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README.md

TODO

see details
  • Training
  • Evaluation
  • Export onnx
  • Upload source code
  • Upload weight convert from paddle, see links
  • Align training details with the paddle version
  • Tuning rtdetr based on pretrained weights

Model Zoo

Model Dataset Input Size APval AP50val #Params(M) FPS checkpoint
rtdetr_r18vd COCO 640 46.4 63.7 20 217 url*
rtdetr_r34vd COCO 640 48.9 66.8 31 161 url*
rtdetr_r50vd_m COCO 640 51.3 69.5 36 145 url*
rtdetr_r50vd COCO 640 53.1 71.2 42 108 url*
rtdetr_r101vd COCO 640 54.3 72.8 76 74 url*
rtdetr_18vd COCO+Objects365 640 49.0 66.5 20 217 url*
rtdetr_r50vd COCO+Objects365 640 55.2 73.4 42 108 url*
rtdetr_r101vd COCO+Objects365 640 56.2 74.5 76 74 url*
rtdetr_regnet COCO 640 51.6 69.6 38 67 url*
rtdetr_dla34 COCO 640 49.6 67.4 34 83 url*

Notes

  • COCO + Objects365 in the table means finetuned model on COCO using pretrained weights trained on Objects365.
  • url* is the url of pretrained weights convert from paddle model for save energy. It may have slight differences between this table and paper

Quick start

Install 
pip install -r requirements.txt
Data
  • Download and extract COCO 2017 train and val images.
path/to/coco/
  annotations/  # annotation json files
  train2017/    # train images
  val2017/      # val images
Training & Evaluation
  • Training on a Single GPU:
# training on single-gpu
export CUDA_VISIBLE_DEVICES=0
python tools/train.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml
  • Training on Multiple GPUs:
# train on multi-gpu
export CUDA_VISIBLE_DEVICES=0,1,2,3
torchrun --nproc_per_node=4 tools/train.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml
  • Evaluation on Multiple GPUs:
# val on multi-gpu
export CUDA_VISIBLE_DEVICES=0,1,2,3
torchrun --nproc_per_node=4 tools/train.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml -r path/to/checkpoint --test-only
Export 
python tools/export_onnx.py -c configs/rtdetr/rtdetr_r18vd_6x_coco.yml -r path/to/checkpoint --check
Train custom data

  1. set remap_mscoco_category: False. This variable only works for ms-coco dataset. If you want to use remap_mscoco_category logic on your dataset, please modify variable mscoco_category2name based on your dataset.

  2. add -t path/to/checkpoint (optinal) to tuning rtdetr based on pretrained checkpoint. see training script details.