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detr.detectron2's Introduction

DETR based on Detectron2

Instructions

  1. Follow detectron2's readme to install detection2
  • cd DETR.detectron2
  • python setup.py build develop
  • link dataset path to DETR.detectron2/datasets/
  1. Train DETR
  • python projects/DETR/train_net.py --num-gpus 8 --config-file projects/DETR/configs/detr.res50.coco.multiscale.150e.yaml: use MSRA pretrain weights
  1. Evaluate DETR using provided weights here
  • python projects/DETR/train_net.py --num-gpus 8 --config-file projects/DETR/configs/detr.res50.coco.multiscale.150e.yaml --eval-only MODEL.WEIGHTS path/to/provided/ckpt.pth
  1. For faster training:
  • python projects/DETR/train_net.py --num-gpus 8 --config-file projects/DETR/configs/detr.res50.coco.multiscale.150e.bs48.yaml Using a 8x2080ti server, 150 epochs take about 3 day 6 hours.

Results

config COCO AP Paper Checkpoint
detr.res50.coco.multiscale.150e.yaml 38.6 without RC 39.5 with RC LINK

"RC" means RandomCrop, it brings about 1% AP improvements accroding to paper.

Disclaimer

  • I haven't add RandomCrop.
  • I haven't add support for segmentaion, but it can be easily added.

Advantage

  • Training faster
  • Avoid memory leaking in official implementation
  • Use backbone in detectron2

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