更换文档检测模型
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简体中文 | [English](README_en.md)
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# PP-PicoDet
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## 最新动态
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- 发布PicoDet-NPU模型，支持模型全量化部署。详情请参考[PicoDet全量化示例](./FULL_QUANTIZATION.md) **（2022.08.10）**
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- 发布全新系列PP-PicoDet模型：**（2022.03.20）**
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- (1)引入TAL及ETA Head，优化PAN等结构，精度提升2个点以上；
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- (2)优化CPU端预测速度，同时训练速度提升一倍；
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- (3)导出模型将后处理包含在网络中，预测直接输出box结果，无需二次开发，迁移成本更低，端到端预测速度提升10%-20%。
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## 历史版本模型
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- 详情请参考：[PicoDet 2021.10版本](./legacy_model/)
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## 简介
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PaddleDetection中提出了全新的轻量级系列模型`PP-PicoDet`，在移动端具有卓越的性能，成为全新SOTA轻量级模型。详细的技术细节可以参考我们的[arXiv技术报告](https://arxiv.org/abs/2111.00902)。
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PP-PicoDet模型有如下特点：
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- 🌟 更高的mAP: 第一个在1M参数量之内`mAP(0.5:0.95)`超越**30+**(输入416像素时)。
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- 🚀 更快的预测速度: 网络预测在ARM CPU下可达150FPS。
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- 😊 部署友好: 支持PaddleLite/MNN/NCNN/OpenVINO等预测库，支持转出ONNX，提供了C++/Python/Android的demo。
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- 😍 先进的算法: 我们在现有SOTA算法中进行了创新, 包括：ESNet, CSP-PAN, SimOTA等等。
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<div align="center">
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<img src="../../docs/images/picodet_map.png" width='600'/>
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</div>
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## 基线
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| 模型 | 输入尺寸 | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | 参数量<br><sup>(M) | FLOPS<br><sup>(G) | 预测时延<sup><small>[CPU](#latency)</small><sup><br><sup>(ms) | 预测时延<sup><small>[Lite](#latency)</small><sup><br><sup>(ms) | 权重下载 | 配置文件 | 导出模型 |
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| :-------- | :--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :-----------------------------: | :-----------------------------: | :----------------------------------------: | :--------------------------------------- | :--------------------------------------- |
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| PicoDet-XS | 320*320 | 23.5 | 36.1 | 0.70 | 0.67 | 3.9ms | 7.81ms | [model](https://paddledet.bj.bcebos.com/models/picodet_xs_320_coco_lcnet.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_xs_320_coco_lcnet.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_xs_320_coco_lcnet.yml) | [w/ 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_xs_320_coco_lcnet.tar) | [w/o 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_xs_320_coco_lcnet_non_postprocess.tar) |
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| PicoDet-XS | 416*416 | 26.2 | 39.3 | 0.70 | 1.13 | 6.1ms | 12.38ms | [model](https://paddledet.bj.bcebos.com/models/picodet_xs_416_coco_lcnet.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_xs_416_coco_lcnet.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_xs_416_coco_lcnet.yml) | [w/ 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_xs_416_coco_lcnet.tar) | [w/o 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_xs_416_coco_lcnet_non_postprocess.tar) |
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| PicoDet-S | 320*320 | 29.1 | 43.4 | 1.18 | 0.97 | 4.8ms | 9.56ms | [model](https://paddledet.bj.bcebos.com/models/picodet_s_320_coco_lcnet.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_s_320_coco_lcnet.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_s_320_coco_lcnet.yml) | [w/ 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_s_320_coco_lcnet.tar) | [w/o 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_s_320_coco_lcnet_non_postprocess.tar) |
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| PicoDet-S | 416*416 | 32.5 | 47.6 | 1.18 | 1.65 | 6.6ms | 15.20ms | [model](https://paddledet.bj.bcebos.com/models/picodet_s_416_coco_lcnet.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_s_416_coco_lcnet.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_s_416_coco_lcnet.yml) | [w/ 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_s_416_coco_lcnet.tar) | [w/o 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_s_416_coco_lcnet_non_postprocess.tar) |
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| PicoDet-M | 320*320 | 34.4 | 50.0 | 3.46 | 2.57 | 8.2ms | 17.68ms | [model](https://paddledet.bj.bcebos.com/models/picodet_m_320_coco_lcnet.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_320_coco_lcnet.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_m_320_coco_lcnet.yml) | [w/ 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_m_320_coco_lcnet.tar) | [w/o 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_m_320_coco_lcnet_non_postprocess.tar) |
