README.md

# PP-PicoDet

![](../../docs/images/picedet_demo.jpeg)
## Introduction

We developed a series of lightweight models, named `PP-PicoDet`. Because of the excellent performance, our models are very suitable for deployment on mobile or CPU. For more details, please refer to our [report on arXiv](https://arxiv.org/abs/2111.00902).

- 🌟 Higher mAP: the **first** object detectors that surpass mAP(0.5:0.95) **30+** within 1M parameters when the input size is 416.
- 🚀 Faster latency: 150FPS on mobile ARM CPU.
- 😊 Deploy friendly: support PaddleLite/MNN/NCNN/OpenVINO and provide C++/Python/Android implementation.
- 😍 Advanced algorithm: use the most advanced algorithms and offer innovation, such as ESNet, CSP-PAN, SimOTA with VFL, etc.


<div align="center">
  <img src="../../docs/images/picodet_map.png" width='600'/>
</div>

### Comming Soon
- [ ] More series of model, such as smaller or larger model.
- [ ] Pretrained models for more scenarios.
- [ ] More features in need.

## Requirements
- PaddlePaddle >= 2.1.2

## Benchmark

| Model     | Input size | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params<br><sup>(M) | FLOPS<br><sup>(G) | Latency<sup><small>[NCNN](#latency)</small><sup><br><sup>(ms) | Latency<sup><small>[Lite](#latency)</small><sup><br><sup>(ms) |  download  | config |
| :-------- | :--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :-----------------------------: | :-----------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------ |
| PicoDet-S |  320*320   |          27.1           |        41.4        |        0.99        |       0.73        |              8.13               |            **6.65**             | [model](https://paddledet.bj.bcebos.com/models/picodet_s_320_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_s_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/picodet/picodet_s_320_coco.yml) |
| PicoDet-S |  416*416   |          30.6           |        45.5        |        0.99        |       1.24        |              12.37              |            **9.82**             | [model](https://paddledet.bj.bcebos.com/models/picodet_s_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_s_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/picodet/picodet_s_416_coco.yml) |
| PicoDet-M |  320*320   |          30.9           |        45.7        |        2.15        |       1.48        |              11.27              |            **9.61**             | [model](https://paddledet.bj.bcebos.com/models/picodet_m_320_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/picodet/picodet_m_320_coco.yml) |
| PicoDet-M |  416*416   |          34.3           |        49.8        |        2.15        |       2.50        |              17.39              |            **15.88**            | [model](https://paddledet.bj.bcebos.com/models/picodet_m_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/picodet/picodet_m_416_coco.yml) |
| PicoDet-L |  320*320   |          32.9           |        48.2        |        3.30        |       2.23        |              15.26              |            **13.42**            | [model](https://paddledet.bj.bcebos.com/models/picodet_l_320_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_320_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/picodet/picodet_l_320_coco.yml) |
| PicoDet-L |  416*416   |          36.6           |        52.5        |        3.30        |       3.76        |              23.36              |            **21.85**            | [model](https://paddledet.bj.bcebos.com/models/picodet_l_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/picodet/picodet_l_416_coco.yml) |
| PicoDet-L |  640*640   |          40.9           |        57.6        |        3.30        |       8.91        |              54.11              |            **50.55**            | [model](https://paddledet.bj.bcebos.com/models/picodet_l_640_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_640_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/picodet/picodet_l_640_coco.yml) |

#### More Configs

| Model     | Input size | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params<br><sup>(M) | FLOPS<br><sup>(G) | Latency<sup><small>[NCNN](#latency)</small><sup><br><sup>(ms) | Latency<sup><small>[Lite](#latency)</small><sup><br><sup>(ms) |  download  | config |
| :--------------------------- | :--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :-----------------------------: | :-----------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------- |
| PicoDet-Shufflenetv2 1x      |  416*416   |          30.0           |        44.6        |        1.17        |       1.53        |              15.06              |            **10.63**            |      [model](https://paddledet.bj.bcebos.com/models/picodet_shufflenetv2_1x_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_shufflenetv2_1x_416_coco.log)      | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/picodet/more_config/picodet_shufflenetv2_1x_416_coco.yml)      |
| PicoDet-MobileNetv3-large 1x |  416*416   |          35.6           |        52.0        |        3.55        |       2.80        |              20.71              |            **17.88**            | [model](https://paddledet.bj.bcebos.com/models/picodet_mobilenetv3_large_1x_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_mobilenetv3_large_1x_416_coco.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/picodet/more_config/picodet_mobilenetv3_large_1x_416_coco.yml) |
| PicoDet-LCNet 1.5x           |  416*416   |          36.3           |        52.2        |        3.10        |       3.85        |              21.29              |            **20.8**             |           [model](https://paddledet.bj.bcebos.com/models/picodet_lcnet_1_5x_416_coco.pdparams) &#124; [log](https://paddledet.bj.bcebos.com/logs/train_picodet_lcnet_1_5x_416_coco.log)           | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.3/configs/picodet/more_config/picodet_lcnet_1_5x_416_coco.yml)           |

<details open>
<summary><b>Table Notes:</b></summary>

- <a name="latency">Latency:</a> All our models test on `Qualcomm Snapdragon 865(4xA77+4xA55)` with 4 threads by arm8 and with FP16. In the above table, test latency on [NCNN](https://github.com/Tencent/ncnn) and `Lite`->[Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite).  And testing latency with code: [MobileDetBenchmark](https://github.com/JiweiMaster/MobileDetBenchmark).
- PicoDet is trained on COCO train2017 dataset and evaluated on COCO val2017.
- PicoDet used 4 or 8 GPUs for training and all checkpoints are trained with default settings and hyperparameters.

