TODO

• 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

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

• Modify config img_folder, ann_file

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.

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