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🔥 2022.11.15:SOTA rotated object detector and small object detector based on PP-YOLOE
2022.8.26:PaddleDetection releasesrelease/2.5 version
🗳 Model features:
🔮 Functions in different scenarios
💡 Cutting-edge algorithms:
📋 Industrial applications: Newly add Smart Fitness, Fighting recognition, and Visitor Analysis.
2022.3.24:PaddleDetection releasedrelease/2.4 version
PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle. Providing over 30 model algorithm and over 300 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.
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
|
Common
KeyPoint
FPN
Loss
Post-processing
Speed
|
Details
|
The comparison between COCO mAP and FPS on Tesla V100 of representative models of each architectures and backbones.
Clarification:
ViT
stands for ViT-Cascade-Faster-RCNN
, which has highest mAP on COCO as 55.7%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% in PaddleDetection modelsPP-YOLOE
are optimized PP-YOLO v2
. It reached accuracy as 51.4% on COCO dataset, inference speed as 78.1 FPS on Tesla V100PP-YOLOE+
are optimized PP-YOLOE
. It reached accuracy as 53.3% on COCO dataset, inference speed as 78.1 FPS on Tesla V100The comparison between COCO mAP and FPS on Qualcomm Snapdragon 865 processor of models on mobile devices.
Clarification:
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 |
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 |
YOLOv7-l | 51.0 | 135.0 | 链接 | 下载地址 |
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 |
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 |
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 | - |
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.
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.
Configuration
Compression based on PaddleSlim
Advanced development
[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
[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
[Industrial features] 2022.3.26 Smart City Industry Seven-Day Class : Urban planning, Urban governance, Smart governance service, Traffic management, community governance.
[Academic exchange] 2022.9.27 YOLO Vision Event: As the first YOLO-themed event, PaddleDetection was invited to communicate with the experts in the field of Computer Vision around the world.
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!
Sparse-RCNN
modelSwin Faster-RCNN
model@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}
}
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