results: Fine-tune the MetaFGNet model on the target dataset for better and final result.

We split the whole program into five parts for better understanding and reuse.

  • The 'regularized meta-learning objective' is implemented in the second part and the fourth part.
  • The proposed sample selection method is implemented in the third part.

We also provide some intermediate results for quickly implementation and verification. They can be downloaded from:

This code is completed with the cooperation of Hui Tang

We provide a supplementary material to demonstrate how to evaluate the gradient of meta-learning loss at Gradient for Meta-Learning Loss

If you have any questions, feel free to contact me at: zhang.yabin@mail.scut.edu.cn.

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The source code of the ECCV 2018 paper: Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data

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