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简体中文 | [English](README_en.md)
English | [简体中文](README_cn.md)
文档：[https://paddledetection.readthedocs.io](https://paddledetection.readthedocs.io)
Documentation:[https://paddledetection.readthedocs.io](https://paddledetection.readthedocs.io)
# PaddleDetection
飞桨推出的PaddleDetection是端到端目标检测开发套件，旨在帮助开发者更快更好地完成检测模型的训练、精度速度优化到部署全流程。PaddleDetection以模块化的设计实现了多种主流目标检测算法，并且提供了丰富的数据增强、网络组件、损失函数等模块，集成了模型压缩和跨平台高性能部署能力。目前基于PaddleDetection已经完成落地的项目涉及工业质检、遥感图像检测、无人巡检等多个领域。
PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which
aims to help developers in the whole development of training models, optimizing performance and
inference speed, and deploying models. PaddleDetection provides varied object detection architectures
in modular design, and wealthy data augmentation methods, network components, loss functions, etc.
PaddleDetection supported practical projects such as industrial quality inspection, remote sensing
image object detection, and automatic inspection with its practical features such as model compression
and multi-platform deployment.
PaddleDetection新发布精度速度领先的[PP-YOLO](https://arxiv.org/abs/2007.12099)模型，COCO数据集精度达到45.2%，单卡Tesla V100预测速度达到72.9 FPS，详细信息见[PP-YOLO模型](configs/ppyolo/README.md)
[PP-YOLO](https://arxiv.org/abs/2007.12099), which is faster and has higer performance than YOLOv4,
has been released, it reached mAP(0.5:0.95) as 45.2% on COCO test2019 dataset and 72.9 FPS on single
Test V100. Please refer to [PP-YOLO](configs/ppyolo/README.md) for details.
**目前检测库下模型均要求使用PaddlePaddle 1.8及以上版本或适当的develop版本。**
**Now all models in PaddleDetection require PaddlePaddle version 1.8 or higher, or suitable develop version.**
<div align="center">
<img src="docs/images/000000570688.jpg" />
</div>
## 简介
## Introduction
特性：
Features:
- 模型丰富：
- Rich models:
PaddleDetection提供了丰富的模型，包含目标检测、实例分割、人脸检测等100+个预训练模型，涵盖多种数据集竞赛冠军方案、适合云端/边缘端设备部署的检测方案。
PaddleDetection provides rich of models, including 100+ pre-trained models
such as object detection, instance segmentation, face detection etc. It covers
the champion models, the practical detection models for cloud and edge device.
- 易部署:
- Production Ready:
PaddleDetection的模型中使用的核心算子均通过C++或CUDA实现，同时基于PaddlePaddle的高性能推理引擎可以方便地部署在多种硬件平台上。
Key operations are implemented in C++ and CUDA, together with PaddlePaddle's
highly efficient inference engine, enables easy deployment in server environments.
- 高灵活度：
- Highly Flexible:
PaddleDetection通过模块化设计来解耦各个组件，基于配置文件可以轻松地搭建各种检测模型。
Components are designed to be modular. Model architectures, as well as data
preprocess pipelines, can be easily customized with simple configuration
changes.
- 高性能：
- Performance Optimized:
基于PaddlePaddle框架的高性能内核，在模型训练速度、显存占用上有一定的优势。例如，YOLOv3的训练速度快于其他框架，在Tesla V100 16GB环境下，Mask-RCNN(ResNet50)可以单卡Batch Size可以达到4 (甚至到5)。
With the help of the underlying PaddlePaddle framework, faster training and
reduced GPU memory footprint is achieved. Notably, YOLOv3 training is
much faster compared to other frameworks. Another example is Mask-RCNN
(ResNet50), we managed to fit up to 4 images per GPU (Tesla V100 16GB) during
multi-GPU training.
Supported Architectures:
支持的模型结构：
| | ResNet | ResNet-vd <sup>[1](#vd)</sup> | ResNeXt-vd | SENet | MobileNet | HRNet | Res2Net |
| ------------------- | :----: | ----------------------------: | :--------: | :---: | :-------: |:------:|:-----: |
| Faster R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ | ✗ |
| Faster R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ |
| Mask R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ | ✗ |
| Mask R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ |
| Cascade Faster-RCNN | ✓ | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ |
| Cascade Mask-RCNN | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| Libra R-CNN | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| RetinaNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| YOLOv3 | ✓ | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ |
| SSD | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
| BlazeFace | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Faceboxes | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| | ResNet | ResNet-vd <sup>[1](#vd)</sup> | ResNeXt-vd | SENet | MobileNet | HRNet | Res2Net |
|--------------------|:------:|------------------------------:|:----------:|:-----:|:---------:|:------:| :--: |
| Faster R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ | ✗ |
| Faster R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ |
| Mask R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ | ✗ |
| Mask R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ |
| Cascade Faster-RCNN | ✓ | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ |
| Cascade Mask-RCNN | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| Libra R-CNN | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| RetinaNet | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
| YOLOv3 | ✓ | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ |
| SSD | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
| BlazeFace | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Faceboxes | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
<a name="vd">[1]</a> [ResNet-vd](https://arxiv.org/pdf/1812.01187) models offer much improved accuracy with negligible performance cost.
<a name="vd">[1]</a> [ResNet-vd](https://arxiv.org/pdf/1812.01187) 模型预测速度基本不变的情况下提高了精度。
**NOTE:** ✓ for config file and pretrain model provided in [Model Zoo](docs/MODEL_ZOO.md), ✗ for not provided but is supported generally.
