README.md

English | [简体中文](README_cn.md)

# PP-YOLOE

## Latest News
- Release PP-YOLOE+ model: **(2022.08)**
  - Pre training model using large-scale data set obj365
  - In the backbone, add the alpha parameter to the block branch
  - Optimize the end-to-end inference speed and improve the training convergence speed

## Legacy model
- Please refer to：[PP-YOLOE 2022.03](./README_legacy.md) for details

## Table of Contents
- [Introduction](#Introduction)
- [Model Zoo](#Model-Zoo)
- [Getting Start](#Getting-Start)
- [Appendix](#Appendix)

## Introduction
PP-YOLOE is an excellent single-stage anchor-free model based on PP-YOLOv2, surpassing a variety of popular YOLO models. PP-YOLOE has a series of models, named s/m/l/x, which are configured through width multiplier and depth multiplier. PP-YOLOE avoids using special operators, such as Deformable Convolution or Matrix NMS, to be deployed friendly on various hardware. For more details, please refer to our [report](https://arxiv.org/abs/2203.16250).

<div align="center">
  <img src="../../docs/images/ppyoloe_plus_map_fps.png" width=500 />
</div>

PP-YOLOE+_l achieves 53.3 mAP on COCO test-dev2017 dataset with 78.1 FPS on Tesla V100. While using TensorRT FP16, PP-YOLOE+_l can be further accelerated to 149.2 FPS. PP-YOLOE+_s/m/x also have excellent accuracy and speed performance, which can be found in [Model Zoo](#Model-Zoo)

PP-YOLOE is composed of following methods:
- Scalable backbone and neck
- [Task Alignment Learning](https://arxiv.org/abs/2108.07755)
- Efficient Task-aligned head with [DFL](https://arxiv.org/abs/2006.04388) and [VFL](https://arxiv.org/abs/2008.13367)
- [SiLU(Swish) activation function](https://arxiv.org/abs/1710.05941)

## Model Zoo

### Model Zoo on COCO

|     Model      | Epoch | GPU number | images/GPU |  backbone  | input shape | Box AP<sup>val<br>0.5:0.95 | Box AP<sup>test<br>0.5:0.95 | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) |                                       download                                       |                   config                    |
|:--------------:|:-----:|:-------:|:----------:|:----------:| :-------:|:--------------------------:|:---------------------------:|:---------:|:--------:|:---------------:| :---------------------: |:------------------------------------------------------------------------------------:|:-------------------------------------------:|
|  PP-YOLOE+_s   |  80   |     8      |     8      | cspresnet-s |     640     |            43.7            |            43.9             |   7.93    |  17.36   |   208.3   |  333.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco.pdparams) | [config](./ppyoloe_plus_crn_s_80e_coco.yml) |
|  PP-YOLOE+_m   |  80   |     8      |     8      | cspresnet-m |     640     |            49.8            |            50.0             |   23.43   |  49.91   |   123.4   |  208.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_m_80e_coco.pdparams) | [config](./ppyoloe_plus_crn_m_80e_coco.yml) |
|   PP-YOLOE+_m(distill)   |  80   |     8     |    8     | cspresnet-m |    640     |            **51.0**            |            51.2             |   23.43   |  49.91   |   123.4   |  208.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_m_80e_coco_distill.pdparams) | [config](distill/ppyoloe_plus_crn_m_80e_coco_distill.yml) |
|  PP-YOLOE+_l   |  80   |     8      |     8      | cspresnet-l |     640     |            52.9            |            53.3             |   52.20   |  110.07  |   78.1    |  149.2   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams) | [config](./ppyoloe_plus_crn_l_80e_coco.yml) |
|   PP-YOLOE+_l(distill)   |  80   |     8     |    8     | cspresnet-l |    640     |            **54.0**            |            54.4             |   52.20   |  110.07  |   78.1    |  149.2   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco_distill.pdparams) | [config](distill/ppyoloe_plus_crn_l_80e_coco_distill.yml) |
|  PP-YOLOE+_x   |  80   |     8      |     8      | cspresnet-x |     640     |            54.7            |            54.9             |   98.42   |  206.59  |   45.0    |   95.2   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_x_80e_coco.pdparams) | [config](./ppyoloe_plus_crn_x_80e_coco.yml) |

