README.md

# PP-PicoDet

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

We developed a series of lightweight models, which named `PP-PicoDet`. Because of its excellent performance, it is very suitable for deployment on mobile or CPU.

- 🌟 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 innovate, such as ESNet, CSP-PAN, SimOTA with VFL, etc.

### 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/develop/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/develop/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/develop/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/develop/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/develop/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/develop/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/develop/configs/picodet/picodet_l_640_coco.yml) |

#### More config

| 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/develop/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/develop/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/develop/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(4\*A77+4\*A55)` 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>


## 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]()

## Slim

### 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 it is being debugged.

</details>

## Cite PiocDet
If you use PiocDet in your research, please cite our work by using the following BibTeX entry:
```
comming soon

```