Official PyTorch implementation of our PR 2024 paper AMMD: Attentive maximum mean discrepancy for few-shot image classification
If you use this code or find this work useful for your research, please cite:
@article{WU2024110680,
title = {AMMD: Attentive maximum mean discrepancy for few-shot image classification},
journal = {Pattern Recognition},
pages = {110680},
year = {2024},
issn = {0031-3203},
doi = {https://doi.org/10.1016/j.patcog.2024.110680},
url = {https://www.sciencedirect.com/science/article/pii/S003132032400431X},
author = {Ji Wu and Shipeng Wang and Jian Sun},
}
The following packages are required to run the scripts:
Some comparing methods may require additional packages to run (e.g, OpenCV in DeepEMD and qpth, cvxpy in MetaOptNet).
The dataset should be placed in dir "./data/dataset_name" with the same format.
For example, miniimagenet dataset is in the following format:
AMMD
├── data
│ ├── miniImagenet
│ │ ├── train
│ │ │ ├──n01532829
│ │ │ ├──────n0153282900000987.png
│ │ ├── val
│ │ │ ├──
│ │ │ ├──────
│ │ ├── test
│ │ │ ├──
│ │ │ ├──────
The miniimagenet and tieredimagenet-DeepEMD dataset can be downloaded from FRN. The CIFAR-FS and FC100 datasets can be downloaded from DeepEMD.
We follow the pretrain method from FewTURE for Swin-Tiny and ViT-Small backbone, MCL for ResNet-12 backbone.
Download the pretrain weights from Google Drive and extract it into the pretrain/ folder.
Dataset_Method_NwayKshot_Backbone_Accuracy (e.g., miniImagenet_AMMD_linear_triplet_N5K1_R12)
├──Time_Accuracy (e.g., 2023-09-15-21_36_70.319)
├──── predictions.txt (evaluation acc)
├──── config.yaml
├──── ebest_Nway_Kshot.txt (validation best epoch .txt)
├──── ebest_Nway_Kshot.pth (validation best epocg .pth)
Moreover, The train/test config and saved checkpoints are saved in the following format as above.
Download the meta-train snapshot from Google Drive and extract it into the snapshots/ folder.
For example, Swin-Tiny AMMD 5-way 5-shot miniimagenet GPU 0
python experiments/run_trainer.py \
--cfg ./configs_AMMD/miniImagenet/AMMD_linear_triplet_N5K5_swin_0.3_0.5.yaml \
-pt ./pretrain/Swin/mini \
--device 0
ResNet-12 AMMD 5-way 5-shot miniimagenet GPU 0,1
python experiments/run_trainer_multiGPUs.py \
--cfg ./configs_AMMD/miniImagenet/AMMD_linear_triplet_N5K5_R12_0.3_0.2.yaml \
-pt ./pretrain/ResNet/mini \
--d 0,1
ViT-Small AMMD 5-way 5-shot miniimagenet GPU 0
cd CPEA-based-AMMD4ViT
python main_mmd_train.py --gpu 0 --way 5 --test_way 5 --shot 5 \
--dataset MiniImageNet \
--init_weights ../pretrain/ViT/mini/checkpoint1600.pth \
--loss_gamma 0.1 \
--temperature 1.0 \
--max_epoch 100 \
--exp mmd-mini-main-5way-1shot-0.1-1.0 > mmd-mini-main-5way-1shot-0.1-1.0.txt
For example, ResNet-12 AMMD 5-way 5-shot GPU 0
python experiments/run_evaluater.py \
--cfg ./snapshots/miniImagenet_AMMD_linear_triplet_N5K5_R12/2023-10-27-22_03_85.226/AMMD_linear_triplet_N5K5_R12_0.3_0.2.yaml \
-c ./snapshots/miniImagenet_AMMD_linear_triplet_N5K5_R12/2023-10-27-22_03_85.226/ebest_5way_5shot.pth \
-d 0
ViT-Small AMMD 5-way 5-shot miniimagenet GPU 0
cd CPEA-based-AMMD4ViT
python main_mmd_evaluate.py --gpu 0 --way 5 --test_way 5 --shot 1 \
--dataset MiniImageNet \
--init_weights ../pretrain/ViT/mini/checkpoint1600.pth \
--loss_gamma 0.1 \
--temperature 1.0 \
--max_epoch 10 \
--exp mmd-mini-main-5way-1shot-0.1-1.0 > mmd-mini-main-5way-1shot-0.1-1.0-only_test.txt
Experimental results on few-shot learning datasets with ResNet-12/ViT-Small/Swin-Tiny backbone. We report average results with 1,000 randomly sampled episodes for both 1-shot and 5-shot evaluation.
We thank the following repos providing helpful components/functions in our work.
We have tried our best to upload the correct snapshots on the google drive.
If you encounter any issues or have questions about using the code, feel free to contact me wuji98@stu.xjtu.edu.cn.