Cross-layer Navigation Convolutional Neural Network for Fine-grained Visual Classification

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.

• Stanford-Cars (cars)

  -/cars/
     └─── car_ims
             └─── 00001.jpg
             └─── 00002.jpg
             └─── ...
     └─── cars_annos.mat

• CUB-200-2011 (birds)

  -/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
             └─── ...

• FGVC-Aircraft (airs)

   -/airs/
     └─── images
             └─── 0034309.jpg
             └─── 0034958.jpg
             └─── ...
     └─── variants.txt
     └─── images_variant_trainval.txt
     └─── images_variant_test.txt

2. 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} [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 on http://localhost:8097.

Citation

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

@article{guo2021cross,
  title={Cross-layer Navigation Convolutional Neural Network for Fine-grained Visual Classification},
  author={Guo, Chenyu and Xie, Jiyang and Liang, Kongming and Sun, Xian and Ma, Zhanyu},
  journal={arXiv preprint arXiv:2106.10920},
  year={2021}
}

Contact

Thanks for your attention! If you have any suggestion or question, you can leave a message here or contact us directly:

• guochenyu@bupt.edu.cn
• mazhanyu@bupt.edu.cn