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| PicoDet-M | 416*416 | 37.5 | 53.4 | 3.46 | 4.34 | 12.7ms | 28.39ms | [model](https://paddledet.bj.bcebos.com/models/picodet_m_416_coco_lcnet.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_m_416_coco_lcnet.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_m_416_coco_lcnet.yml) | [w/ 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_m_416_coco_lcnet.tar) | [w/o 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_m_416_coco_lcnet_non_postprocess.tar) |
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| PicoDet-L | 320*320 | 36.1 | 52.0 | 5.80 | 4.20 | 11.5ms | 25.21ms | [model](https://paddledet.bj.bcebos.com/models/picodet_l_320_coco_lcnet.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_320_coco_lcnet.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_l_320_coco_lcnet.yml) | [w/ 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_l_320_coco_lcnet.tar) | [w/o 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_l_320_coco_lcnet_non_postprocess.tar) |
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| PicoDet-L | 416*416 | 39.4 | 55.7 | 5.80 | 7.10 | 20.7ms | 42.23ms | [model](https://paddledet.bj.bcebos.com/models/picodet_l_416_coco_lcnet.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_416_coco_lcnet.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_l_416_coco_lcnet.yml) | [w/ 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_l_416_coco_lcnet.tar) | [w/o 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_l_416_coco_lcnet_non_postprocess.tar) |
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| PicoDet-L | 640*640 | 42.6 | 59.2 | 5.80 | 16.81 | 62.5ms | 108.1ms | [model](https://paddledet.bj.bcebos.com/models/picodet_l_640_coco_lcnet.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_l_640_coco_lcnet.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_l_640_coco_lcnet.yml) | [w/ 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_l_640_coco_lcnet.tar) | [w/o 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_l_640_coco_lcnet_non_postprocess.tar) |
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- 特色模型
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| 模型 | 输入尺寸 | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | 参数量<br><sup>(M) | FLOPS<br><sup>(G) | 预测时延<sup><small>[CPU](#latency)</small><sup><br><sup>(ms) | 预测时延<sup><small>[Lite](#latency)</small><sup><br><sup>(ms) | 权重下载 | 配置文件 |
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| :-------- | :--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :-----------------------------: | :-----------------------------: | :----------------------------------------: | :--------------------------------------- |
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| PicoDet-S-NPU | 416*416 | 30.1 | 44.2 | - | - | - | - | [model](https://paddledet.bj.bcebos.com/models/picodet_s_416_coco_npu.pdparams) | [log](https://paddledet.bj.bcebos.com/logs/train_picodet_s_416_coco_npu.log) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/picodet_s_416_coco_npu.yml) |
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<details open>
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<summary><b>注意事项:</b></summary>
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- <a name="latency">时延测试：</a> 我们所有的模型都在`英特尔酷睿i7 10750H`的CPU 和`骁龙865(4xA77+4xA55)`的ARM CPU上测试(4线程，FP16预测)。上面表格中标有`CPU`的是使用OpenVINO测试，标有`Lite`的是使用[Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite)进行测试。
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- PicoDet在COCO train2017上训练，并且在COCO val2017上进行验证。使用4卡GPU训练，并且上表所有的预训练模型都是通过发布的默认配置训练得到。
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- Benchmark测试：测试速度benchmark性能时，导出模型后处理不包含在网络中，需要设置`-o export.benchmark=True` 或手动修改[runtime.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/runtime.yml#L12)。
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</details>
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#### 其他模型的基线
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| 模型 | 输入尺寸 | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | 参数量<br><sup>(M) | FLOPS<br><sup>(G) | 预测时延<sup><small>[NCNN](#latency)</small><sup><br><sup>(ms) |
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| :-------- | :--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :-----------------------------: |
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| YOLOv3-Tiny | 416*416 | 16.6 | 33.1 | 8.86 | 5.62 | 25.42 |
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| YOLOv4-Tiny | 416*416 | 21.7 | 40.2 | 6.06 | 6.96 | 23.69 |
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| PP-YOLO-Tiny | 320*320 | 20.6 | - | 1.08 | 0.58 | 6.75 |
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| PP-YOLO-Tiny | 416*416 | 22.7 | - | 1.08 | 1.02 | 10.48 |
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| Nanodet-M | 320*320 | 20.6 | - | 0.95 | 0.72 | 8.71 |
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| Nanodet-M | 416*416 | 23.5 | - | 0.95 | 1.2 | 13.35 |
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| Nanodet-M 1.5x | 416*416 | 26.8 | - | 2.08 | 2.42 | 15.83 |
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| YOLOX-Nano | 416*416 | 25.8 | - | 0.91 | 1.08 | 19.23 |
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| YOLOX-Tiny | 416*416 | 32.8 | - | 5.06 | 6.45 | 32.77 |
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| YOLOv5n | 640*640 | 28.4 | 46.0 | 1.9 | 4.5 | 40.35 |
