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English | [简体中文](README_cn.md)
## 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](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r18vd_dec3_6x_coco.pdparams) | [config](./configs/rtdetr/rtdetr_r18vd_6x_coco.yml) | [rtdetr_r18vd_dec3_6x_coco_log.txt](https://github.com/lyuwenyu/RT-DETR/files/12038864/rtdetr_r18vd_dec3_6x_coco_log.txt)
| 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) | [rtdetr_r34vd_dec4_6x_coco_log.txt](https://github.com/lyuwenyu/RT-DETR/files/12038861/rtdetr_r34vd_dec4_6x_coco_log.txt)
| 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) | [rtdetr_r50vd_6x_coco_log.txt](https://github.com/lyuwenyu/RT-DETR/files/12038669/rtdetr_r50vd_6x_coco_log.txt)
| 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) | [rtdetr_r101vd_6x_coco_log.txt](https://github.com/lyuwenyu/RT-DETR/files/12038707/rtdetr_r101vd_6x_coco_log.txt)
| 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) | [rtdetr_hgnetv2_l_6x_coco_log.txt](https://github.com/lyuwenyu/RT-DETR/files/12038753/rtdetr_hgnetv2_l_6x_coco_log.txt)
| 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) | [rtdetr_hgnetv2_x_6x_coco_log.txt](https://github.com/lyuwenyu/RT-DETR/files/12038795/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](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r18vd_1x_objects365.pdparams) | [log.txt](https://github.com/lyuwenyu/RT-DETR/files/12394706/rtdetr_r18vd_1x_objects365_log.txt)
RT-DETR-R18 | 5x | COCO + Objects365 | 640 | **49.2** | **66.6** | **217** | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r18vd_5x_coco_objects365.pdparams) | [log.txt](https://github.com/lyuwenyu/RT-DETR/files/12416808/rtdetr_r18vd_5x_coco_objects365_log.txt)
RT-DETR-R50 | 1x | Objects365 | 640 | 35.1 | 46.2 | - | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_1x_objects365.pdparams) |[log.txt](https://github.com/lyuwenyu/RT-DETR/files/12193246/rtdetr_r50vd_1x_objects365_log.txt)
RT-DETR-R50 | 2x | COCO + Objects365 | 640 | **55.3** | **73.4** | **108** | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r50vd_2x_coco_objects365.pdparams) | [log.txt](https://github.com/lyuwenyu/RT-DETR/files/12208338/rtdetr_r50vd_2x_coco_objects365_log.txt)
RT-DETR-R101 | 1x | Objects365 | 640 | 36.8 | 48.3 | - | [download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r101vd_1x_objects365.pdparams) | [log.txt](https://github.com/lyuwenyu/RT-DETR/files/12340691/rtdetr_r101vd_1x_objects365_log.txt)
RT-DETR-R101 | 2x | COCO + Objects365 | 640 | **56.2** | **74.6** | **74** |[download](https://bj.bcebos.com/v1/paddledet/models/rtdetr_r101vd_2x_coco_objects365.pdparams) | [log.txt](https://github.com/lyuwenyu/RT-DETR/files/12340672/rtdetr_r101vd_2x_coco_objects365_log.txt)
**Notes:**
- `COCO + Objects365` in the table means finetuned model on COCO using pretrained weights trained on Objects365.
## Quick start
<details open>
<summary>Install requirements</summary>
<!-- - PaddlePaddle == 2.4.2 -->
```bash
pip install -r requirements.txt
```
</details>
<details>
<summary>Compile (optional)</summary>
```bash
cd ./ppdet/modeling/transformers/ext_op/
python setup_ms_deformable_attn_op.py install
```
See [details](./ppdet/modeling/transformers/ext_op/)
</details>
<details>
<summary>Data preparation</summary>
- Download and extract COCO 2017 train and val images.
```
path/to/coco/
annotations/ # annotation json files
train2017/ # train images
val2017/ # val images
```
- Modify config [`dataset_dir`](configs/datasets/coco_detection.yml)
</details>
<details>
<summary>Training & Evaluation & Testing</summary>
- Training on a Single GPU:
```shell
# 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:
```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
```
- Evaluation:
```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
```
- Inference:
```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
```
</details>
## Finetune
<details>
<summary>Details</summary>
1. prepare data as coco format.
```
path/to/custom/data/
annotations/ # annotation json files
train/ # train images
val/ # val images
```
2. Modify dataset config [`dataset_dir`, `image_dir`, `anno_path`](configs/datasets/coco_detection.yml)
3. Modify model config [`pretrain_weights`](configs/rtdetr/_base_/rtdetr_r50vd.yml) to coco pretrained parameters url in model zoo.
```bash
# 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
```
</details>
## Deploy
<details open>
<summary>1. Export model </summary>
```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
```
</details>
<details>
<summary>2. Convert to ONNX </summary>
- Install [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX) and ONNX
```shell
pip install onnx==1.13.0
pip install paddle2onnx==1.0.5
```
- Convert:
```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
```
</details>
<details>
<summary>3. Convert to TensorRT </summary>
- TensorRT version >= 8.5.1
- Inference can refer to [Bennchmark](../benchmark)
```shell
trtexec --onnx=./rtdetr_r50vd_6x_coco.onnx \
--workspace=4096 \
--shapes=image:1x3x640x640 \
--saveEngine=rtdetr_r50vd_6x_coco.trt \
--avgRuns=100 \
--fp16
```
-
</details>
## Others
<details>
<summary>1. Parameters and FLOPs </summary>
1. Find and modify paddle [`dynamic_flops.py` ](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/hapi/dynamic_flops.py#L28) source code in your local machine
```python
# 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
```
2. Run below code
```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)
# Outpus
# Total Flops: 68348108800 Total Params: 41514204
```
</details>