HCCNet (WACV '24 Best Paper Finalist [Top 0.6%])

Official PyTorch implementation of HCCNet: Efficient Semantic Matching with Hypercolumn Correlation (WACV '24 Oral, Best paper finalist (top 0.6%))

| Project Page | Paper(Arxiv)|

HCCNet performance - latency visualization

HCCNet architecture

Environment settings:

conda create -n mbm python=3.10
conda activate mbm

# xformer 0.0.25 is compatible with pytorch 2.2.1
pip3 install torch==2.2.1 torchvision --index-url https://download.pytorch.org/whl/cu118 
# lightning
pip3 install lightning
pip install torchmetrics
# logging
pip3 install wandb

#formatting
pip install black

pip install einops
pip install -U albumentations
pip install pandas
conda install scipy

Training

Change the dataset you want to train with from configs/config.yaml, from pfpascal or spair. Then run the following:

python train.py

The training / validation will be logged on WandB by default, under the project name "HCCNet".

TODOs

• Release training code
• Release training script per dataset
• Release test code per dataset
• Release pre-trained weights