**Note:**
- M and L models use distillation, please refer to [distill](distill) for details.

#### Tiny model

|   Model  | Epoch | GPU number | images/GPU |  backbone  | input shape | Box AP<sup>val<br>0.5:0.95 | Box AP<sup>test<br>0.5:0.95 | Params(M) | FLOPs(G) | T4 TensorRT FP16(FPS) | download  | config  |
|:--------:|:-----:|:----------:|:----------:|:----------:|:-----------:|:--------------------------:|:---------------------------:|:---------:|:--------:|:---------------------:| :------: |:--------:|
|   PP-YOLOE+_t-aux(640) |  300   |     8     |    8     | cspresnet-t  |    640  |            39.9       |            56.6             |   4.85   |  19.15 |  344.8   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_t_auxhead_300e_coco.pdparams) | [config](./ppyoloe_plus_crn_t_auxhead_300e_coco.yml) |
|   PP-YOLOE+_t-aux(640)-relu |  300   |     8     |    8     | cspresnet-t  |    640  |            36.4       |            53.0        |   3.60   |  12.17 |   476.2   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_t_auxhead_relu_300e_coco.pdparams) | [config](./ppyoloe_plus_crn_t_auxhead_relu_300e_coco.yml) |
|   PP-YOLOE+_t-aux(320) |  300   |     8     |    8     | cspresnet-t  |    320  |            33.3       |            48.5             |   4.85   |  4.80 |   729.9   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_t_auxhead_320_300e_coco.pdparams) | [config](./ppyoloe_plus_crn_t_auxhead_320_300e_coco.yml) |
|   PP-YOLOE+_t-aux(320)-relu |  300   |     8     |    8     | cspresnet-t  |    320  |            30.1       |            44.7        |   3.60   |  3.04 |   984.8   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_t_auxhead_relu_320_300e_coco.pdparams) | [config](./ppyoloe_plus_crn_t_auxhead_relu_320_300e_coco.yml) |


### Comprehensive Metrics
|          Model           | Epoch | AP<sup>0.5:0.95 | AP<sup>0.5 | AP<sup>0.75  | AP<sup>small | AP<sup>medium | AP<sup>large | AR<sup>small | AR<sup>medium | AR<sup>large |
|:------------------------:|:-----:|:---------------:|:----------:|:------------:|:------------:| :-----------: |:------------:|:------------:|:-------------:|:------------:|
|       PP-YOLOE+_s        |  80   |      43.7       |    60.6    |    47.9     |     26.5     |     47.5      |     59.0     |     46.7     |     71.4      |     81.7     |
|       PP-YOLOE+_m        |  80   |      49.8       |    67.1    |    54.5     |     31.8     |     53.9      |     66.2     |     53.3     |     75.0      |     84.6     |
|       PP-YOLOE+_m(distill)|  80   |      51.0       |    68.1    |    55.8     |     32.5     |     55.7      |     67.4     |     51.9     |     76.1      |     86.4     |
|       PP-YOLOE+_l        |  80   |      52.9       |    70.1    |    57.9     |     35.2     |     57.5      |     69.1     |     56.0     |     77.9      |     86.9     |
|       PP-YOLOE+_l(distill)|  80   |      54.0       |    71.2    |    59.2     |     36.1     |     58.8      |     70.4     |     55.0     |     78.7      |     87.7     |
|       PP-YOLOE+_x        |  80   |      54.7       |    72.0    |    59.9     |     37.9     |     59.3      |     70.4     |     57.0     |     78.7      |     87.2     |

**Note:**
- M and L models use distillation, please refer to [distill](distill) for details.


### End-to-end Speed
|    Model    | AP<sup>0.5:0.95 | TRT-FP32(fps) | TRT-FP16(fps) |
|:-----------:|:---------------:|:-------------:|:-------------:|
| PP-YOLOE+_s |      43.7       |     44.44     |     47.85     |
| PP-YOLOE+_m |      49.8       |     39.06     |     43.86     |
| PP-YOLOE+_l |      52.9       |     34.01     |     42.02     |
| PP-YOLOE+_x |      54.7       |     26.88     |     36.76     |

