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RDNet for Object Detection & Instance Segmentation

backbone/rdnet.py for detection and segmentation is the exact same file as rdnet.py for classification.

Tested Environment

  • python 3.9.18
  • torch 1.11.0
  • timm 0.9.6
  • mmcv-full 1.4.8
  • mmdet 2.25.3
  • mmcls 0.25.0
  • mmsegmentation 0.22.1

Pretrained Models & logs

Mask-RCNN Pretrained Models & logs (HFHub): https://huggingface.co/naver-ai/rdnet_mask_rcnn_coco_3x/tree/main

Cascade Mask-RCNN Pretrained Models & logs (HFHub): https://huggingface.co/naver-ai/rdnet_cascade_mask_rcnn_coco_3x/tree/main

$Model$ $Backbone$ $Params$ $FLOPs$ $AP_{box}$ $AP^{50}_{box}$ $AP^{75}_{box}$ $AP_{mask}$ $AP^{50}_{mask}$ $AP^{75}_{mask}$ url
Mask-RCNN RDNet-T 43M 278G 47.5 68.5 52.1 42.4 65.6 45.7 ckpt, train_log
Cascade Mask-RCNN RDNet-T 81M 757G 51.6 70.5 56.0 44.6 67.9 48.3 ckpt, train_log
Cascade Mask-RCNN RDNet-S 108M 832G 52.3 70.8 56.6 45.4 68.5 49.3 ckpt, train_log
Cascade Mask-RCNN RDNet-B 144M 971G 52.9 71.5 57.2 46.0 69.1 50.0 ckpt, train_log

Train

torchrun --nproc_per_node=4 train.py configs/rdnet/mask_rcnn_rdnet_tiny_3x.py --launcher pytorch
torchrun --nproc_per_node=4 train.py configs/rdnet/cascade_mask_rcnn_rdnet_tiny_3x.py --launcher pytorch
torchrun --nproc_per_node=4 train.py configs/rdnet/cascade_mask_rcnn_rdnet_small_3x.py --launcher pytorch
torchrun --nproc_per_node=4 train.py configs/rdnet/cascade_mask_rcnn_rdnet_base_3x.py --launcher pytorch