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🎊YOLO Vision Event🎊
Join the experts of Ultralytics as well as leaders in the space on September 27th, 2022 to explore the technical and business insights shaping the future of Vision AI!
- ⏰Time：Sep 27th
- 👨🏫Tech Talk：PaddleDetection Toolkit and PP-YOLO Series
- 💎Panel Topic：Open Source Projects Enabling the Future of Computer Vision AI
⛓Register Now：https://ultralytics.com/yolo-vision
- 🔮Easter eggs：PaddleDetection has released the YOLO Family model zoo on August 26th, including YOLOv3/YOLOv5/YOLOX/YOLOv7 and PP-YOLOE/PP-YOLOE+, feel free to check out: https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/docs/feature_models/YOLOSERIES_MODEL.md
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🔥 2022.8.26：PaddleDetection releasesrelease/2.5 version
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🗳 Model features：
- Release PP-YOLOE+: Increased accuracy by a maximum of 2.4% mAP to 54.9% mAP, 3.75 times faster model training convergence rate, and up to 2.3 times faster end-to-end inference speed; improved generalization for multiple downstream tasks
- Release PicoDet-NPU model which supports full quantization deployment of models; add PicoDet layout analysis model
- Release PP-TinyPose Plus. With 9.1% AP accuracy improvement in physical exercise, dance, and other scenarios, our PP-TinyPose Plus supports unconventional movements such as turning to one side, lying down, jumping, and high lifts
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🔮 Functions in different scenarios
- Release the pedestrian analysis tool PP-Human v2. It introduces four new behavior recognition: fighting, telephoning, smoking, and trespassing. The underlying algorithm performance is optimized, covering three core algorithm capabilities: detection, tracking, and attributes of pedestrians. Our model provides end-to-end development and model optimization strategies for beginners and supports online video streaming input.
- First release PP-Vehicle, which has four major functions: license plate recognition, vehicle attribute analysis (color, model), traffic flow statistics, and violation detection. It is compatible with input formats, including pictures, online video streaming, and video. And we also offer our users a comprehensive set of tutorials for customization.
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💡 Cutting-edge algorithms：
- Covers YOLO family classic and latest models: YOLOv3, PP-YOLOE (a real-time high-precision object detection model developed by Baidu PaddlePaddle), and cutting-edge detection algorithms such as YOLOv4, YOLOv5, YOLOX, YOLOv6, and YOLOv7
- Newly add high precision detection model based on ViT backbone network, with a 55.7% mAP accuracy on COCO dataset; newly add multi-object tracking model OC-SORT; newly add ConvNeXt backbone network.
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📋 Industrial applications: Newly add Smart Fitness, Fighting recognition, and Visitor Analysis.
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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. Providing over 30 model algorithm and over 250 pre-trained models, it covers object detection, instance segmentation, keypoint detection, multi-object tracking. In particular, PaddleDetection offers high- performance & light-weight industrial SOTA models on servers and mobile devices, champion solution and cutting-edge algorithm. PaddleDetection provides various data augmentation methods, configurable network components, loss functions and other advanced optimization & deployment schemes. In addition to running through the whole process of data processing, model development, training, compression and deployment, PaddlePaddle also provides rich cases and tutorials to accelerate the industrial application of algorithm.
- Rich model library: PaddleDetection provides over 250 pre-trained models including object detection, instance segmentation, face recognition, multi-object tracking. It covers a variety of global competition champion schemes.
- Simple to use: Modular design, decoupling each network component, easy for developers to build and try various detection models and optimization strategies, quick access to high-performance, customized algorithm.
- Getting Through End to End: PaddlePaddle gets through end to end from data augmentation, constructing models, training, compression, depolyment. It also supports multi-architecture, multi-device deployment for cloud and edge device.
- High Performance: Due to the high performance core, PaddlePaddle has clear advantages in training speed and memory occupation. It also supports FP16 training and multi-machine training.
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If you have any question or suggestion, please give us your valuable input via GitHub Issues
Welcome to join PaddleDetection user groups on WeChat (scan the QR code, add and reply "D" to the assistant)
| Architectures | Backbones | Components | Data Augmentation |
Object DetectionInstance SegmentationFace DetectionMulti-Object-TrackingKeyPoint-Detection |
Details
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Common
KeyPoint
FPN
Loss
Post-processing
Speed
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Details
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Performance comparison of Cloud models
The comparison between COCO mAP and FPS on Tesla V100 of representative models of each architectures and backbones.
