简体中文 | [English](README_en.md) ## 模型 | Model | Epoch | backbone | input shape | $AP^{val}$ | $AP^{val}_{50}$| Params(M) | FLOPs(G) | T4 TensorRT FP16(FPS) | Pretrained Model | config | |:--------------:|:-----:|:----------:| :-------:|:--------------------------:|:---------------------------:|:---------:|:--------:| :---------------------: |:------------------------------------------------------------------------------------:|:-------------------------------------------:| | RT-DETR-R18 | 6x | ResNet-18 | 640 | 46.5 | 63.8 | 20 | 60 | 217 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r18vd_dec3_6x_coco.pdparams) | [config](./configs/rtdetr/rtdetr_r18vd_6x_coco.yml) | RT-DETR-R34 | 6x | ResNet-34 | 640 | 48.9 | 66.8 | 31 | 92 | 161 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r34vd_dec4_6x_coco.pdparams) | [config](./configs/rtdetr/rtdetr_r34vd_6x_coco.yml) | RT-DETR-R50-m | 6x | ResNet-50 | 640 | 51.3 | 69.6 | 36 | 100 | 145 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_m_6x_coco.pdparams) | [config](./configs/rtdetr/rtdetr_r50vd_m_6x_coco.yml) | RT-DETR-R50 | 6x | ResNet-50 | 640 | 53.1 | 71.3 | 42 | 136 | 108 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_6x_coco.pdparams) | [config](./configs/rtdetr/rtdetr_r50vd_6x_coco.yml) | RT-DETR-R101 | 6x | ResNet-101 | 640 | 54.3 | 72.7 | 76 | 259 | 74 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r101vd_6x_coco.pdparams) | [config](./configs/rtdetr/rtdetr_r101vd_6x_coco.yml) | RT-DETR-L | 6x | HGNetv2 | 640 | 53.0 | 71.6 | 32 | 110 | 114 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_hgnetv2_l_6x_coco.pdparams) | [config](./configs/rtdetr/rtdetr_hgnetv2_l_6x_coco.yml) | RT-DETR-X | 6x | HGNetv2 | 640 | 54.8 | 73.1 | 67 | 234 | 74 | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_hgnetv2_x_6x_coco.pdparams) | [config](./configs/rtdetr/rtdetr_hgnetv2_x_6x_coco.yml) **注意事项:** - RT-DETR 使用4个GPU训练。 - RT-DETR 在COCO train2017上训练，并在val2017上评估。 ## 快速开始
依赖包 ```bash pip install -r requirements.txt ```
准备数据 - 修改[配置文件`dataset_dir`](configs/datasets/coco_detection.yml)
训练&评估 - 单卡GPU上训练: ```shell # training on single-GPU export CUDA_VISIBLE_DEVICES=0 python tools/train.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml --eval ``` - 多卡GPU上训练: ```shell # 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 ``` - 评估: ```shell 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 ``` - 测试: ```shell 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 ``` 详情请参考[快速开始文档](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/GETTING_STARTED.md).
## 部署
1. 导出模型 ```shell 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. 转换模型至ONNX - 安装[Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX) 和 ONNX ```shell pip install onnx==1.13.0 pip install paddle2onnx==1.0.5 ``` - 转换模型: ```shell 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. 转换成TensorRT - 确保TensorRT的版本>=8.5.1 - TRT推理可以参考[RT-DETR](https://github.com/lyuwenyu/RT-DETR)的部分代码或者其他网络资源 ```shell trtexec --onnx=./rtdetr_r50vd_6x_coco.onnx \ --workspace=4096 \ --shapes=image:1x3x640x640 \ --saveEngine=rtdetr_r50vd_6x_coco.trt \ --avgRuns=100 \ --fp16 ``` -
## 其他
1. 参数量和计算量统计 1. 找到[本地安装paddle的flops源代码](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/hapi/dynamic_flops.py#L28), 并修改为 ```python # 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 ``` 2. 使用以下代码片段实现参数量和计算量的统计 ```python 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) ```
2. YOLOs端到端速度测速 - 可以参考[RT-DETR](https://github.com/lyuwenyu/RT-DETR) benchmark部分或者其他网络资源
## 引用RT-DETR 如果需要在你的研究中使用RT-DETR，请通过以下方式引用我们的论文： ``` @misc{lv2023detrs, title={DETRs Beat YOLOs on Real-time Object Detection}, author={Wenyu Lv and Shangliang Xu and Yian Zhao and Guanzhong Wang and Jinman Wei and Cheng Cui and Yuning Du and Qingqing Dang and Yi Liu}, year={2023}, eprint={2304.08069}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```