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AP-CNN

Code release for Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification (TIP2021).

Dependencies

Python 3.6 with all of the pip install -r requirements.txt packages including:

  • torch == 0.4.1
  • opencv-python
  • visdom

Data

  1. Download the FGVC image data. Extract them to data/cars/, data/birds/ and data/airs/, respectively.
  -/cars/
     └─── car_ims
             └─── 00001.jpg
             └─── 00002.jpg
             └─── ...
     └─── cars_annos.mat

  -/birds/
     └─── images.txt
     └─── image_class_labels.txt
     └─── train_test_split.txt
     └─── images
             └─── 001.Black_footed_Albatross
                       └─── Black_Footed_Albatross_0001_796111.jpg
                       └─── ...
             └─── 002.Laysan_Albatross
             └─── ...

   -/airs/
     └─── images
             └─── 0034309.jpg
             └─── 0034958.jpg
             └─── ...
     └─── variants.txt
     └─── images_variant_trainval.txt
     └─── images_variant_test.txt
  1. Preprocess images.
  • For birds: python utils/split_dataset/birds_dataset.py
  • For cars: python utils/split_dataset/cars_dataset.py
  • For airs: python utils/split_dataset/airs_dataset.py

Training

Start:

  1. python train.py --dataset {cars,airs,birds} --model {resnet50,vgg19} [options: --visualize] to start training.
  • For example, to train ResNet50 on Stanford-Cars: python train.py --dataset cars --model resnet50
  • Run python train.py --help to see full input arguments.

Visualize:

  1. python -m visdom.server to start visdom server.

  2. Visualize online attention masks and ROIs on http://localhost:8097.

Pretrained Checkpoints

Pretrained checkpoints with following settings are available on download link, with access code "kjqu".

Dataset base model accuracy(%)
CUB-200-2011 resnet50 88.4
Stanford-Cars resnet50 95.3
FGVC-Aircraft resnet50 94.0

Citation

If you find this paper useful in your research, please consider citing:

@ARTICLE{9350209,
author={Y. {Ding} and Z. {Ma} and S. {Wen} and J. {Xie} and D. {Chang} and Z. {Si} and M. {Wu} and H. {Ling}},
journal={IEEE Transactions on Image Processing},
title={AP-CNN: Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification},
year={2021},
volume={30},
number={},
pages={2826-2836},
doi={10.1109/TIP.2021.3055617}}

Contact

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