Clarification：
ViTstands forViT-Cascade-Faster-RCNN, which has highest mAP on COCO as 55.7%Cascade-Faster-RCNNstands forCascade-Faster-RCNN-ResNet50vd-DCN, which has been optimized to 20 FPS inference speed when COCO mAP as 47.8% in PaddleDetection modelsPP-YOLOEare optimizedPP-YOLO v2. It reached accuracy as 51.4% on COCO dataset, inference speed as 78.1 FPS on Tesla V100PP-YOLOE+are optimizedPP-YOLOE. It reached accuracy as 53.3% on COCO dataset, inference speed as 78.1 FPS on Tesla V100- The models in the figure are available in the model library
Performance omparison on mobiles
The comparison between COCO mAP and FPS on Qualcomm Snapdragon 865 processor of models on mobile devices.
Clarification：
- Tests were conducted on Qualcomm Snapdragon 865 (4 *A77 + 4 *A55) batch_size=1, 4 thread, and NCNN inference library, test script see MobileDetBenchmark
- PP-PicoDet and PP-YOLO-Tiny are self-developed models of PaddleDetection, and other models are not tested yet.
1. General detection
PP-YOLOE series Recommended scenarios: Cloud GPU such as Nvidia V100, T4 and edge devices such as Jetson series
| Model | COCO Accuracy（mAP） | V100 TensorRT FP16 Speed(FPS) | Configuration | Download |
|---|---|---|---|---|
| PP-YOLOE+_s | 43.9 | 333.3 | link | download |
| PP-YOLOE+_m | 50.0 | 208.3 | link | download |
| PP-YOLOE+_l | 53.3 | 149.2 | link | download |
| PP-YOLOE+_x | 54.9 | 95.2 | link | download |
PP-PicoDet series Recommended scenarios: Mobile chips and x86 CPU devices, such as ARM CPU(RK3399, Raspberry Pi) and NPU(BITMAIN)
| Model | COCO Accuracy（mAP） | Snapdragon 865 four-thread speed (ms) | Configuration | Download |
|---|---|---|---|---|
| PicoDet-XS | 23.5 | 7.81 | Link | Download |
| PicoDet-S | 29.1 | 9.56 | Link | Download |
| PicoDet-M | 34.4 | 17.68 | Link | Download |
| PicoDet-L | 36.1 | 25.21 | Link | Download |
| Model | COCO Accuracy（mAP） | V100 TensorRT FP16 speed(FPS) | Configuration | Download |
|---|---|---|---|---|
| YOLOX-l | 50.1 | 107.5 | Link | Download |
| YOLOv5-l | 48.6 | 136.0 | Link | Download |
Other general purpose models doc
2. Instance segmentation
| Model | Introduction | Recommended Scenarios | COCO Accuracy(mAP) | Configuration | Download |
|---|---|---|---|---|---|
| Mask RCNN | Two-stage instance segmentation algorithm | Edge-Cloud end |
box AP: 41.4 mask AP: 37.5 |
Link | Download |
| Cascade Mask RCNN | Two-stage instance segmentation algorithm | Edge-Cloud end |
box AP: 45.7 mask AP: 39.7 |
Link | Download |
| SOLOv2 | Lightweight single-stage instance segmentation algorithm | Edge-Cloud end |
mask AP: 38.0 | Link | Download |
3. Keypoint detection
| Model | Introduction | Recommended scenarios | COCO Accuracy（AP） | Speed | Configuration | Download |
|---|---|---|---|---|---|---|
| HRNet-w32 + DarkPose | Top-down Keypoint detection algorithm Input size: 384x288 |
Edge-Cloud end |
78.3 | T4 TensorRT FP16 2.96ms | Link | Download |
| HRNet-w32 + DarkPose | Top-down Keypoint detection algorithm Input size: 256x192 |
Edge-Cloud end | 78.0 | T4 TensorRT FP16 1.75ms | Link | Download |
| PP-TinyPose | Light-weight keypoint algorithm Input size: 256x192 |
Mobile | 68.8 | Snapdragon 865 four-thread 6.30ms | Link | Download |
| PP-TinyPose | Light-weight keypoint algorithm Input size: 128x96 |
Mobile | 58.1 | Snapdragon 865 four-thread 2.37ms | Link | Download |
Other keypoint detection models doc
4. Multi-object tracking PP-Tracking
| Model | Introduction | Recommended scenarios | Accuracy | Configuration | Download |
|---|---|---|---|---|---|
| ByteTrack | SDE Multi-object tracking algorithm with detection model only | Edge-Cloud end | MOT-17 half val: 77.3 | Link | Download |
| FairMOT | JDE multi-object tracking algorithm multi-task learning | Edge-Cloud end | MOT-16 test: 75.0 | Link | Download |
| OC-SORT | SDE multi-object tracking algorithm with detection model only | Edge-Cloud end | MOT-16 half val: 75.5 | Link | - |
Other multi-object tracking models docs