**说明：** ✓ 为[模型库](docs/MODEL_ZOO_cn.md)中提供了对应配置文件和预训练模型，✗ 为未提供参考配置，但一般都支持。
更多的模型:
More models:
- EfficientDet
- FCOS
......@@ -65,94 +81,96 @@ PaddleDetection新发布精度速度领先的[PP-YOLO](https://arxiv.org/abs/200
- YOLOv4
- PP-YOLO
更多的Backone：
More Backbones:
- DarkNet
- VGG
- GCNet
- CBNet
- Hourglass
扩展特性：
Advanced Features:
- [x] **Synchronized Batch Norm**
- [x] **Group Norm**
- [x] **Modulated Deformable Convolution**
- [x] **Deformable PSRoI Pooling**
- [x] **Non-local和GCNet**
- [x] **Non-local and GCNet**
**注意:** Synchronized batch normalization 只能在多GPU环境下使用，不能在CPU环境或者单GPU环境下使用。
**NOTE:** Synchronized batch normalization can only be used on multiple GPU devices, can not be used on CPU devices or single GPU device.
以下为选取各模型结构和骨干网络的代表模型COCO数据集精度mAP和单卡Tesla V100上预测速度(FPS)关系图。
The following is the relationship between COCO mAP and FPS on Tesla V100 of representative models of each architectures and backbones.
<div align="center">
<img src="docs/images/map_fps.png" width=800 />
</div>
**说明：**
- `CBResNet`为`Cascade-Faster-RCNN-CBResNet200vd-FPN`模型，COCO数据集mAP高达53.3%
- `Cascade-Faster-RCNN`为`Cascade-Faster-RCNN-ResNet50vd-DCN`，PaddleDetection将其优化到COCO数据mAP为47.8%时推理速度为20FPS
- PaddleDetection增强版`YOLOv3-ResNet50vd-DCN`在COCO数据集mAP高于原作10.6个绝对百分点，推理速度为61.3FPS，快于原作约70%
- 图中模型均可在[模型库](#模型库)中获取
**NOTE:**
- `CBResNet` stands for `Cascade-Faster-RCNN-CBResNet200vd-FPN`, which has highest mAP on COCO as 53.3% in PaddleDetection models
- `Cascade-Faster-RCNN` stands for `Cascade-Faster-RCNN-ResNet50vd-DCN`, which has been optimized to 20 FPS inference speed when COCO mAP as 47.8%
- The enhanced `YOLOv3-ResNet50vd-DCN` is 10.6 absolute percentage points higher than paper on COCO mAP, and inference speed is nearly 70% faster than the darknet framework
- All these models can be get in [Model Zoo](#Model-Zoo)
以下为PaddleDetection发布的精度和预测速度优于YOLOv4模型的PP-YOLO与前沿目标检测算法的COCO数据集精度与单卡Tesla V100预测速度(FPS)关系图， PP-YOLO模型在[COCO](http://cocodataset.org) test2019数据集上精度达到45.2%，在单卡V100上FP32推理速度为72.9 FPS，详细信息见[PP-YOLO模型](configs/ppyolo/README.md)
The following is the relationship between COCO mAP and FPS on Tesla V100 of SOTA object detecters and PP-YOLO, which is faster and has better performance than YOLOv4, and reached mAP(0.5:0.95) as 45.2% on COCO test2019 dataset and 72.9 FPS on single Test V100. Please refer to [PP-YOLO](configs/ppyolo/README.md) for details.
<div align="center">
<img src="docs/images/ppyolo_map_fps.png" width=600 />
</div>
## 文档教程
## Tutorials
### Get Started
### 入门教程
- [Installation guide](docs/tutorials/INSTALL.md)
- [Quick start on small dataset](docs/tutorials/QUICK_STARTED.md)
- [Train/Evaluation/Inference](docs/tutorials/GETTING_STARTED.md)
- [How to train a custom dataset](docs/tutorials/Custom_DataSet.md)
- [FAQ](docs/FAQ.md)
- [安装说明](docs/tutorials/INSTALL_cn.md)
- [快速开始](docs/tutorials/QUICK_STARTED_cn.md)
- [训练/评估/预测流程](docs/tutorials/GETTING_STARTED_cn.md)
- [如何训练自定义数据集](docs/tutorials/Custom_DataSet.md)
- [常见问题汇总](docs/FAQ.md)
### Advanced Tutorial
### 进阶教程
- [数据预处理及数据集定义](docs/advanced_tutorials/READER.md)
- [搭建模型步骤](docs/advanced_tutorials/MODEL_TECHNICAL.md)
- [模型参数配置](docs/advanced_tutorials/config_doc):
- [配置模块设计和介绍](docs/advanced_tutorials/config_doc/CONFIG_cn.md)
- [RCNN模型参数说明](docs/advanced_tutorials/config_doc/RCNN_PARAMS_DOC.md)
- [迁移学习教程](docs/advanced_tutorials/TRANSFER_LEARNING_cn.md)
- [Guide to preprocess pipeline and dataset definition](docs/advanced_tutorials/READER.md)
- [Models technical](docs/advanced_tutorials/MODEL_TECHNICAL.md)
- [Transfer learning document](docs/advanced_tutorials/TRANSFER_LEARNING.md)
- [Parameter configuration](docs/advanced_tutorials/config_doc):
- [Introduction to the configuration workflow](docs/advanced_tutorials/config_doc/CONFIG.md)
- [Parameter configuration for RCNN model](docs/advanced_tutorials/config_doc/RCNN_PARAMS_DOC.md)
- [IPython Notebook demo](demo/mask_rcnn_demo.ipynb)
- [模型压缩](slim)
