Prediction-Guided Distillation

PyTorch implementation of our ECCV 2022 paper: Prediction-Guided Distillation for Dense Object Detection

Requirements

• Our codebase is built on top of MMDetection, which can be installed following the offcial instuctions.
• We used pytorch pre-trained ResNets for training.
• Please follow the MMdetection offcial instuction to set up COCO dataset.
• Please download the CrowdHuman and set up the dataset by running this script.

Usage

Set up datasets and pre-trained models

mkdir data
ln -s path_to_coco data/coco
ln -s path_to_crowdhuman data/crowdhuman 
ln -s path_to_pretrainedModel data/pretrain_models 

COCO Experiments

# ------------------------------------
#    Here we use ATSS as an example
# ------------------------------------

# Training and testing teacher model
zsh tools/dist_train.sh work_configs/detectors/atss_r101_3x_ms.py 8
zsh tools/dist_test.sh work_configs/detectors/atss_r101_3x_ms.py work_dirs/atss_r101_3x_ms/latest.pth 8

# Training and testing student model 
zsh tools/dist_train.sh work_configs/detectors/atss_r50_1x.py 8
zsh tools/dist_test.sh work_configs/detectors/atss_r50_1x.py work_dirs/atss_r50_1x/latest.pth 8

# Training and testing PGD model
zsh tools/dist_train.sh work_configs/pgd_atss_r101_r50_1x.py 8
zsh tools/dist_test.sh work_configs/pgd_atss_r101_r50_1x.py work_dirs/pgd_atss_r101_r50_1x/latest.pth 8

CrowdHuman Experiments

# Training teacher, conducting KD, and evalauation
zsh tools/run_crowdhuman.sh

Model Zoo

COCO

CrowdHuman

Ciation

@article{yang2022predictionguided,
  title={{Prediction-Guided Distillation for Dense Object Detection}},
  author={Yang, Chenhongyi and Ochal, Mateusz and Storkey, Amos and Crowley, Elliot J},
  journal={ECCV 2022},
  year={2022}
}

Acknowledgement

We thank FGD and DDOD for their code base.