5. Industrial real-time pedestrain analysis tool-PP Human
| Task | End-to-End Speed（ms） | Model | Size |
|---|---|---|---|
| Pedestrian detection (high precision) | 25.1ms | Multi-object tracking | 182M |
| Pedestrian detection (lightweight) | 16.2ms | Multi-object tracking | 27M |
| Pedestrian tracking (high precision) | 31.8ms | Multi-object tracking | 182M |
| Pedestrian tracking (lightweight) | 21.0ms | Multi-object tracking | 27M |
| Attribute recognition (high precision) | Single person8.5ms | Object detection Attribute recognition |
Object detection：182M Attribute recognition：86M |
| Attribute recognition (lightweight) | Single person 7.1ms | Object detection Attribute recognition |
Object detection：182M Attribute recognition：86M |
| Falling detection | Single person 10ms | Multi-object tracking Keypoint detection Behavior detection based on key points |
Multi-object tracking：182M Keypoint detection：101M Behavior detection based on key points: 21.8M |
| Intrusion detection | 31.8ms | Multi-object tracking | 182M |
| Fighting detection | 19.7ms | Video classification | 90M |
| Smoking detection | Single person 15.1ms | Object detection Object detection based on Human Id |
Object detection：182M Object detection based on Human ID: 27M |
| Phoning detection | Single person ms | Object detection Image classification based on Human ID |
Object detection：182M Image classification based on Human ID：45M |
Please refer to docs for details.
6. Industrial real-time vehicle analysis tool-PP Vehicle
| Task | End-to-End Speed（ms） | Model | Size |
|---|---|---|---|
| Vehicle detection (high precision) | 25.7ms | object detection | 182M |
| Vehicle detection (lightweight) | 13.2ms | object detection | 27M |
| Vehicle tracking (high precision) | 40ms | multi-object tracking | 182M |
| Vehicle tracking (lightweight) | 25ms | multi-object tracking | 27M |
| Plate Recognition | 4.68ms | plate detection plate recognition |
Plate detection：3.9M Plate recognition：12M |
| Vehicle attribute | 7.31ms | attribute recognition | 7.2M |
Please refer to docs for details.
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Configuration
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Compression based on PaddleSlim
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Advanced development
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[Theoretical foundation] Object detection 7-day camp: Overview of object detection tasks, details of RCNN series object detection algorithm and YOLO series object detection algorithm, PP-YOLO optimization strategy and case sharing, introduction and practice of AnchorFree series algorithm
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[Industrial application] AI Fast Track industrial object detection technology and application: Super object detection algorithms, real-time pedestrian analysis system PP-Human, breakdown and practice of object detection industrial application
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[Industrial features] 2022.3.26 Smart City Industry Seven-Day Class : Urban planning, Urban governance, Smart governance service, Traffic management, community governance.
- Deployment of PaddleDetection for Windows I
- Deployment of PaddleDetection for Windows II
- Deployment of PaddleDetection on Jestson Nano
- How to deploy YOLOv3 model on Raspberry Pi for Helmet detection
- Use SSD-MobileNetv1 for a project -- From dataset to deployment on Raspberry Pi
Please refer to the Release note for more details about the updates
PaddlePaddle is provided under the Apache 2.0 license
We appreciate your contributions and your feedback！
- Thank Mandroide for code cleanup and
- Thank FL77N for
Sparse-RCNNmodel - Thank Chen-Song for
Swin Faster-RCNNmodel - Thank yangyudong, hchhtc123 for developing PP-Tracking GUI interface
- Thank Shigure19 for developing PP-TinyPose fitness APP
- Thank manangoel99 for Wandb visualization methods
@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}
}