</details>

#### Benchmark of Other Models

| Model     | Input size | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params<br><sup>(M) | FLOPS<br><sup>(G) | Latency<sup><small>[NCNN](#latency)</small><sup><br><sup>(ms) |
| :-------- | :--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :-----------------------------: |
| YOLOv3-Tiny |  416*416   |          16.6           |        33.1      |        8.86        |       5.62        |             25.42               |
| YOLOv4-Tiny |  416*416   |          21.7           |        40.2        |        6.06           |       6.96           |             23.69               |
| PP-YOLO-Tiny |  320*320       |          20.6         |        -              |   1.08             |    0.58             |    6.75                           |  
| PP-YOLO-Tiny |  416*416   |          22.7          |    -               |    1.08               |    1.02             |    10.48                          |  
| Nanodet-M |  320*320      |          20.6            |    -               |    0.95               |    0.72             |    8.71                           |  
| Nanodet-M |  416*416   |          23.5             |    -               |    0.95               |    1.2              |  13.35                          |
| Nanodet-M 1.5x |  416*416   |          26.8        |    -                  | 2.08               |    2.42             |    15.83                          |
| YOLOX-Nano     |  416*416   |          25.8          |    -               |    0.91               |    1.08             |    19.23                          |
| YOLOX-Tiny     |  416*416   |          32.8          |    -               |    5.06               |    6.45             |    32.77                          |
| YOLOv5n |  640*640       |          28.4             |    46.0            |    1.9                |    4.5              |    40.35                          |
| YOLOv5s |  640*640       |          37.2             |    56.0            |    7.2                |    16.5             |    78.05                          |


## Deployment

### Export and Convert Model

<details>
<summary>1. Export model (click to expand)</summary>

```shell
cd PaddleDetection
python tools/export_model.py -c configs/picodet/picodet_s_320_coco.yml \
              -o weights=https://paddledet.bj.bcebos.com/models/picodet_s_320_coco.pdparams --output_dir=inference_model
```

</details>

<details>
<summary>2. Convert to PaddleLite (click to expand)</summary>

- Install Paddlelite>=2.10.rc:

```shell
pip install paddlelite
```

- Convert model:

```shell
# FP32
paddle_lite_opt --model_dir=inference_model/picodet_s_320_coco --valid_targets=arm --optimize_out=picodet_s_320_coco_fp32
# FP16
paddle_lite_opt --model_dir=inference_model/picodet_s_320_coco --valid_targets=arm --optimize_out=picodet_s_320_coco_fp16 --enable_fp16=true
```

</details>

<details>
<summary>3. Convert to ONNX (click to expand)</summary>

- Install [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX) >= 0.7 and ONNX > 1.10.1, for details, please refer to [Tutorials of Export ONNX Model](../../deploy/EXPORT_ONNX_MODEL.md)

```shell
pip install onnx
pip install paddle2onnx
```

- Convert model:

```shell
paddle2onnx --model_dir output_inference/picodet_s_320_coco/ \
            --model_filename model.pdmodel  \
            --params_filename model.pdiparams \
            --opset_version 11 \
            --save_file picodet_s_320_coco.onnx
```

- Simplify ONNX model: use onnx-simplifier to simplify onnx model.

  - Install onnx-simplifier >= 0.3.6:
  ```shell
  pip install onnx-simplifier
  ```
  - simplify onnx model:
  ```shell
  python -m onnxsim picodet_s_320_coco.onnx picodet_s_processed.onnx
  ```

</details>

### Deploy

- PaddleInference demo [Python](../../deploy/python) & [C++](../../deploy/cpp)
- [PaddleLite C++ demo](../../deploy/lite)
- [NCNN C++/Python demo](../../deploy/third_engine/demo_ncnn)
- [MNN C++/Python demo](../../deploy/third_engine/demo_mnn)
- [OpenVINO C++/Python demo](../../deploy/third_engine/demo_openvino)
- [Android demo](https://github.com/JiweiMaster/PP-PicoDet-Android-Demo)


Android demo visualization:
<div align="center">
  <img src="../../docs/images/picodet_android_demo1.jpg" height="500px" ><img src="../../docs/images/picodet_android_demo2.jpg" height="500px" ><img src="../../docs/images/picodet_android_demo3.jpg" height="500px" ><img src="../../docs/images/picodet_android_demo4.jpg" height="500px" >
</div>


## Quantization

<details open>
<summary>Requirements:</summary>

- PaddlePaddle >= 2.2.0rc0
- PaddleSlim >= 2.2.0rc0

**Install:**

```shell
pip install paddleslim==2.2.0rc0
```

</details>

<details>
<summary>Quant aware (click to expand)</summary>

Configure the quant config and start training:

```shell
python tools/train.py -c configs/picodet/picodet_s_320_coco.yml \
          --slim_config configs/slim/quant/picodet_s_quant.yml --eval
```

</details>

<details>
<summary>Post quant (click to expand)</summary>

Configure the post quant config and start calibrate model:

```shell
python tools/post_quant.py -c configs/picodet/picodet_s_320_coco.yml \
          --slim_config configs/slim/post_quant/picodet_s_ptq.yml
```

- Notes: Now the accuracy of post quant is abnormal and this problem is being solved.

</details>

## Cite PP-PiocDet
If you use PiocDet in your research, please cite our work by using the following BibTeX entry:
```
@misc{yu2021pppicodet,
      title={PP-PicoDet: A Better Real-Time Object Detector on Mobile Devices},
      author={Guanghua Yu and Qinyao Chang and Wenyu Lv and Chang Xu and Cheng Cui and Wei Ji and Qingqing Dang and Kaipeng Deng and Guanzhong Wang and Yuning Du and Baohua Lai and Qiwen Liu and Xiaoguang Hu and Dianhai Yu and Yanjun Ma},
      year={2021},
      eprint={2111.00902},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

```