- [压缩benchmark](slim)
- [量化](slim/quantization)
- [剪枝](slim/prune)
- [蒸馏](slim/distillation)
- [神经网络搜索](slim/nas)
- [推理部署](deploy)
- [模型导出教程](docs/advanced_tutorials/deploy/EXPORT_MODEL.md)
- [Python端推理部署](deploy/python)
- [C++端推理部署](deploy/cpp)
- [推理Benchmark](docs/advanced_tutorials/deploy/BENCHMARK_INFER_cn.md)
## 模型库
- [模型库](docs/MODEL_ZOO_cn.md)
- [移动端模型](configs/mobile/README.md)
- [Anchor free模型](configs/anchor_free/README.md)
- [人脸检测模型](docs/featured_model/FACE_DETECTION.md)
- [YOLOv3增强模型](docs/featured_model/YOLOv3_ENHANCEMENT.md): COCO mAP高达43.6%，原论文精度为33.0%
- [PP-YOLO模型](configs/ppyolo/README.md): COCO mAP高达45.3%，单卡Tesla V100预测速度高达72.9 FPS
- [行人检测预训练模型](docs/featured_model/CONTRIB_cn.md)
- [车辆检测预训练模型](docs/featured_model/CONTRIB_cn.md)
- [Objects365 2019 Challenge夺冠模型](docs/featured_model/champion_model/CACascadeRCNN.md)
- [Open Images 2019-Object Detction比赛最佳单模型](docs/featured_model/champion_model/OIDV5_BASELINE_MODEL.md)
- [服务器端实用目标检测模型](configs/rcnn_enhance/README.md): V100上速度20FPS时，COCO mAP高达47.8%。
- [大规模实用目标检测模型](docs/featured_model/LARGE_SCALE_DET_MODEL.md): 提供了包含676个类别的大规模服务器端实用目标检测模型，适用于绝大部分使用场景，可以直接用来预测，也可以用于微调其他任务。
## 许可证书
本项目的发布受[Apache 2.0 license](LICENSE)许可认证。
## 版本更新
v0.4.0版本已经在`07/2020`发布，增加PP-YOLO, TTFNet, HTC, ACFPN等多个模型，新增BlazeFace人脸关键点检测模型，新增移动端SSDLite系列优化模型，新增GridMask，RandomErasing数据增强方法，新增Matrix NMS和EMA训练，提升易用性，修复已知诸多bug等，详细内容请参考[版本更新文档](docs/CHANGELOG.md)。
## 如何贡献代码
我们非常欢迎你可以为PaddleDetection提供代码，也十分感谢你的反馈。
- [Model compression](slim)
- [Model compression benchmark](slim)
- [Quantization](slim/quantization)
- [Model pruning](slim/prune)
- [Model distillation](slim/distillation)
- [Neural Architecture Search](slim/nas)
- [Deployment](deploy)
- [Export model for inference](docs/advanced_tutorials/deploy/EXPORT_MODEL.md)
- [Python inference](deploy/python)
- [C++ inference](deploy/cpp)
- [Inference benchmark](docs/advanced_tutorials/inference/BENCHMARK_INFER_cn.md)
## Model Zoo
- Pretrained models are available in the [PaddleDetection model zoo](docs/MODEL_ZOO.md).
- [Mobile models](configs/mobile/README.md)
- [Anchor free models](configs/anchor_free/README.md)
- [Face detection models](docs/featured_model/FACE_DETECTION_en.md)
- [Pretrained models for pedestrian detection](docs/featured_model/CONTRIB.md)
- [Pretrained models for vehicle detection](docs/featured_model/CONTRIB.md)
- [YOLOv3 enhanced model](docs/featured_model/YOLOv3_ENHANCEMENT.md): Compared to MAP of 33.0% in paper, enhanced YOLOv3 reaches the MAP of 43.6%, and inference speed is improved as well
- [PP-YOLO](configs/ppyolo/README.md): PP-YOLO reeached mAP as 45.3% on COCO dataset，and 72.9 FPS on single Tesla V100
- [Objects365 2019 Challenge champion model](docs/featured_model/champion_model/CACascadeRCNN.md)
- [Best single model of Open Images 2019-Object Detction](docs/featured_model/champion_model/OIDV5_BASELINE_MODEL.md)
- [Practical Server-side detection method](configs/rcnn_enhance/README_en.md): Inference speed on single V100 GPU can reach 20FPS when COCO mAP is 47.8%.
- [Large-scale practical object detection models](docs/featured_model/LARGE_SCALE_DET_MODEL_en.md): Large-scale practical server-side detection pretrained models with 676 categories are provided for most application scenarios, which can be used not only for direct inference but also finetuning on other datasets.
## License
PaddleDetection is released under the [Apache 2.0 license](LICENSE).
## Updates
v0.4.0 was released at `05/2020`, add PP-YOLO, TTFNet, HTC, ACFPN, etc. And add BlaceFace face landmark detection model, add a series of optimized SSDLite models on mobile side, add data augmentations GridMask and RandomErasing, add Matrix NMS and EMA training, and improved ease of use, fix many known bugs, etc.
Please refer to [版本更新文档](docs/CHANGELOG.md) for details.
## Contributing
Contributions are highly welcomed and we would really appreciate your feedback!!
