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Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition

This repo contains the reference source code for our CVPR 2020 paper Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition.

Environment

Python 3.7

Pytorch 1.1.0 with CUDA 9.0

tensorboardX

Set up dataset

In our experiments, we use four datasets: CUB-200-2011, NABirds, FGVC-Aircraft and OID-Aircraft.

You have two options to download this data:

  • Manually download them using the hyper-links provided above, and then extract them into the dataset folder.
  • If you don't want to download and extract them one by one, you can also go into the dataset folder and execute download.sh. Before doing that, though, please go to the official NABirds website and register using your name and email address, and also accept their terms of use.

After download is finished, navigate to the dataset folder and execute init.sh to generate the dataset we use for training and evaluation. More details about dataset split can be found in the paper.

Train and test

For experiments on CUB and FGVC, each model has its own individual folder in the CUB or FGVC directory respectively.

For traininng, you just need to go to the model folder you wish to run, and execute Con4.sh or ResNet18.sh for the 4-layer ConvNet or ResNet18 backbone. The hyper-parameters have already been set to the values given in the supplementary materials. We set the default gpu device to 0. If you want to specify others, just change the --gpu argument in the .sh script.

The training and validation accuracies are displayed in both the std output and the generated *.log file. The training history, including losses, train/validation accuracy and heatmap visualization, can also be displayed via tensorboard. The tensorboard summary is located in the log_* folder. During the training process, the model snapshot with the best validation performance will be saved in model_*.pth.

After training is complete, the script will automatically evaluate the final model on the corresponding test set, and output the test accuracy numbers in both the std output and *.log file.

Train with less part annotation

For the ablation study on training on the CUB dataset with less part annotation, navigate to proto+PN_less_annot in the CUB directory. The .sh scripts have been set to training with 20% part annotation and batch size 7. If you want to train with other percentages of annotation, change both the --percent and --batch_size arguments in the scripts as described in the supplementary matrials.

Citation

If you find our code or paper useful, please consider citing our work using the following bibtex:

@inproceedings{tang2020revisiting,
  title={Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition},
  author={Tang, Luming and Wertheimer, Davis and Hariharan, Bharath},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14352--14361},
  year={2020}
}

Updates

05/27/2021: As pointed out in this issue, the website for OID-Aircraft seems to be down for now. As an alternative for downloading the dataset, I upload one copy to this google drive link. More details about the dataset could be found in its original paper. If you wanna download the dataset from google drive via command line directly, please refer to this line of code in download.sh, or you can refer to our recent FRN repository for more details.