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| YOLOv5s | 640*640 | 37.2 | 56.0 | 7.2 | 16.5 | 78.05 |
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- ARM测试的benchmark脚本来自: [MobileDetBenchmark](https://github.com/JiweiMaster/MobileDetBenchmark)。
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## 快速开始
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<details open>
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<summary>依赖包:</summary>
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- PaddlePaddle == 2.2.2
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</details>
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<details>
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<summary>安装</summary>
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- [安装指导文档](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/INSTALL.md)
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- [准备数据文档](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/data/PrepareDataSet_en.md)
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</details>
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<details>
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<summary>训练&评估</summary>
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- 单卡GPU上训练:
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```shell
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# training on single-GPU
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export CUDA_VISIBLE_DEVICES=0
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python tools/train.py -c configs/picodet/picodet_s_320_coco_lcnet.yml --eval
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```
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**注意：**如果训练时显存out memory，将TrainReader中batch_size调小，同时LearningRate中base_lr等比例减小。同时我们发布的config均由4卡训练得到，如果改变GPU卡数为1，那么base_lr需要减小4倍。
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- 多卡GPU上训练:
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```shell
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# training on multi-GPU
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export CUDA_VISIBLE_DEVICES=0,1,2,3
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python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/picodet/picodet_s_320_coco_lcnet.yml --eval
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```
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**注意：**PicoDet所有模型均由4卡GPU训练得到，如果改变训练GPU卡数，需要按线性比例缩放学习率base_lr。
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- 评估:
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```shell
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python tools/eval.py -c configs/picodet/picodet_s_320_coco_lcnet.yml \
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-o weights=https://paddledet.bj.bcebos.com/models/picodet_s_320_coco_lcnet.pdparams
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```
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- 测试:
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```shell
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python tools/infer.py -c configs/picodet/picodet_s_320_coco_lcnet.yml \
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-o weights=https://paddledet.bj.bcebos.com/models/picodet_s_320_coco_lcnet.pdparams
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```
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详情请参考[快速开始文档](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/docs/tutorials/GETTING_STARTED.md).
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</details>
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## 部署
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### 导出及转换模型
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<details open>
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<summary>1. 导出模型</summary>
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```shell
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cd PaddleDetection
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python tools/export_model.py -c configs/picodet/picodet_s_320_coco_lcnet.yml \
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-o weights=https://paddledet.bj.bcebos.com/models/picodet_s_320_coco_lcnet.pdparams \
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--output_dir=output_inference
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```
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- 如无需导出后处理，请指定：`-o export.post_process=False`（如果-o已出现过，此处删掉-o）或者手动修改[runtime.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/runtime.yml) 中相应字段。
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- 如无需导出NMS，请指定：`-o export.nms=False`或者手动修改[runtime.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/runtime.yml) 中相应字段。 许多导出至ONNX场景只支持单输入及固定shape输出，所以如果导出至ONNX，推荐不导出NMS。
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</details>
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<details>
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<summary>2. 转换模型至Paddle Lite (点击展开)</summary>
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- 安装Paddlelite>=2.10:
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```shell
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pip install paddlelite
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```