README_cn.md 0 → 100644
浏览文件 @ 8138f9aa
简体中文 | [English](README.md)
文档：[https://paddledetection.readthedocs.io](https://paddledetection.readthedocs.io)
# PaddleDetection
飞桨推出的PaddleDetection是端到端目标检测开发套件，旨在帮助开发者更快更好地完成检测模型的训练、精度速度优化到部署全流程。PaddleDetection以模块化的设计实现了多种主流目标检测算法，并且提供了丰富的数据增强、网络组件、损失函数等模块，集成了模型压缩和跨平台高性能部署能力。目前基于PaddleDetection已经完成落地的项目涉及工业质检、遥感图像检测、无人巡检等多个领域。
PaddleDetection新发布精度速度领先的[PP-YOLO](https://arxiv.org/abs/2007.12099)模型，COCO数据集精度达到45.2%，单卡Tesla V100预测速度达到72.9 FPS，详细信息见[PP-YOLO模型](configs/ppyolo/README_cn.md)
**目前检测库下模型均要求使用PaddlePaddle 1.8及以上版本或适当的develop版本。**
<div align="center">
<img src="docs/images/000000570688.jpg" />
</div>
## 简介
特性：
- 模型丰富：
PaddleDetection提供了丰富的模型，包含目标检测、实例分割、人脸检测等100+个预训练模型，涵盖多种数据集竞赛冠军方案、适合云端/边缘端设备部署的检测方案。
- 易部署:
PaddleDetection的模型中使用的核心算子均通过C++或CUDA实现，同时基于PaddlePaddle的高性能推理引擎可以方便地部署在多种硬件平台上。
- 高灵活度：
PaddleDetection通过模块化设计来解耦各个组件，基于配置文件可以轻松地搭建各种检测模型。
- 高性能：
基于PaddlePaddle框架的高性能内核，在模型训练速度、显存占用上有一定的优势。例如，YOLOv3的训练速度快于其他框架，在Tesla V100 16GB环境下，Mask-RCNN(ResNet50)可以单卡Batch Size可以达到4 (甚至到5)。
支持的模型结构：
| | ResNet | ResNet-vd <sup>[1](#vd)</sup> | ResNeXt-vd | SENet | MobileNet | HRNet | Res2Net |
|--------------------|:------:|------------------------------:|:----------:|:-----:|:---------:|:------:| :--: |
| Faster R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ | ✗ |
| Faster R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ |
| Mask R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ | ✗ |
| Mask R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ |
| Cascade Faster-RCNN | ✓ | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ |
| Cascade Mask-RCNN | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| Libra R-CNN | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| RetinaNet | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
| YOLOv3 | ✓ | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ |
| SSD | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
| BlazeFace | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Faceboxes | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
<a name="vd">[1]</a> [ResNet-vd](https://arxiv.org/pdf/1812.01187) 模型预测速度基本不变的情况下提高了精度。
**说明：** ✓ 为[模型库](docs/MODEL_ZOO_cn.md)中提供了对应配置文件和预训练模型，✗ 为未提供参考配置，但一般都支持。
更多的模型:
- EfficientDet
- FCOS
- CornerNet-Squeeze
- YOLOv4
- PP-YOLO
更多的Backone：
- DarkNet
- VGG
- GCNet
- CBNet
- Hourglass
扩展特性：
- [x] **Synchronized Batch Norm**
- [x] **Group Norm**
- [x] **Modulated Deformable Convolution**
- [x] **Deformable PSRoI Pooling**
- [x] **Non-local和GCNet**
**注意:** Synchronized batch normalization 只能在多GPU环境下使用，不能在CPU环境或者单GPU环境下使用。
以下为选取各模型结构和骨干网络的代表模型COCO数据集精度mAP和单卡Tesla V100上预测速度(FPS)关系图。
<div align="center">
<img src="docs/images/map_fps.png" width=800 />
</div>
**说明：**
- `CBResNet`为`Cascade-Faster-RCNN-CBResNet200vd-FPN`模型，COCO数据集mAP高达53.3%
- `Cascade-Faster-RCNN`为`Cascade-Faster-RCNN-ResNet50vd-DCN`，PaddleDetection将其优化到COCO数据mAP为47.8%时推理速度为20FPS
- PaddleDetection增强版`YOLOv3-ResNet50vd-DCN`在COCO数据集mAP高于原作10.6个绝对百分点，推理速度为61.3FPS，快于原作约70%
- 图中模型均可在[模型库](#模型库)中获取
以下为PaddleDetection发布的精度和预测速度优于YOLOv4模型的PP-YOLO与前沿目标检测算法的COCO数据集精度与单卡Tesla V100预测速度(FPS)关系图， PP-YOLO模型在[COCO](http://cocodataset.org) test2019数据集上精度达到45.2%，在单卡V100上FP32推理速度为72.9 FPS，详细信息见[PP-YOLO模型](configs/ppyolo/README_cn.md)
<div align="center">
<img src="docs/images/ppyolo_map_fps.png" width=600 />
</div>
## 文档教程
### 入门教程
- [安装说明](docs/tutorials/INSTALL_cn.md)
- [快速开始](docs/tutorials/QUICK_STARTED_cn.md)
- [训练/评估/预测流程](docs/tutorials/GETTING_STARTED_cn.md)
- [如何训练自定义数据集](docs/tutorials/Custom_DataSet.md)
- [常见问题汇总](docs/FAQ.md)
### 进阶教程
- [数据预处理及数据集定义](docs/advanced_tutorials/READER.md)
- [搭建模型步骤](docs/advanced_tutorials/MODEL_TECHNICAL.md)
- [模型参数配置](docs/advanced_tutorials/config_doc):
- [配置模块设计和介绍](docs/advanced_tutorials/config_doc/CONFIG_cn.md)
- [RCNN模型参数说明](docs/advanced_tutorials/config_doc/RCNN_PARAMS_DOC.md)
- [迁移学习教程](docs/advanced_tutorials/TRANSFER_LEARNING_cn.md)
- [IPython Notebook demo](demo/mask_rcnn_demo.ipynb)
- [模型压缩](slim)
- [压缩benchmark](slim)
- [量化](slim/quantization)
- [剪枝](slim/prune)
- [蒸馏](slim/distillation)
- [神经网络搜索](slim/nas)
- [推理部署](deploy)
- [模型导出教程](docs/advanced_tutorials/deploy/EXPORT_MODEL.md)
- [Python端推理部署](deploy/python)
- [C++端推理部署](deploy/cpp)
- [推理Benchmark](docs/advanced_tutorials/deploy/BENCHMARK_INFER_cn.md)
## 模型库
- [模型库](docs/MODEL_ZOO_cn.md)
- [移动端模型](configs/mobile/README.md)
- [Anchor free模型](configs/anchor_free/README.md)
- [人脸检测模型](docs/featured_model/FACE_DETECTION.md)
- [YOLOv3增强模型](docs/featured_model/YOLOv3_ENHANCEMENT.md): COCO mAP高达43.6%，原论文精度为33.0%
- [PP-YOLO模型](configs/ppyolo/README_cn.md): COCO mAP高达45.3%，单卡Tesla V100预测速度高达72.9 FPS
- [行人检测预训练模型](docs/featured_model/CONTRIB_cn.md)
- [车辆检测预训练模型](docs/featured_model/CONTRIB_cn.md)