**Notes:**

- PP-YOLOE is trained on COCO train2017 dataset and evaluated on val2017 & test-dev2017 dataset.
- The model weights in the table of Comprehensive Metrics are **the same as** that in the original Model Zoo, and evaluated on **val2017**.
- PP-YOLOE used 8 GPUs for mixed precision training, if **GPU number** or **mini-batch size** is changed, **learning rate** should be adjusted according to the formula **lr<sub>new</sub> = lr<sub>default</sub> * (batch_size<sub>new</sub> * GPU_number<sub>new</sub>) / (batch_size<sub>default</sub> * GPU_number<sub>default</sub>)**.
- PP-YOLOE inference speed is tesed on single Tesla V100 with batch size as 1, **CUDA 10.2**, **CUDNN 7.6.5**, **TensorRT 6.0.1.8** in TensorRT mode.
- Refer to [Speed testing](#Speed-testing) to reproduce the speed testing results of PP-YOLOE.
- If you set `--run_benchmark=True`，you should install these dependencies at first, `pip install pynvml psutil GPUtil`.
- End-to-end speed test includes pre-processing + inference + post-processing and NMS time, using **Intel(R) Xeon(R) Gold 5117 CPU @ 2.00GHz**, **single Tesla V100**, **CUDA 11.2**, **CUDNN 8.2.0**, **TensorRT 8.0.1.6**.

### Model Zoo on Objects365
|     Model      | Epoch | Machine number | GPU number | images/GPU |  backbone  | input shape | Box AP<sup>0.5 | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) |  download  |  config  |
|:---------------:|:-----:|:-----------:|:-----------:|:-----------:|:---------:|:----------:|:--------------:|:---------:|:---------:|:-------------:|:-----------------------:| :--------:|:--------:|
|   PP-YOLOE+_s   |  60   |  3 |  8     |    8     | cspresnet-s |    640     |   18.1  |  7.93  |  17.36   |   208.3   |  333.3   | [model](https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_s_obj365_pretrained.pdparams) | [config](./objects365/ppyoloe_plus_crn_s_60e_objects365.yml) |
|   PP-YOLOE+_m   |  60   |   4 |  8     |    8     | cspresnet-m |    640     |   25.0  |  23.43  |  49.91   |   123.4       |  208.3  | [model](https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_m_obj365_pretrained.pdparams) | [config](./objects365/ppyoloe_plus_crn_m_60e_objects365.yml) |
|   PP-YOLOE+_l   |  60   |   3 |  8     |    8     | cspresnet-l |    640     |   30.8  |  52.20 |  110.07  |   78.1    |  149.2   | [model](https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_l_obj365_pretrained.pdparams) | [config](./objects365/ppyoloe_plus_crn_l_60e_objects365.yml) |
|   PP-YOLOE+_x   |  60   |  4 |   8     |    8     | cspresnet-x |    640     |   32.7  |  98.42 |  206.59      |   45.0        |  95.2  | [model](https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_x_obj365_pretrained.pdparams) | [config](./objects365/ppyoloe_plus_crn_x_60e_objects365.yml) |


**Notes:**
- The Details for multiple machine and multi-gpu training, see [DistributedTraining](../../docs/tutorials/DistributedTraining_en.md)
- For Objects365 dataset download, please refer to [objects365 official website](http://www.objects365.org/overview.html). The specific category list can be downloaded from [objects365_detection_label_list.txt](https://bj.bcebos.com/v1/paddledet/data/objects365/objects365_detection_label_list.txt) organized by PaddleDetection team. It should be stored in `dataset/objects365/`, and each line represents one category. The categories need to be read when exporting the model or doing inference. If the json file is not exist, you can make the following changes to `configs/datasets/objects365_detection.yml`:
```
TestDataset:
  !ImageFolder
    # anno_path: annotations/zhiyuan_objv2_val.json
    anno_path: objects365_detection_label_list.txt
    dataset_dir: dataset/objects365/
```


### Model Zoo on VOC

|     Model      | Epoch | GPU number | images/GPU |  backbone  | input shape | Box AP<sup>0.5 | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) |  download  |  config  |
|:---------------:|:-----:|:-----------:|:-----------:|:---------:|:----------:|:--------------:|:---------:|:---------:|:-------------:|:-----------------------:| :-------: |:--------:|
|   PP-YOLOE+_s   |  30   |     8     |    8     | cspresnet-s |    640     |   86.7  |  7.93  |  17.36   |   208.3   |  333.3   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_30e_voc.pdparams) | [config](./voc/ppyoloe_plus_crn_s_30e_voc.yml) |
|   PP-YOLOE+_l   |  30   |     8     |    8     | cspresnet-l |    640     |   89.0  |  52.20 |  110.07  |   78.1    |  149.2   | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_30e_voc.pdparams) | [config](./voc/ppyoloe_plus_crn_l_30e_voc.yml) |