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- 转换模型至Paddle Lite格式：
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```shell
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# FP32
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paddle_lite_opt --model_dir=output_inference/picodet_s_320_coco_lcnet --valid_targets=arm --optimize_out=picodet_s_320_coco_fp32
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# FP16
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paddle_lite_opt --model_dir=output_inference/picodet_s_320_coco_lcnet --valid_targets=arm --optimize_out=picodet_s_320_coco_fp16 --enable_fp16=true
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```
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</details>
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<details>
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<summary>3. 转换模型至ONNX (点击展开)</summary>
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- 安装[Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX) >= 0.7 并且 ONNX > 1.10.1, 细节请参考[导出ONNX模型教程](../../deploy/EXPORT_ONNX_MODEL.md)
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```shell
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pip install onnx
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pip install paddle2onnx==0.9.2
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```
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- 转换模型:
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```shell
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paddle2onnx --model_dir output_inference/picodet_s_320_coco_lcnet/ \
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--model_filename model.pdmodel \
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--params_filename model.pdiparams \
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--opset_version 11 \
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--save_file picodet_s_320_coco.onnx
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```
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- 简化ONNX模型: 使用`onnx-simplifier`库来简化ONNX模型。
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- 安装 onnxsim >= 0.4.1:
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```shell
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pip install onnxsim
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```
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- 简化ONNX模型:
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```shell
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onnxsim picodet_s_320_coco.onnx picodet_s_processed.onnx
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```
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</details>
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- 部署用的模型
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| 模型 | 输入尺寸 | ONNX | Paddle Lite(fp32) | Paddle Lite(fp16) |
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| :-------- | :--------: | :---------------------: | :----------------: | :----------------: |
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| PicoDet-XS | 320*320 | [( w/ 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_xs_320_lcnet_postprocessed.onnx) | [( w/o 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_xs_320_coco_lcnet.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_xs_320_coco_lcnet.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_xs_320_coco_lcnet_fp16.tar) |
|
||||
| PicoDet-XS | 416*416 | [( w/ 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_xs_416_lcnet_postprocessed.onnx) | [( w/o 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_xs_416_coco_lcnet.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_xs_416_coco_lcnet.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_xs_416_coco_lcnet_fp16.tar) |
|
||||
| PicoDet-S | 320*320 | [( w/ 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_s_320_lcnet_postprocessed.onnx) | [( w/o 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_s_320_coco_lcnet.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_s_320_coco_lcnet.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_s_320_coco_lcnet_fp16.tar) |
|
||||
| PicoDet-S | 416*416 | [( w/ 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_s_416_lcnet_postprocessed.onnx) | [( w/o 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_s_416_coco_lcnet.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_s_416_coco_lcnet.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_s_416_coco_lcnet_fp16.tar) |
|
||||
| PicoDet-M | 320*320 | [( w/ 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_m_320_lcnet_postprocessed.onnx) | [( w/o 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_m_320_coco_lcnet.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_m_320_coco_lcnet.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_m_320_coco_lcnet_fp16.tar) |
|
||||
| PicoDet-M | 416*416 | [( w/ 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_m_416_lcnet_postprocessed.onnx) | [( w/o 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_m_416_coco_lcnet.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_m_416_coco_lcnet.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_m_416_coco_lcnet_fp16.tar) |
|
||||