- [Objects365 2019 Challenge夺冠模型](docs/featured_model/champion_model/CACascadeRCNN.md)
- [Open Images 2019-Object Detction比赛最佳单模型](docs/featured_model/champion_model/OIDV5_BASELINE_MODEL.md)
- [服务器端实用目标检测模型](configs/rcnn_enhance/README.md): V100上速度20FPS时，COCO mAP高达47.8%。
- [大规模实用目标检测模型](docs/featured_model/LARGE_SCALE_DET_MODEL.md): 提供了包含676个类别的大规模服务器端实用目标检测模型，适用于绝大部分使用场景，可以直接用来预测，也可以用于微调其他任务。
## 许可证书
本项目的发布受[Apache 2.0 license](LICENSE)许可认证。
## 版本更新
v0.4.0版本已经在`07/2020`发布，增加PP-YOLO, TTFNet, HTC, ACFPN等多个模型，新增BlazeFace人脸关键点检测模型，新增移动端SSDLite系列优化模型，新增GridMask，RandomErasing数据增强方法，新增Matrix NMS和EMA训练，提升易用性，修复已知诸多bug等，详细内容请参考[版本更新文档](docs/CHANGELOG.md)。
## 如何贡献代码
我们非常欢迎你可以为PaddleDetection提供代码，也十分感谢你的反馈。
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English | [简体中文](README.md)
Documentation:[https://paddledetection.readthedocs.io](https://paddledetection.readthedocs.io)
# PaddleDetection
PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which
aims to help developers in the whole development of training models, optimizing performance and
inference speed, and deploying models. PaddleDetection provides varied object detection architectures
in modular design, and wealthy data augmentation methods, network components, loss functions, etc.
PaddleDetection supported practical projects such as industrial quality inspection, remote sensing
image object detection, and automatic inspection with its practical features such as model compression
and multi-platform deployment.
[PP-YOLO](https://arxiv.org/abs/2007.12099), which is faster and has higer performance than YOLOv4,
has been released, it reached mAP(0.5:0.95) as 45.2% on COCO test2019 dataset and 72.9 FPS on single
Test V100. Please refer to [PP-YOLO](configs/ppyolo/README.md) for details.
**Now all models in PaddleDetection require PaddlePaddle version 1.8 or higher, or suitable develop version.**
<div align="center">
<img src="docs/images/000000570688.jpg" />
</div>
## Introduction
Features:
- Rich models:
PaddleDetection provides rich of models, including 100+ pre-trained models
such as object detection, instance segmentation, face detection etc. It covers
the champion models, the practical detection models for cloud and edge device.
- Production Ready:
Key operations are implemented in C++ and CUDA, together with PaddlePaddle's
highly efficient inference engine, enables easy deployment in server environments.
- Highly Flexible:
Components are designed to be modular. Model architectures, as well as data
preprocess pipelines, can be easily customized with simple configuration
changes.
- Performance Optimized:
With the help of the underlying PaddlePaddle framework, faster training and
reduced GPU memory footprint is achieved. Notably, YOLOv3 training is
much faster compared to other frameworks. Another example is Mask-RCNN
(ResNet50), we managed to fit up to 4 images per GPU (Tesla V100 16GB) during
multi-GPU training.
Supported Architectures:
| | ResNet | ResNet-vd <sup>[1](#vd)</sup> | ResNeXt-vd | SENet | MobileNet | HRNet | Res2Net |
| ------------------- | :----: | ----------------------------: | :--------: | :---: | :-------: |:------:|:-----: |
| Faster R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ | ✗ |
| Faster R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✓ | ✓ |
| Mask R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ | ✗ |
| Mask R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ |
| Cascade Faster-RCNN | ✓ | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ |
| Cascade Mask-RCNN | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| Libra R-CNN | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| RetinaNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| YOLOv3 | ✓ | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ |
| SSD | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
| BlazeFace | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Faceboxes | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
<a name="vd">[1]</a> [ResNet-vd](https://arxiv.org/pdf/1812.01187) models offer much improved accuracy with negligible performance cost.
**NOTE:** ✓ for config file and pretrain model provided in [Model Zoo](docs/MODEL_ZOO.md), ✗ for not provided but is supported generally.
More models:
- EfficientDet
- FCOS
- CornerNet-Squeeze
- YOLOv4
- PP-YOLO
More Backbones:
- DarkNet
- VGG
- GCNet
- CBNet
Advanced Features:
- [x] **Synchronized Batch Norm**
- [x] **Group Norm**
- [x] **Modulated Deformable Convolution**
- [x] **Deformable PSRoI Pooling**
- [x] **Non-local and GCNet**
**NOTE:** Synchronized batch normalization can only be used on multiple GPU devices, can not be used on CPU devices or single GPU device.
The following is the relationship between COCO mAP and FPS on Tesla V100 of representative models of each architectures and backbones.
