PAA_minimal

Minimal PyTorch implementation of ECCV2020: Probabilistic Anchor Assignment with IoU Prediction for Object Detection.
The original project is here.

Environments

1.1 <= PyTorch <= 1.4 (Version > 1.4 will cause a compilation error).
Python >= 3.6.
Other common packages.

Prepare

• Download COCO 2017 datasets, modify the paths of training and evalution datasets in config.py.

• # Build DCN, NMS, CUDA FocalLoss.
  cd build_stuff
  python setup.py build develop
  

• Download weights and put the weight files in weights folder.

PAA trained weights.

I trained on two RTX-2080Ti GPUs. Following are results on COCO val2017. SV=score voting.
The result is slightly different from the original paper because of a different training batch size and the training progress itself is a little unstable.

cfg	total iterations	mAP	Google Drive	Baidu Cloud
res50_1x	120000 (bs=12)	40.2 (40.5 with SV)	res50_1x_116000.pth	password: 070q
res101_2x	288000 (bs=10)	44.2 (44.3 with SV)	res101_2x_287999.pth	password: 9hpa

Backbone pre-trained weights.

Backbone	Google Drive	Baidu Cloud
Resnet50	R-50.pkl	password: i8i3
Resnet101	R-101.pkl	password: 04ia

Train

# Train by res50_1x configuration with a certain batch_size on some specific GPUs.
export CUDA_VISIBLE_DEVICES=0,1
python -m torch.distributed.launch --nproc_per_node=2 train.py --train_bs=12

# Train with other configuration. (There are 4 configurations in total: res50_1x, res50_15x, res101_2x, res101_dcn_2x.)
python -m torch.distributed.launch --nproc_per_node=2 train.py --train_bs=12 --cfg=res101_2x

# Resume training.
python -m torch.distributed.launch --nproc_per_node=2 train.py --train_bs=12 --cfg=res101_2x --resume=weight/[weight_file]

# Other utilization 
--test_bs=2, set validation batch size.
--val_interval=6000, set validation interval during training.
--val_num=500, set validation number during training.
--score_voting, activate score voting during validation.
--improved_coco, use an improved COCO API to do validation.

Evalution

# Evaluate COCO val2017 on a specific GPU.
python val.py --gpu_id=0 --weight=weights/res50_1x_116000.pth

# Evaluate with a specific batch size.
python val.py --gpu_id=0 --weight=weights/res50_1x_116000.pth --test_bs=2

# Specify validation number.
python val.py --gpu_id=0 --weight=weights/res50_1x_116000.pth --val_num=500

# Evaluate with score voting.
python val.py --gpu_id=0 --weight=weights/res50_1x_116000.pth --score_voting

# Use an improved COCO API to do validation.
python val.py --gpu_id=0 --weight=weights/res50_1x_116000.pth --improved_coco

Reference:

• https://github.com/kkhoot/PAA

@inproceedings{paa-eccv2020,
  title={Probabilistic Anchor Assignment with IoU Prediction for Object Detection},
  author={Kim, Kang and Lee, Hee Seok},
  booktitle = {ECCV},
  year={2020}
}