| PicoDet-L | 320*320 | [( w/ 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_l_320_lcnet_postprocessed.onnx) | [( w/o 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_l_320_coco_lcnet.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_l_320_coco_lcnet.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_l_320_coco_lcnet_fp16.tar) |
|
||||
| PicoDet-L | 416*416 | [( w/ 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_l_416_lcnet_postprocessed.onnx) | [( w/o 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_l_416_coco_lcnet.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_l_416_coco_lcnet.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_l_416_coco_lcnet_fp16.tar) |
|
||||
| PicoDet-L | 640*640 | [( w/ 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_l_640_lcnet_postprocessed.onnx) | [( w/o 后处理)](https://paddledet.bj.bcebos.com/deploy/third_engine/picodet_l_640_coco_lcnet.onnx) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_l_640_coco_lcnet.tar) | [model](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_l_640_coco_lcnet_fp16.tar) |
|
||||
|
||||
### 部署
|
||||
|
||||
| 预测库 | Python | C++ | 带后处理预测 |
|
||||
| :-------- | :--------: | :---------------------: | :----------------: |
|
||||
| OpenVINO | [Python](../../deploy/third_engine/demo_openvino/python) | [C++](../../deploy/third_engine/demo_openvino)（带后处理开发中） | ✔︎ |
|
||||
| Paddle Lite | - | [C++](../../deploy/lite) | ✔︎ |
|
||||
| Android Demo | - | [Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite-Demo/tree/develop/object_detection/android/app/cxx/picodet_detection_demo) | ✔︎ |
|
||||
| PaddleInference | [Python](../../deploy/python) | [C++](../../deploy/cpp) | ✔︎ |
|
||||
| ONNXRuntime | [Python](../../deploy/third_engine/demo_onnxruntime) | Coming soon | ✔︎ |
|
||||
| NCNN | Coming soon | [C++](../../deploy/third_engine/demo_ncnn) | ✘ |
|
||||
| MNN | Coming soon | [C++](../../deploy/third_engine/demo_mnn) | ✘ |
|
||||
|
||||
|
||||
|
||||
Android demo可视化：
|
||||
<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>
|
||||
|
||||
|
||||
## 量化
|
||||
|
||||
<details open>
|
||||
<summary>依赖包:</summary>
|
||||
|
||||
- PaddlePaddle >= 2.2.2
|
||||
- PaddleSlim >= 2.2.2
|
||||
|
||||
**安装:**
|
||||
|
||||
```shell
|
||||
pip install paddleslim==2.2.2
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details open>
|
||||
<summary>量化训练</summary>
|
||||
|
||||
开始量化训练:
|
||||
|
||||
```shell
|
||||
python tools/train.py -c configs/picodet/picodet_s_416_coco_lcnet.yml \
|
||||
--slim_config configs/slim/quant/picodet_s_416_lcnet_quant.yml --eval
|
||||
```
|
||||
|
||||
- 更多细节请参考[slim文档](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim)
|
||||
|
||||
</details>
|
||||
|
||||
- 量化训练Model ZOO：
|
||||
|
||||
| 量化模型 | 输入尺寸 | mAP<sup>val<br>0.5:0.95 | Configs | Weight | Inference Model | Paddle Lite(INT8) |
|
||||
| :-------- | :--------: | :--------------------: | :-------: | :----------------: | :----------------: | :----------------: |
|
||||
| PicoDet-S | 416*416 | 31.5 | [config](./picodet_s_416_coco_lcnet.yml) | [slim config](../slim/quant/picodet_s_416_lcnet_quant.yml) | [model](https://paddledet.bj.bcebos.com/models/picodet_s_416_coco_lcnet_quant.pdparams) | [w/ 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_s_416_coco_lcnet_quant.tar) | [w/o 后处理](https://paddledet.bj.bcebos.com/deploy/Inference/picodet_s_416_coco_lcnet_quant_non_postprocess.tar) | [w/ 后处理](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_s_416_coco_lcnet_quant.nb) | [w/o 后处理](https://paddledet.bj.bcebos.com/deploy/paddlelite/picodet_s_416_coco_lcnet_quant_non_postprocess.nb) |
|
||||
|
||||
## 非结构化剪枝
|
||||
|
||||
<details open>
|
||||
<summary>教程:</summary>
|
||||
|
||||
训练及部署细节请参考[非结构化剪枝文档](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/picodet/legacy_model/pruner/README.md)。
|
||||
|
||||
</details>
|
||||
|
||||
## 应用
|
||||
|
||||
- **行人检测：** `PicoDet-S-Pedestrian`行人检测模型请参考[PP-TinyPose](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/keypoint/tiny_pose#%E8%A1%8C%E4%BA%BA%E6%A3%80%E6%B5%8B%E6%A8%A1%E5%9E%8B)
|
||||
|
||||
- **主体检测：** `PicoDet-L-Mainbody`主体检测模型请参考[主体检测文档](./legacy_model/application/mainbody_detection/README.md)
|
||||
|
||||
## FAQ
|
||||
|
||||
<details>
|
||||
<summary>显存爆炸(Out of memory error)</summary>
|
||||
|
||||
请减小配置文件中`TrainReader`的`batch_size`。
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>如何迁移学习</summary>
|
||||
|
||||
请重新设置配置文件中的`pretrain_weights`字段，比如利用COCO上训好的模型在自己的数据上继续训练：
|
||||
```yaml
|
||||
pretrain_weights: https://paddledet.bj.bcebos.com/models/picodet_l_640_coco_lcnet.pdparams
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>`transpose`算子在某些硬件上耗时验证</summary>
|
||||
|
||||
请使用`PicoDet-LCNet`模型，`transpose`较少。
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<details>
|
||||
<summary>如何计算模型参数量。</summary>
|
||||
|
||||
可以将以下代码插入：[trainer.py](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/engine/trainer.py#L141) 来计算参数量。
|
||||
|
||||
```python
|
||||
params = sum([
|
||||
p.numel() for n, p in self.model. named_parameters()
|
||||
if all([x not in n for x in ['_mean', '_variance']])
|
||||
]) # exclude BatchNorm running status
|
||||
print('params: ', params)
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## 引用PP-PicoDet
|
||||
如果需要在你的研究中使用PP-PicoDet，请通过一下方式引用我们的技术报告：
|
||||
```
|
||||
@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}
|
||||
}
|
||||
|
||||
```
|
||||
Reference in New Issue
Block a user