<div align="center">
<img src="docs/images/map_fps.png" width=800 />
</div>
**NOTE:**
- `CBResNet` stands for `Cascade-Faster-RCNN-CBResNet200vd-FPN`, which has highest mAP on COCO as 53.3% in PaddleDetection models
- `Cascade-Faster-RCNN` stands for `Cascade-Faster-RCNN-ResNet50vd-DCN`, which has been optimized to 20 FPS inference speed when COCO mAP as 47.8%
- The enhanced `YOLOv3-ResNet50vd-DCN` is 10.6 absolute percentage points higher than paper on COCO mAP, and inference speed is nearly 70% faster than the darknet framework
- All these models can be get in [Model Zoo](#Model-Zoo)
The following is the relationship between COCO mAP and FPS on Tesla V100 of SOTA object detecters and PP-YOLO, which is faster and has better performance than YOLOv4, and reached mAP(0.5:0.95) as 45.2% on COCO test2019 dataset and 72.9 FPS on single Test V100. Please refer to [PP-YOLO](configs/ppyolo/README.md) for details.
<div align="center">
<img src="docs/images/ppyolo_map_fps.png" width=600 />
</div>
## Tutorials
### Get Started
- [Installation guide](docs/tutorials/INSTALL.md)
- [Quick start on small dataset](docs/tutorials/QUICK_STARTED.md)
- [Train/Evaluation/Inference](docs/tutorials/GETTING_STARTED.md)
- [How to train a custom dataset](docs/tutorials/Custom_DataSet.md)
- [FAQ](docs/FAQ.md)
### Advanced Tutorial
- [Guide to preprocess pipeline and dataset definition](docs/advanced_tutorials/READER.md)
- [Models technical](docs/advanced_tutorials/MODEL_TECHNICAL.md)
- [Transfer learning document](docs/advanced_tutorials/TRANSFER_LEARNING.md)
- [Parameter configuration](docs/advanced_tutorials/config_doc):
- [Introduction to the configuration workflow](docs/advanced_tutorials/config_doc/CONFIG.md)
- [Parameter configuration for RCNN model](docs/advanced_tutorials/config_doc/RCNN_PARAMS_DOC.md)
- [IPython Notebook demo](demo/mask_rcnn_demo.ipynb)
- [Model compression](slim)
- [Model compression benchmark](slim)
- [Quantization](slim/quantization)
- [Model pruning](slim/prune)
- [Model distillation](slim/distillation)
- [Neural Architecture Search](slim/nas)
- [Deployment](deploy)
- [Export model for inference](docs/advanced_tutorials/deploy/EXPORT_MODEL.md)
- [Python inference](deploy/python)
- [C++ inference](deploy/cpp)
- [Inference benchmark](docs/advanced_tutorials/inference/BENCHMARK_INFER_cn.md)
## Model Zoo
- Pretrained models are available in the [PaddleDetection model zoo](docs/MODEL_ZOO.md).
- [Mobile models](configs/mobile/README.md)
- [Anchor free models](configs/anchor_free/README.md)
- [Face detection models](docs/featured_model/FACE_DETECTION_en.md)
- [Pretrained models for pedestrian detection](docs/featured_model/CONTRIB.md)
- [Pretrained models for vehicle detection](docs/featured_model/CONTRIB.md)
- [YOLOv3 enhanced model](docs/featured_model/YOLOv3_ENHANCEMENT.md): Compared to MAP of 33.0% in paper, enhanced YOLOv3 reaches the MAP of 43.6%, and inference speed is improved as well
- [PP-YOLO](configs/ppyolo/README.md): PP-YOLO reeached mAP as 45.3% on COCO dataset，and 72.9 FPS on single Tesla V100
- [Objects365 2019 Challenge champion model](docs/featured_model/champion_model/CACascadeRCNN.md)
- [Best single model of Open Images 2019-Object Detction](docs/featured_model/champion_model/OIDV5_BASELINE_MODEL.md)
- [Practical Server-side detection method](configs/rcnn_enhance/README_en.md): Inference speed on single V100 GPU can reach 20FPS when COCO mAP is 47.8%.
- [Large-scale practical object detection models](docs/featured_model/LARGE_SCALE_DET_MODEL_en.md): Large-scale practical server-side detection pretrained models with 676 categories are provided for most application scenarios, which can be used not only for direct inference but also finetuning on other datasets.
## License
PaddleDetection is released under the [Apache 2.0 license](LICENSE).
## Updates
v0.4.0 was released at `05/2020`, add PP-YOLO, TTFNet, HTC, ACFPN, etc. And add BlaceFace face landmark detection model, add a series of optimized SSDLite models on mobile side, add data augmentations GridMask and RandomErasing, add Matrix NMS and EMA training, and improved ease of use, fix many known bugs, etc.
Please refer to [版本更新文档](docs/CHANGELOG.md) for details.
## Contributing
Contributions are highly welcomed and we would really appreciate your feedback!!
