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Model Zoo on COCO

Model Epoch Backbone Input shape $AP^{val}$ $AP^{val}_{50}$ Params(M) FLOPs(G) T4 TensorRT FP16(FPS) Weight Config Log
RT-DETR-R18 6x ResNet-18 640 46.5 63.8 20 60 217 download config rtdetr_r18vd_dec3_6x_coco_log.txt
RT-DETR-R34 6x ResNet-34 640 48.9 66.8 31 92 161 download config rtdetr_r34vd_dec4_6x_coco_log.txt
RT-DETR-R50-m 6x ResNet-50 640 51.3 69.6 36 100 145 download config -
RT-DETR-R50 6x ResNet-50 640 53.1 71.3 42 136 108 download config rtdetr_r50vd_6x_coco_log.txt
RT-DETR-R101 6x ResNet-101 640 54.3 72.7 76 259 74 download config rtdetr_r101vd_6x_coco_log.txt
RT-DETR-L 6x HGNetv2 640 53.0 71.6 32 110 114 download config rtdetr_hgnetv2_l_6x_coco_log.txt
RT-DETR-X 6x HGNetv2 640 54.8 73.1 67 234 74 download config rtdetr_hgnetv2_x_6x_coco_log.txt

Notes:

  • RT-DETR uses 4 GPUs for training.
  • RT-DETR was trained on COCO train2017 and evaluated on val2017.

Model Zoo on Objects365

Model Epoch Dataset Input shape $AP^{val}$ $AP^{val}_{50}$ T4 TensorRT FP16(FPS) Weight Log
RT-DETR-R18 1x Objects365 640 22.9 31.2 - download log.txt
RT-DETR-R18 5x COCO + Objects365 640 49.2 66.6 217 download log.txt
RT-DETR-R50 1x Objects365 640 35.1 46.2 - download log.txt
RT-DETR-R50 2x COCO + Objects365 640 55.3 73.4 108 download log.txt
RT-DETR-R101 1x Objects365 640 36.8 48.3 - download log.txt
RT-DETR-R101 2x COCO + Objects365 640 56.2 74.6 74 download log.txt

Notes:

  • COCO + Objects365 in the table means finetuned model on COCO using pretrained weights trained on Objects365.

Quick start

Install requirements 
pip install -r requirements.txt
Compile (optional) 
cd ./ppdet/modeling/transformers/ext_op/

python setup_ms_deformable_attn_op.py install

See details

Data preparation
  • Download and extract COCO 2017 train and val images.
path/to/coco/
  annotations/  # annotation json files
  train2017/    # train images
  val2017/      # val images
Training & Evaluation & Testing
  • 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 --eval
  • Training on Multiple GPUs:
# training on multi-GPU
export CUDA_VISIBLE_DEVICES=0,1,2,3
python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml --fleet --eval
  • Evaluation:
python tools/eval.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml \
              -o weights=https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_6x_coco.pdparams
  • Inference:
python tools/infer.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml \
              -o weights=https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_6x_coco.pdparams \
              --infer_img=./demo/000000570688.jpg

Finetune

Details
  1. prepare data as coco format.
path/to/custom/data/
    annotations/  # annotation json files
    train/    # train images
    val/      # val images

  1. Modify dataset config dataset_dir, image_dir, anno_path

  2. Modify model config pretrain_weights to coco pretrained parameters url in model zoo.

# or modified in command line

fleetrun --gpus=0,1,2,3 tools/train.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml -o pretrain_weights=https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_6x_coco.pdparams --eval

Deploy

1. Export model 
python tools/export_model.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml \
              -o weights=https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_6x_coco.pdparams trt=True \
              --output_dir=output_inference
2. Convert to ONNX 
pip install onnx==1.13.0
pip install paddle2onnx==1.0.5
  • Convert:
paddle2onnx --model_dir=./output_inference/rtdetr_r50vd_6x_coco/ \
            --model_filename model.pdmodel  \
            --params_filename model.pdiparams \
            --opset_version 16 \
            --save_file rtdetr_r50vd_6x_coco.onnx
3. Convert to TensorRT
  • TensorRT version >= 8.5.1
  • Inference can refer to Bennchmark
trtexec --onnx=./rtdetr_r50vd_6x_coco.onnx \
        --workspace=4096 \
        --shapes=image:1x3x640x640 \
        --saveEngine=rtdetr_r50vd_6x_coco.trt \
        --avgRuns=100 \
        --fp16

Others

1. Parameters and FLOPs
  1. Find and modify paddle dynamic_flops.py source code in your local machine
# eg. /path/to/anaconda3/lib/python3.8/site-packages/paddle/hapi/dynamic_flops.py

def flops(net, input_size, inputs=None, custom_ops=None, print_detail=False):
    if isinstance(net, nn.Layer):
        # If net is a dy2stat model, net.forward is StaticFunction instance,
        # we set net.forward to original forward function.
        _, net.forward = unwrap_decorators(net.forward)

        # by lyuwenyu
        if inputs is None:
            inputs = paddle.randn(input_size)

        return dynamic_flops(
            net, inputs=inputs, custom_ops=custom_ops, print_detail=print_detail
        )
    elif isinstance(net, paddle.static.Program):
        return static_flops(net, print_detail=print_detail)
    else:
        warnings.warn(
            "Your model must be an instance of paddle.nn.Layer or paddle.static.Program."
        )
        return -1
  1. Run below code
import paddle
from ppdet.core.workspace import load_config, merge_config
from ppdet.core.workspace import create

cfg_path = './configs/rtdetr/rtdetr_r50vd_6x_coco.yml'
cfg = load_config(cfg_path)
model = create(cfg.architecture)

blob = {
    'image': paddle.randn([1, 3, 640, 640]),
    'im_shape': paddle.to_tensor([[640, 640]]),
    'scale_factor': paddle.to_tensor([[1., 1.]])
}
paddle.flops(model, None, blob, custom_ops=None, print_detail=False)

# Outpus
# Total Flops: 68348108800     Total Params: 41514204