RepMAF: When Re-parameterization Meets Multi-scale Attention

CSCI-GA 2271: Computer Vision - Final Project (Tutored by Prof. Rob Fergus)
Group Members: Haoming Liu, Chen Song Zhang, Jiayao Jin.

Setup

Requisites: numpy, torch, torchvision, torchsummary.
Clone the project repo:

git clone https://github.com/hmdliu/RepMAF -b final
cd RepMAF

Download the CIFAR-10 dataset:

python prep_dataset.py
rm cifar-10-python.tar.gz

Training script

1. On a HPC with a singlularity env and slurm:

# remember to modify the path in the sbatch script
sbatch train.SBATCH [exp_id]

2. On a computer with GPU:

# remember to activate the env
python train.py [exp_id] > [exp_id].log 2>&1 

Training log can be found in [exp_id].log \

Experiment IDs

The IDs follow the original order in the report.

Table 1: vgg-idt, vgg-se, repvgg-idt, repvgg-se,birepvgg-idt3, birepvgg-se3, repmaf-maf3, repmaf-maf4.

Table 2: repvgg-idt, repvgg-ses, repvgg-se.
(Note: To disable data augmentation, please set config['aug'] = False in config.py.)

Table 3: repvgg-se3, repvgg-se2, repvgg-se1, repvgg-se3, repmaf-maf5, repmaf-maf3, repmaf-maf1, repmaf-maf6, repmaf-maf4, repmaf-maf2.

Inference speed test

# On a HPC with a singlularity env and slurm
sbatch inference.SBATCH

# On a computer with GPU
python inference.py

Util functions

# Check best pred of multiple experiments
python helper.py dump

# Archive training logs
python helper.py log move