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简体中文 | [English](README.md)
# PP-YOLO 模型
## 内容
- [简介](#简介)
- [模型库与基线](#模型库与基线)
- [使用说明](#使用说明)
- [未来工作](#未来工作)
- [附录](#附录)
## 简介
[PP-YOLO](https://arxiv.org/abs/2007.12099)是PaddleDetection优化和改进的YOLOv3的模型，其精度(COCO数据集mAP)和推理速度均优于[YOLOv4](https://arxiv.org/abs/2004.10934)模型，要求使用PaddlePaddle 1.8.4(2020年8月中旬发布)或适当的[develop版本](https://www.paddlepaddle.org.cn/documentation/docs/zh/install/Tables.html#whl-dev)。
PP-YOLO在[COCO](http://cocodataset.org) test-dev2017数据集上精度达到45.2%，在单卡V100上FP32推理速度为72.9 FPS, V100上开启TensorRT下FP16推理速度为155.6 FPS。
<div align="center">
<img src="../../docs/images/ppyolo_map_fps.png" width=500 />
</div>
PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度：
- 更优的骨干网络: ResNet50vd-DCN
- 更大的训练batch size: 8 GPUs，每GPU batch_size=24，对应调整学习率和迭代轮数
- [Drop Block](https://arxiv.org/abs/1810.12890)
- [Exponential Moving Average](https://www.investopedia.com/terms/e/ema.asp)
- [IoU Loss](https://arxiv.org/pdf/1902.09630.pdf)
- [Grid Sensitive](https://arxiv.org/abs/2004.10934)
- [Matrix NMS](https://arxiv.org/pdf/2003.10152.pdf)
- [CoordConv](https://arxiv.org/abs/1807.03247)
- [Spatial Pyramid Pooling](https://arxiv.org/abs/1406.4729)
- 更优的预训练模型
## 模型库
### PP-YOLO模型
| 模型 | GPU个数 | 每GPU图片个数 | 骨干网络 | 输入尺寸 | Box AP | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件 |
|:------------------------:|:-------:|:-------------:|:----------:| :-------:| :----: | :------------: | :---------------------: | :------: | :------: |
| YOLOv4(AlexyAB) | - | - | CSPDarknet | 608 | 43.5 | 62 | 105.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml) |
| YOLOv4(AlexyAB) | - | - | CSPDarknet | 512 | 43.0 | 83 | 138.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml) |
| YOLOv4(AlexyAB) | - | - | CSPDarknet | 416 | 41.2 | 96 | 164.0 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml) |
| YOLOv4(AlexyAB) | - | - | CSPDarknet | 320 | 38.0 | 123 | 199.0 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_csdarknet.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 608 | 45.2 | 72.9 | 155.6 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 512 | 44.4 | 89.9 | 188.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 416 | 42.5 | 109.1 | 215.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) |
| PP-YOLO | 8 | 24 | ResNet50vd | 320 | 39.3 | 132.2 | 242.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) |
**注意:**
- PP-YOLO模型使用COCO数据集中train2017作为训练集，使用test-dev2017作为测试集，`Box AP`为`mAP(IoU=0.5:0.95)`评估结果。
- PP-YOLO模型训练过程中使用8 GPUs，每GPU batch size为24进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](../../docs/FAQ.md)调整学习率和迭代次数。
- PP-YOLO模型推理速度测试采用单卡V100，batch size=1进行测试，使用CUDA 10.2, CUDNN 7.5.1，TensorRT推理速度测试使用TensorRT 5.1.2.2。
- PP-YOLO模型FP32的推理速度测试数据为使用`tools/export_model.py`脚本导出模型后，使用`deploy/python/infer.py`脚本中的`--run_benchnark`参数使用Paddle预测库进行推理速度benchmark测试结果, 且测试的均为不包含数据预处理和模型输出后处理(NMS)的数据(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)。
- TensorRT FP16的速度测试相比于FP32去除了`yolo_box`(bbox解码)部分耗时，即不包含数据预处理，bbox解码和NMS(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)。
- YOLOv4(AlexyAB)模型精度和V100 FP32推理速度数据使用[YOLOv4 github库](https://github.com/AlexeyAB/darknet)提供的单卡V100上精度速度测试数据，V100 TensorRT FP16推理速度为使用[AlexyAB/darknet](https://github.com/AlexeyAB/darknet)库中tkDNN配置于单卡V100，TensorRT 5.1.2.2的测试结果。
- PP-YOLO模型推理速度测试采用单卡V100，batch size=1进行测试，使用CUDA 10.2, CUDNN 7.5.1，TensorRT推理速度测试使用TensorRT 5.1.2.2。
- YOLOv4(AlexyAB)行`模型下载`和`配置文件`为PaddleDetection复现的YOLOv4模型，目前评估精度已对齐，支持finetune，训练精度对齐中，可参见[PaddleDetection YOLOv4 模型](../yolov4/README.md)
### PP-YOLO tiny模型
| 模型 | GPU个数 | 每GPU图片个数 | 骨干网络 | 输入尺寸 | Box AP | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件 |
|:------------------------:|:-------:|:-------------:|:----------:| :-------:| :----: | :------------: | :---------------------: | :------: | :------: |
| PP-YOLO tiny | 4 | 32 | ResNet18vd | 416 | 47.0 | 401.6 | 724.6 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_tiny.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_tiny.yml) |
| PP-YOLO tiny | 4 | 32 | ResNet18vd | 320 | 43.7 | 478.5 | 791.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_tiny.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_tiny.yml) |
- PP-YOLO tiny模型使用COCO数据集中train2017作为训练集，使用val2017左右测试集，`Box AP`为`mAP(IoU=0.5)`评估结果。
- PP-YOLO tiny模型训练过程中使用4GPU，每GPU batch size为32进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](../../docs/FAQ.md)调整学习率和迭代次数。
- PP-YOLO tiny模型推理速度测试环境配置和测试方法与PP-YOLO模型一致。
## 使用说明
### 1. 训练
使用8GPU通过如下命令一键式启动训练(以下命令均默认在PaddleDetection根目录运行), 通过`--eval`参数开启训练中交替评估。
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python tools/train.py -c configs/ppyolo/ppyolo.yml --eval
```
### 2. 评估
使用单GPU通过如下命令一键式评估模型在COCO val2017数据集效果
```bash
# 使用PaddleDetection发布的权重
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/ppyolo/ppyolo.yml -o weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams
