Awesome

TDD

The implementation of our CVPR2022 paper "Cross Domain Object Detection by Target-Perceived Dual Branch Distillation"

Paper: https://arxiv.org/pdf/2205.01291

2022.5.2 update the test code and model.
2022.5.10 update the train code and model.\

The enviroment and data preparation will be updated soon.

Inference:

python train_net.py --eval-only --num-gpus 8 --config-file configs/TDD/***.yaml MODEL.WEIGHTS ***.pth

Released Weights

For your convenience, the trained models are provided in this link. (BaiduYun code: e0lh)

Joint Pretrain:
The first stage of pretraining our detector with source images and target-like images. python train_net.py --num-gpus 8 --config-file configs/prertain_r50_FPN/faster_rcnn_R_50_FPN_focal_cross_bdd.yaml

The weights are also preovided in the link above. python train_net.py --num-gpus 8 --eval-only --config-file *****.yaml MODEL.WEIGHTS ********.pth\

TDD Train
python train_net.py --num-gpus 8 --config-file configs/TDD/faster_rcnn_R_50_FPN_foggy.yaml\

Note: Now only support that one gpu two images ( when GPUS=8, set IMG_PER_BATCH_LABEL: 8 IMG_PER_BATCH_UNLABEL: 8)

The two stage can be trained with one config file by modify the BURN_UP_STEP which means the step of joint pretrain.