### Feature Models

The PaddleDetection team provides configs and weights of various feature detection models based on PP-YOLOE, which users can download for use:

|Scenarios | Related Datasets | Links|
| :--------: | :---------: | :------: |
|Pedestrian Detection | CrowdHuman | [pphuman](../pphuman) |
|Vehicle Detection | BDD100K, UA-DETRAC | [ppvehicle](../ppvehicle) |
|Small Object Detection | VisDrone、DOTA、xView |  [smalldet](../smalldet) |
|Densely Packed Object Detection | SKU110k | [application](./application) |
|Rotated Object Detection | DOTA | [PP-YOLOE-R](../rotate/ppyoloe_r/) |


## Getting Start

### Datasets and Metrics

PaddleDetection team provides **COCO and VOC dataset** , decompress and place it under `PaddleDetection/dataset/`:

```
wget https://bj.bcebos.com/v1/paddledet/data/coco.tar
# tar -xvf coco.tar

wget https://bj.bcebos.com/v1/paddledet/data/voc.zip
# unzip voc.zip
```

**Note:**
  - For the format of COCO style dataset, please refer to [format-data](https://cocodataset.org/#format-data) and [format-results](https://cocodataset.org/#format-results).
  - For the evaluation metric of COCO, please refer to [detection-eval](https://cocodataset.org/#detection-eval), and install  [cocoapi](https://github.com/cocodataset/cocoapi) at first.
  - For the evaluation metric of VOC, please refer to [VOC2012](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html).

### Custom dataset

1.For the annotation of custom dataset, please refer to [DetAnnoTools](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/docs/tutorials/data/DetAnnoTools_en.md);

2.For training preparation of custom dataset，please refer to [PrepareDataSet](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/docs/tutorials/data/PrepareDetDataSet_en.md).


### Training

Training PP-YOLOE+ on 8 GPUs with following command

```bash
python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml --eval --amp
```

**Notes:**
- If you need to evaluate while training, please add `--eval`.
- PP-YOLOE+ supports mixed precision training, please add `--amp`.
- PaddleDetection supports multi-machine distributed training, you can refer to [DistributedTraining tutorial](../../docs/tutorials/DistributedTraining_en.md).


### Evaluation

Evaluating PP-YOLOE+ on COCO val2017 dataset in single GPU with following commands:

```bash
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams
```

For evaluation on COCO test-dev2017 dataset, please download COCO test-dev2017 dataset from [COCO dataset download](https://cocodataset.org/#download) and decompress to COCO dataset directory and configure `EvalDataset` like `configs/ppyolo/ppyolo_test.yml`.

### Inference

Inference images in single GPU with following commands, use `--infer_img` to inference a single image and `--infer_dir` to inference all images in the directory.

```bash
# inference single image
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams --infer_img=demo/000000014439_640x640.jpg

# inference all images in the directory
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams --infer_dir=demo
```

### Exporting models

For deployment on GPU or speed testing, model should be first exported to inference model using `tools/export_model.py`.

**Exporting PP-YOLOE+ for Paddle Inference without TensorRT**, use following command

```bash
python tools/export_model.py -c configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams
```

**Exporting PP-YOLOE+ for Paddle Inference with TensorRT** for better performance, use following command with extra `-o trt=True` setting.

```bash
python tools/export_model.py -c configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams trt=True
```

If you want to export PP-YOLOE model to **ONNX format**, use following command refer to [PaddleDetection Model Export as ONNX Format Tutorial](../../deploy/EXPORT_ONNX_MODEL_en.md).

```bash
# export inference model
python tools/export_model.py -c configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml --output_dir=output_inference -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams trt=True

# install paddle2onnx
pip install paddle2onnx

# convert to onnx
paddle2onnx --model_dir output_inference/ppyoloe_plus_crn_l_80e_coco --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 11 --save_file ppyoloe_plus_crn_l_80e_coco.onnx

```

**Notes:** ONNX model only supports batch_size=1 now

### Speed testing

For fair comparison, the speed in [Model Zoo](#Model-Zoo) do not contains the time cost of data reading and post-processing(NMS), which is same as [YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet) in testing method. Thus, you should export model with extra `-o exclude_nms=True` setting.

