English | 简体中文
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🔥 2022.8.09:Release YOLO series model zoo
- Comprehensive coverage of classic and latest models of the YOLO series: Including YOLOv3,Paddle real-time object detection model PP-YOLOE, and frontier detection algorithms YOLOv4, YOLOv5, YOLOX, MT-YOLOv6 and YOLOv7
- Better model performance:Upgrade based on various YOLO algorithms, shorten training time in 5-8 times and the accuracy is generally improved by 1%-5% mAP. The model compression strategy is used to achieve 30% improvement in speed without precision loss
- Complete end-to-end development support:End-to-end development pipieline including training, evaluation, inference, model compression and deployment on various hardware. Meanwhile, support flexible algorithnm switch and implement customized development efficiently
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🔥 2022.8.01:Release PP-TinyPose plus. The end-to-end precision improves 9.1% AP in dataset of fitness and dance scenes
- Increase data of sports scenes, and the recognition performance of complex actions is significantly improved, covering actions such as sideways, lying down, jumping, and raising legs
- Detection model uses PP-PicoDet plus and the precision on COCO dataset is improved by 3.1% mAP
- The stability of keypoints is enhanced. Implement the filter stabilization method to make the video prediction result more stable and smooth.
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2022.7.14:Release pedestrian analysis tool PP-Human v2
- Four major functions: five complicated action recognition with high performance and Flexible, real-time human attribute recognition, visitor flow statistics and high-accuracy multi-camera tracking.
- High performance algorithm: including pedestrian detection, tracking, attribute recognition which is robust to the number of targets and the variant of background and light.
- Highly Flexible: providing complete introduction of end-to-end development and optimization strategy, simple command for deployment and compatibility with different input format.
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2022.3.24:PaddleDetection releasedrelease/2.4 version
- Release high-performanace SOTA object detection model PP-YOLOE. It integrates cloud and edge devices and provides S/M/L/X versions. In particular, Verson L has the accuracy as 51.4% on COCO test 2017 dataset, inference speed as 78.1 FPS on a single Test V100. It supports mixed precision training, 33% faster than PP-YOLOv2. Its full range of multi-sized models can meet different hardware arithmetic requirements, and adaptable to server, edge-device GPU and other AI accelerator cards on servers.
- Release ultra-lightweight SOTA object detection model PP-PicoDet Plus with 2% improvement in accuracy and 63% improvement in CPU inference speed. Add PicoDet-XS model with a 0.7M parameter, providing model sparsification and quantization functions for model acceleration. No specific post processing module is required for all the hardware, simplifying the deployment.
- Release the real-time pedestrian analysis tool PP-Human. It has four major functions: pedestrian tracking, visitor flow statistics, human attribute recognition and falling detection. For falling detection, it is optimized based on real-life data with accurate recognition of various types of falling posture. It can adapt to different environmental background, light and camera angle.
- Add YOLOX object detection model with nano/tiny/S/M/L/X. X version has the accuracy as 51.8% on COCO Val2017 dataset.
PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which implements varied mainstream object detection, instance segmentation, tracking and keypoint detection algorithms in modular design with configurable modules such as network components, data augmentations and losses. It releases many kinds SOTA industry practice models and integrates abilities of model compression and cross-platform high-performance deployment to help developers in the whole process with a faster and better way.
PaddleDetection provides image processing capabilities such as object detection, instance segmentation, multi-object tracking, keypoint detection and etc.
PaddleDetection covers industrialization, smart city, security & protection, retail, medicare industry and etc.
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Rich Models
PaddleDetection provides rich of models, including 250+ pre-trained models such as object detection, instance segmentation, face detection, keypoint detection, multi-object tracking and etc, covering a variety of global competition champion schemes.
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Highly Flexible
Components are designed to be modular. Model architectures, as well as data preprocess pipelines and optimization strategies, can be easily customized with simple configuration changes.
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Production Ready
From data augmentation, constructing models, training, compression, depolyment, get through end to end, and complete support for multi-architecture, multi-device deployment for cloud and edge device.
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High Performance
Based on the high performance core of PaddlePaddle, advantages of training speed and memory occupation are obvious. FP16 training and multi-machine training are supported as well.
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If you have any problem or suggestion on PaddleDetection, please send us issues through GitHub Issues.
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Welcome to Join PaddleDetection QQ Group and Wechat Group (reply "Det").
Architectures | Backbones | Components | Data Augmentation |
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The relationship between COCO mAP and FPS on Tesla V100 of representative models of each server side architectures and backbones.
NOTE:
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CBResNet stands
forCascade-Faster-RCNN-CBResNet200vd-FPN
, which has highest mAP on COCO as 53.3% -
Cascade-Faster-RCNN
stands forCascade-Faster-RCNN-ResNet50vd-DCN
, which has been optimized to 20 FPS inference speed when COCO mAP as 47.8% in PaddleDetection models -
PP-YOLO
achieves mAP of 45.9% on COCO and 72.9FPS on Tesla V100. Both precision and speed surpass YOLOv4 -
PP-YOLO v2
is optimized version ofPP-YOLO
which has mAP of 49.5% and 68.9FPS on Tesla V100 -
PP-YOLOE
is optimized version ofPP-YOLO v2
which has mAP of 51.6% and 78.1FPS on Tesla V100 -
All these models can be get in Model Zoo
The relationship between COCO mAP and FPS on Qualcomm Snapdragon 865 of representative mobile side models.
NOTE:
- All data tested on Qualcomm Snapdragon 865(4A77 + 4A55) processor with batch size of 1 and CPU threads of 4, and use NCNN library in testing, benchmark scripts is publiced at MobileDetBenchmark
- PP-PicoDet and PP-YOLO-Tiny are developed and released by PaddleDetection, other models are not provided in PaddleDetection.
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Parameter Configuration
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Model Compression(Based on PaddleSlim)
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Inference and Deployment
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Advanced Development
- General Object Detection
- General Instance Segmentation
- Rotated Object Detection
- Keypoint Detection
- PP-TinyPose
- HigherHRNet
- HRNet
- LiteHRNet
- Multi-Object Tracking
- Practical Specific Models
- Scienario Solution
- Competition Solution
For the details of version update, please refer to Version Update Doc.
PaddleDetection is released under the Apache 2.0 license.
Contributions are highly welcomed and we would really appreciate your feedback!!
- Thanks Mandroide for cleaning the code and unifying some function interface.
- Thanks FL77N for contributing the code of
Sparse-RCNN
model. - Thanks Chen-Song for contributing the code of
Swin Faster-RCNN
model. - Thanks yangyudong, hchhtc123 for contributing PP-Tracking GUI interface.
- Thanks Shigure19 for contributing PP-TinyPose fitness APP.
- Thanks manangoel99 for contributing Wandblogger for visualization of the training and evaluation metrics
@misc{ppdet2019,
title={PaddleDetection, Object detection and instance segmentation toolkit based on PaddlePaddle.},
author={PaddlePaddle Authors},
howpublished = {\url{https://github.com/PaddlePaddle/PaddleDetection}},
year={2019}
}