github.com/PaddlePaddle/PaddleDetection @v2.9.0 sqlite

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A High-Efficient Development Toolkit for Object Detection based on PaddlePaddle

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Product Update

• 🔥 2022.11.15：SOTA rotated object detector and small object detector based on PP-YOLOE
• Rotated object detector PP-YOLOE-R
  • SOTA Anchor-free rotated object detection model with high accuracy and efficiency
  • A series of models, named s/m/l/x, for cloud and edge devices
  • Avoiding using special operators to be deployed friendly with TensorRT.

• Small object detector PP-YOLOE-SOD

  • End-to-end detection pipeline based on sliced images
  • SOTA model on VisDrone based on original images.

• 2022.8.26：PaddleDetection releasesrelease/2.5 version

• 🗳 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

• 🔮 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.

• 💡 Cutting-edge algorithms：

  • Release PaddleYOLO which overs classic and latest models of YOLO family: 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, YOLOv7 and YOLOv8
  • 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.

• 📋 Industrial applications: Newly add Smart Fitness, Fighting recognition, and Visitor Analysis.

• 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.

• More releases

Brief Introduction

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.

Features

• 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.

Exchanges

• If you have any question or suggestion, please give us your valuable input via GitHub Issues

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Kit Structure