# 使用训练保存的checkpoint
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/ppyolo/ppyolo.yml -o weights=output/ppyolo/best_model
```
我们提供了`configs/ppyolo/ppyolo_test.yml`用于评估COCO test-dev2017数据集的效果，评估COCO test-dev2017数据集的效果须先从[COCO数据集下载页](https://cocodataset.org/#download)下载test-dev2017数据集，解压到`configs/ppyolo/ppyolo_test.yml`中`EvalReader.dataset`中配置的路径，并使用如下命令进行评估
```bash
# 使用PaddleDetection发布的权重
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/ppyolo/ppyolo_test.yml -o weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams
# 使用训练保存的checkpoint
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/ppyolo/ppyolo_test.yml -o weights=output/ppyolo/best_model
```
评估结果保存于`bbox.json`中，将其压缩为zip包后通过[COCO数据集评估页](https://competitions.codalab.org/competitions/20794#participate)提交评估。
**注意:** `configs/ppyolo/ppyolo_test.yml`仅用于评估COCO test-dev数据集，不用于训练和评估COCO val2017数据集。
### 3. 推理
使用单GPU通过如下命令一键式推理图像，通过`--infer_img`指定图像路径，或通过`--infer_dir`指定目录并推理目录下所有图像
```bash
# 推理单张图像
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyolo/ppyolo.yml -o weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams --infer_img=demo/000000014439_640x640.jpg
# 推理目录下所有图像
CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/ppyolo/ppyolo.yml -o weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams --infer_dir=demo
```
### 4. 推理部署与benchmark
PP-YOLO模型部署及推理benchmark需要通过`tools/export_model.py`导出模型后使用Paddle预测库进行部署和推理，可通过如下命令一键式启动。
```bash
# 导出模型，默认存储于output/ppyolo目录
python tools/export_model.py -c configs/ppyolo/ppyolo.yml -o weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams
# 预测库推理
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output/ppyolo --image_file=demo/000000014439_640x640.jpg --use_gpu=True
```
PP-YOLO模型benchmark测试为不包含数据预处理和网络输出后处理(NMS)的网络结构部分数据，导出模型时须指定`--exlcude_nms`来裁剪掉模型中后处理的NMS部分，通过如下命令进行模型导出和benchmark测试。
```bash
# 导出模型，通过--exclude_nms参数裁剪掉模型中的NMS部分，默认存储于output/ppyolo目录
python tools/export_model.py -c configs/ppyolo/ppyolo.yml -o weights=https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams --exclude_nms
# FP32 benchmark测试
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output/ppyolo --image_file=demo/000000014439_640x640.jpg --use_gpu=True --run_benchmark=True
# TensorRT FP16 benchmark测试
CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output/ppyolo --image_file=demo/000000014439_640x640.jpg --use_gpu=True --run_benchmark=True --run_mode=trt_fp16
```
## 未来工作
1. 发布PP-YOLO-tiny模型
2. 发布更多骨干网络的PP-YOLO及PP-YOLO-tiny模型
## 附录
PP-YOLO模型相对于YOLOv3模型优化项消融实验数据如下表所示。
| 序号 | 模型 | Box AP | 参数量(M) | FLOPs(G) | V100 FP32 FPS |
| :--: | :--------------------------- | :----: | :-------: | :------: | :-----------: |
| A | YOLOv3-DarkNet53 | 38.9 | 59.13 | 65.52 | 58.2 |
| B | YOLOv3-ResNet50vd-DCN | 39.1 | 43.89 | 44.71 | 79.2 |
| C | B + LB + EMA + DropBlock | 41.4 | 43.89 | 44.71 | 79.2 |
| D | C + IoU Loss | 41.9 | 43.89 | 44.71 | 79.2 |
| E | D + IoU Aware | 42.5 | 43.90 | 44.71 | 74.9 |
| F | E + Grid Sensitive | 42.8 | 43.90 | 44.71 | 74.8 |
| G | F + Matrix NMS | 43.5 | 43.90 | 44.71 | 74.8 |
| H | G + CoordConv | 44.0 | 43.93 | 44.76 | 74.1 |
| I | H + SPP | 44.3 | 44.93 | 45.12 | 72.9 |
| J | I + Better ImageNet Pretrain | 44.6 | 44.93 | 45.12 | 72.9 |
**注意:**
- 精度与推理速度数据均为使用输入图像尺寸为608的测试结果
- Box AP为在COCO train2017数据集训练，val2017数据集上评估数据
- 推理速度为单卡V100上，batch size=1, 使用上述benchmark测试方法的测试结果，测试环境配置为CUDA 10.2，CUDNN 7.5.1
- [YOLOv3-DarkNet53](../yolov3_darknet.yml)精度38.9为PaddleDetection优化后的YOLOv3模型，可参见[模型库](../../docs/MODEL_ZOO_cn.md)
ppdet/modeling/backbones/mobilenet_v3.py
浏览文件 @ 8138f9aa
......@@ -68,6 +68,9 @@ class MobileNetV3(object):
if isinstance(feature_maps, Integral):
feature_maps = [feature_maps]
if norm_type == 'sync_bn' and freeze_norm:
raise ValueError(
"The norm_type should not be sync_bn when freeze_norm is True")
self.scale = scale
self.model_name = model_name
self.feature_maps = feature_maps
......@@ -437,16 +440,15 @@ class MobileNetV3(object):
@register
class MobileNetV3RCNN(MobileNetV3):
def __init__(
self,
scale=1.0,
model_name='large',
conv_decay=0.0,
norm_type='bn',
norm_decay=0.0,
freeze_norm=True,
feature_maps=[2, 3, 4, 5],
lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0], ):
def __init__(self,
scale=1.0,
model_name='large',
conv_decay=0.0,
norm_type='bn',
norm_decay=0.0,
freeze_norm=True,
feature_maps=[2, 3, 4, 5],
lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0]):
super(MobileNetV3RCNN, self).__init__(
scale=scale,
model_name=model_name,
......@@ -454,7 +456,8 @@ class MobileNetV3RCNN(MobileNetV3):
norm_type=norm_type,
norm_decay=norm_decay,
lr_mult_list=lr_mult_list,
feature_maps=feature_maps)
feature_maps=feature_maps,
freeze_norm=freeze_norm)
self.curr_stage = 0
self.block_stride = 1
......
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