**Using Paddle Inference without TensorRT** to test speed, run following command

```bash
# export inference model
python tools/export_model.py -c configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams exclude_nms=True

# speed testing with run_benchmark=True
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_file=demo/000000014439_640x640.jpg --run_mode=paddle --device=gpu --run_benchmark=True
```

**Using Paddle Inference with TensorRT** to test speed, run following command

```bash
# export inference model with trt=True
python tools/export_model.py -c configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams exclude_nms=True trt=True

# speed testing with run_benchmark=True,run_mode=trt_fp32/trt_fp16
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_file=demo/000000014439_640x640.jpg --run_mode=trt_fp16 --device=gpu --run_benchmark=True

```

**Using TensorRT Inference with ONNX** to test speed, run following command

```bash
# export inference model with trt=True
python tools/export_model.py -c configs/ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco.pdparams exclude_nms=True trt=True

# convert to onnx
paddle2onnx --model_dir output_inference/ppyoloe_plus_crn_s_80e_coco --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 12 --save_file ppyoloe_plus_crn_s_80e_coco.onnx

# trt inference using fp16 and batch_size=1
trtexec --onnx=./ppyoloe_plus_crn_s_80e_coco.onnx --saveEngine=./ppyoloe_s_bs1.engine --workspace=1024 --avgRuns=1000 --shapes=image:1x3x640x640,scale_factor:1x2 --fp16

# trt inference using fp16 and batch_size=32
trtexec --onnx=./ppyoloe_plus_crn_s_80e_coco.onnx --saveEngine=./ppyoloe_s_bs32.engine --workspace=1024 --avgRuns=1000 --shapes=image:32x3x640x640,scale_factor:32x2 --fp16

# Using the above script, T4 and tensorrt 7.2 machine, the speed of PPYOLOE-s model is as follows,

# batch_size=1, 2.80ms, 357fps
# batch_size=32, 67.69ms, 472fps

```


### Deployment

PP-YOLOE can be deployed by following approaches:
  - Paddle Inference [Python](../../deploy/python) & [C++](../../deploy/cpp)
  - [Paddle-TensorRT](../../deploy/TENSOR_RT.md)
  - [PaddleServing](https://github.com/PaddlePaddle/Serving)
  - [PaddleSlim](../slim)

Next, we will introduce how to use Paddle Inference to deploy PP-YOLOE models in TensorRT FP16 mode.

First, refer to [Paddle Inference Docs](https://www.paddlepaddle.org.cn/inference/master/user_guides/download_lib.html#python), download and install packages corresponding to CUDA, CUDNN and TensorRT version.

Then, Exporting PP-YOLOE for Paddle Inference **with TensorRT**, use following command.

```bash
python tools/export_model.py -c configs/ppyoloe/ppyoloe_plus_crn_l_80e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_80e_coco.pdparams trt=True
```

Finally, inference in TensorRT FP16 mode.

```bash
# inference single image
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_mode=trt_fp16

# inference all images in the directory
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_crn_l_80e_coco --image_dir=demo/ --device=gpu  --run_mode=trt_fp16

```

**Notes:**
- TensorRT will perform optimization for the current hardware platform according to the definition of the network, generate an inference engine and serialize it into a file. This inference engine is only applicable to the current hardware hardware platform. If your hardware and software platform has not changed, you can set `use_static=True` in [enable_tensorrt_engine](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/deploy/python/infer.py#L660). In this way, the serialized file generated will be saved in the `output_inference` folder, and the saved serialized file will be loaded the next time when TensorRT is executed.
- PaddleDetection release/2.4 and later versions will support NMS calling TensorRT, which requires PaddlePaddle release/2.3 and later versions.

### Other Datasets

Model | AP | AP<sub>50</sub>
---|---|---
[YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) | 22.6 | 37.5
[YOLOv5](https://github.com/ultralytics/yolov5) | 26.0 | 42.7
**PP-YOLOE** | **30.5** | **46.4**

**Notes**
- Here, we use [VisDrone](https://github.com/VisDrone/VisDrone-Dataset) dataset, and to detect 9 objects including `person, bicycles, car, van, truck, tricycle, awning-tricycle, bus, motor`.
- Above models trained using official default config, and load pretrained parameters on COCO dataset.
- *Due to the limited time, more verification results will be supplemented in the future. You are also welcome to contribute to PP-YOLOE*


## Appendix

Ablation experiments of PP-YOLOE.

| NO.  |        Model                 | Box AP<sup>val</sup> | Params(M) | FLOPs(G) | V100 FP32 FPS |
| :--: | :---------------------------: | :------------------: | :-------: | :------: | :-----------: |
|  A   | PP-YOLOv2          |         49.1         |   54.58   |  115.77   |     68.9     |
|  B   | A + Anchor-free    |         48.8         |   54.27   |  114.78   |     69.8     |
|  C   | B + CSPRepResNet   |         49.5         |   47.42   |  101.87   |     85.5     |
|  D   | C + TAL            |         50.4         |   48.32   |  104.75   |     84.0     |
|  E   | D + ET-Head        |         50.9         |   52.20   |  110.07   |     78.1     |