Associating Multi-Scale Receptive Fields for Fine-grained Recognition

By Zihan Ye (zihhye@outlook.com), Fuyuan Hu, Yin Liu (echoooooliu1998@gmail.com), Zhenping Xia, Fan Lyu (fanlyu@tju.edu.cn) and Pengqing Liu

Introduction

This is a PyTorch implementation for the paper "Associating Multi-Scale Receptive Fields for Fine-grained Recognition" in ICIP2020. It brings the CNL models trained on the CUB-200, Stanford-Dogs and Stanford-Cars.

Citation

If you think this code is useful in your research or wish to refer to the baseline results published in our paper, please use the following BibTeX entry.

@article{Associating2020Zihan,
    author={Zihan Ye and Fuyuan Hu and Yin Liu and Zhenping Xia and Fan Lyu and Pengqing Liu},
    title={Associating Multi-Scale Receptive Fields for Fine-grained Recognition},
    journal={ICIP},
    year={2020}
}

Requirements

• PyTorch >= 0.4.1 or 1.0 from a nightly release
• Python >= 3.5
• torchvision >= 0.2.1
• termcolor >= 1.1.0

Environment

The code is developed and tested under 1 RTX 8000 GPU cards on CentOS with installed CUDA-9.2/8.0 and cuDNN-7.1.

Baselines and Main Results on CUB-200 Dataset

Model	Best Top-1 (%)	Top-5 (%)
R-50	84.05	96.00
R-50 w/ 5NL	85.10	96.18
R-50 w/ 5CNL	85.64	96.84
R-101	85.05	96.70
R-101 w/ 5NL	85.53	96.65
R-101 w/ 5CNL	86.73	96.75

Notes:

• The input size is 448.
• Prolonging the WARMUP_ITERS appropriately would produce the better results for CNLNet models.

Getting Start

Prepare Dataset

• Download pytorch imagenet pretrained models from pytorch model zoo. The optional download links can be found in torchvision. Put them in the pretrained folder.

• Download the training and validation lists for CUB-200 dataset from Baidu Pan(Password: b8r6).

• Download the training and validation lists for Stanford_Car dataset from Baidu Pan(Password: lrtz).

• Download the training and validation lists for Stanford_Dog dataset from Baidu Pan(Password: ih96). Put them in the data folder and make them look like:

  ${THIS REPO ROOT}
   `-- pretrained
       |-- resnet50-19c8e357.pth
       |-- resnet101-5d3b4d8f.pth
   `-- data
       `-- cub
           `-- images
           |   |-- 001.Black_footed_Albatross
           |   |-- 002.Laysan_Albatross
           |   |-- ...
           |   |-- 200.Common_Yellowthroat
           |-- cub_train.list
           |-- cub_val.list
           |-- images.txt
           |-- image_class_labels.txt
           |-- README
       `-- Stanford_Car
           `-- images
           |-- cars_train.list
           |-- cars_test.list
           |-- ...
       `-- Stanford_Dog
           `-- Images
           |-- dogs_train.list
           |-- dogs_test.list
           |-- ...
  

Perform Validating

$ python train_val.py --arch '50' --dataset 'cub' --checkpoints ${FOLDER_DIR} --valid

Perform Training ResNet with Cross-layer Non-Local module

$ python train_val.py --arch '50' --dataset 'cub' --warmup

License

This code is released under the MIT License. See LICENSE for additional details.