✔️ Requirements

• Ubuntu 16.04
• Python 3.7
• CUDA 11.0
• PyTorch 1.7.1

⚙️ Conda environmnet installation

conda env create --name CBRNet --file environment.yml
conda activate CBRNet

📚 Datasets

cd datasets
bash download_miniimagenet.sh
bash download_cub.sh
bash download_cifar_fs.sh
bash download_tieredimagenet.sh

🌳 Authors' checkpoints

cd checkpoints
bash download_checkpoints_CBRNet.sh

The file structure should be as follows:

CBRNet/
├── datasets/
├── model/
├── scripts/
├── checkpoints/
│   ├── cifar_fs/
│   ├── cub/
│   ├── miniimagenet/
│   └── tieredimagenet/
train.py
test.py
README.md
environment.yml

📌 Quick start: testing scripts

To test in the 5-way K-shot setting:

bash scripts/test/{dataset_name}_5wKs.sh

For example, to test CBRNet on the miniImagenet dataset in the 5-way 1-shot setting:

bash scripts/test/miniimagenet_5w1s.sh

🔥 Training scripts

To train in the 5-way K-shot setting:

bash scripts/train/{dataset_name}_5wKs.sh

For example, to train CBRNet on the CUB dataset in the 5-way 1-shot setting:

bash scripts/train/cub_5w1s.sh

Training & testing a 5-way 1-shot model on the CUB dataset using a TitanRTX 3090 GPU takes 41m 30s.

🎨 Few-shot classification results

Experimental results on few-shot classification datasets with ResNet-12 backbone. We report average results with 2,000 randomly sampled episodes.

datasets	miniImageNet		tieredImageNet
setups	5-way 1-shot	5-way 5-shot	5-way 1-shot	5-way 5-shot
accuracy	67.60	82.58	71.61	85.28

datasets	CUB-200-2011		CIFAR-FS
setups	5-way 1-shot	5-way 5-shot	5-way 1-shot	5-way 5-shot
accuracy	79.49	91.11	74.51	86.60