Awesome

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

Detector	Setting	mAP	Config
FCOS	Teacher (r101, 3x, multi-scale)	43.1	config
-	Student (r50, 1x, single-scale)	38.2	config
-	PGD (r50, 1x, single-scale)	42.5 (+4.3)	config
AutoAssign	Teacher (r101, 3x, multi-scale)	44.8	config
-	Student (r50, 1x, single-scale)	40.6	config
-	PGD (r50, 1x, single-scale)	43.8 (+3.1)	config
ATSS	Teacher (r101, 3x, multi-scale)	45.5	config
-	Student (r50, 1x, single-scale)	39.6	config
-	PGD (r50, 1x, single-scale)	44.2 (+4.6)	config
GFL	Teacher (r101, 3x, multi-scale)	45.8	config
-	Student (r50, 1x, single-scale)	40.2	config
-	PGD (r50, 1x, single-scale)	43.8 (+3.6)	config
DDOD	Teacher (r101, 3x, multi-scale)	46.6	config
-	Student (r50, 1x, single-scale)	42.0	config
-	PGD (r50, 1x, single-scale)	45.4 (+3.4)	config

CrowdHuman

Detector	Setting	MR ↓	AP ↑	JI ↑	Config
DDOD	Teacher (r101, 36 epoch, multi-scale)	41.4	90.2	81.4	config
-	Student (r50, 12 epoch, single-scale)	46.0	88.0	79.0	config
-	PGD (r50, 12 epoch, single-scale)	42.8 (-3.2)	90.0 (+2.0)	80.7 (+1.7)	config

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.