MMAL-Net:这是论文“用于细粒度视觉分类的多分支和多尺度注意力学习（MMAL-Net）”的PyTorch实施（张凡，李萌，...

# MMAL-Net This is a PyTorch implementation of the paper ["Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization (MMAL-Net)"](https://arxiv.org/abs/2003.09150) (Fan Zhang, Meng Li, Guisheng Zhai, Yizhao Liu), and the paper has been accepted by the 27th International Conference on Multimedia Modeling (MMM2021). Welcome to discuss with us in issues!  ### Table of Contents - <a href='#requirements'>Requirements</a> - <a href='#datasets'>Datasets</a> - <a href='#training L-Net'>Training MMAL-Net</a> - <a href='#evaluation'>Evaluation</a> - <a href='#model'>Model</a> - <a href='#reference'>Reference</a> ## Requirements - python 3.7 - pytorch 1.3.1 - numpy 1.17.3 - scikit-image 0.16.2 - Tensorboard 1.15.0 - TensorboardX 2.0 - tqdm 4.41.1 - imageio 2.6.1 - pillow 6.1.0 ## Datasets Download the [CUB-200-2011](http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz) datasets and copy the contents of the extracted **images** folder into **datasets/CUB 200-2011/images**. Download the [FGVC-Aircraft](http://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/archives/fgvc-aircraft-2013b.tar.gz) datasets and copy the contents of the extracted **data/images** folder into **datasets/FGVC_Aircraft/data/images**) You can also try other fine-grained datasets. ## Training TBMSL-Net If you want to train the MMAL-Net, please download the pretrained model of [ResNet-50](https://drive.google.com/open?id=1raU0m3zA52dh5ayQc3kB-7Ddusa0lOT-) and move it to **models/pretrained** before run ``python train.py``. You may need to change the configurations in ``config.py`` if your GPU memory is not enough. The parameter ``N_list`` is ``N1, N2, N3`` in the original paper and you can adjust them according to GPU memory. During training, the log file and checkpoint file will be saved in ``model_path`` directory. ## Evaluation If you want to test the MMAL-Net, just run ``python test.py``. You need to specify the ``model_path`` in ``test.py`` to choose the checkpoint model for testing. ## Model We also provide the checkpoint model trained by ourselves, you can download if from [Google Drive](https://drive.google.com/open?id=13ANynWz7O3QK0RdL4KqASW8X_vMb6V4B) for **CUB-200-2011** or download from [here](https://drive.google.com/file/d/1-LD1Jz6Dh-P6Ibtl17scfrTFQTrW4Zy3/view?usp=sharing) for **FGVC-Aircraft**. If you test on our provided model, you will get 89.6% and 94.7% test accuracy, respectively. ## Reference If you are interested in our work and want to cite it, please acknowledge the following paper: ``` @misc{zhang2020threebranch, title={Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization}, author={Fan Zhang and Meng Li and Guisheng Zhai and Yizhao Liu}, year={2020}, eprint={2003.09150}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```