This project is based on mmdetection, mmyolo, detectron2.
mmengine==0.7.3
mmcv==2.0.0
mmdet==3.0.0
mmyolo==0.5.0
detectron2==0.6
#dcnv2==0.1.1
dcn_4==1.0.0 (recommend)
Please refer to mmdetection, mmyolo, detectron2, dcnv4 for installation.
PR-FPN
├── DCNv2
├── DCNv4
├── detectron2
├── mmdetection
├── mmyolo
├── data
│ ├── coco
│ │ ├── annotations
│ │ ├── train2017
│ │ ├── val2017
│ │ ├── test2017
├── faster-rcnn_r50_afpn_1x_coco.py
├── yolov8_n-v61_syncbn_fast_8xb16-300e_coco.py
├── yolov5_n-v61_syncbn_fast_8xb16-300e_coco.py
├── train.py
├── test.py
Single gpu for training:
CUDA_VISIBLE_DEVICES=0 python ./mmdetection/tools/train.py faster-rcnn_r50_prfpn_1x_coco.py --work-dir ./weight/
python3 ./detectron2/tools/train_net.py --config-file ./detectron2/configs/COCO-Detection/mask_rcnn_R_50_PRFPN_1x.yaml --num-gpus 1Multiple gpus for training:
CUDA_VISIBLE_DEVICES=0,1 ./mmdetection/tools/dist_train.sh faster-rcnn_r50_prfpn_1x_coco.py 2 --work-dir ./weight/
python3 ./detectron2/tools/train_net.py --config-file ./detectron2/configs/COCO-Detection/mask_rcnn_R_50_PRFPN_1x.yaml --num-gpus 2If you want to train more models, please refer to train.py.
CUDA_VISIBLE_DEVICES=0 _DEVICES=1 python ./mmdetection/tools/test.py faster-rcnn_r50_prfpn_1x_coco.py <CHECKPOINT_FILE>
python3 ./detectron2/tools/train_net.py --config-file <config.yaml> --num-gpus 1 --eval-only MODEL.WEIGHTS /path/to/model_checkpointFor example,
CUDA_VISIBLE_DEVICES=0 python ./mmdetection/tools/test.py faster-rcnn_r50_prfpn_1x_coco.py ./weight/prfpn_weight.pthIf you want to test more models, please refer to test.py.
If you find PR-FPN useful in your research, please consider citing: