English | [简体中文](./CHANGELOG.md) # Version Update Information ## Last Version Information ### 2.5(08.26/2022) - Featured model - PP-YOLOE+： - Released PP-YOLOE+ model, with a 0.7%-2.4% mAP improvement on COCO test2017. 3.75 times faster model training convergence rate and 1.73-2.3 times faster end-to-end inference speed - Released pre-trained models for smart agriculture, night security detection, and industrial quality inspection with 1.3%-8.1% mAP accuracy improvement - supports 10 high-performance training deployment capabilities, including distributed training, online quantization, and serving deployment. We also provide more than five new deployment demos, such as C++/Python Serving, TRT native inference, and ONNX Runtime - PP-PicoDet： - Release the PicoDet-NPU model to support full quantization of model deployment - Add PicoDet layout analysis model with 0.5% mAP accuracy improvement due to FGD distillation algorithm - PP-TinyPose - Release PP-TinyPose Plus with 9.1% end-to-end AP improvement for business data sets such as physical exercises, dance, and other scenarios - Covers unconventional movements such as turning to one side, lying down, jumping, high lift - Add stabilization module (via filter) to significantly improve the stability at key points - Functions in different scenarios - PP-Human v2 - Release PP-Human v2, which supports four industrial features: behavioral recognition case zoo for multiple solutions, human attribute recognition, human traffic detection and trajectory retention, as well as high precision multi-camera tracking - Upgraded underlying algorithm capabilities: 1.5% mAP improvement in pedestrian detection accuracy; 10.2% MOTA improvement in pedestrian tracking accuracy, 34% speed improvement in the lightweight model; 0.6% ma improvement in attribute recognition accuracy, 62.5% speed improvement in the lightweight model - Provides comprehensive tutorials covering data collection and annotation, model training optimization and prediction deployment, and post-processing code modification in the pipeline - Supports online video streaming input - Become more user-friendly with a one-line code execution function that automates the process determination and model download - PP-Vehicle - Launch PP-Vehicle, which supports four core functions for traffic application: license plate recognition, attribute recognition, traffic flow statistics, and violation detection - License plate recognition supports a lightweight model based on PP-OCR v3 - Vehicle attribute recognition supports a multi-label classification model based on PP-LCNet - Compatible with various data input formats such as pictures, videos and online video streaming - Become more user-friendly with a one-line code execution function that automates the process determination and model download - Cutting-edge algorithms - YOLO Family - Release the full range of YOLO family models covering the cutting-edge detection algorithms YOLOv5, YOLOv6 and YOLOv7 - Based on the ConvNext backbone network, YOLO's algorithm training periods are reduced by 5-8 times with accuracy generally improving by 1%-5% mAP; Thanks to the model compression strategy, its speed increased by over 30% with no loss of precision. - Newly add high precision detection model based on [ViT](configs/vitdet) backbone network, with a 55.7% mAP accuracy on the COCO dataset - Newly add multi-object tracking model [OC-SORT](configs/mot/ocsort) - Newly add [ConvNeXt](configs/convnext) backbone network. - Industrial application - Intelligent physical exercise recognition based on PP-TinyPose Plus - Fighting recognition based on PP-Human - Business hall visitor analysis based on PP-Human - Vehicle structuring analysis based on PP-Vehicle - PCB board defect detection based on PP-YOLOE+ - Framework capabilities - New functions - Release auto-compression tools and demos, 0.3% mAP accuracy loss for PP-YOLOE l version, while 13% speed increase for V100 - Release PaddleServing python/C++ and ONNXRuntime deployment demos - Release PP-YOLOE end-to-end TensorRT deployment demo - Release FGC distillation algorithm with RetinaNet accuracy improved by 3.3% - Release distributed training documentation - Improvement and fixes - Fix compilation problem with Windows c++ deployment - Fix problems when saving results of inference data in VOC format - Fix the detection box output of FairMOT c++ deployment - Rotating frame detection model S2ANet supports batch size>1 deployment ### 2.4(03.24/2022) - PP-YOLOE： - Release PP-YOLOE object detection models, achieve mAP as 51.6% on COCO test dataset and 78.1 FPS on Nvidia V100 by PP-YOLOE-l, reach SOTA performance for object detection on GPU`` - Release series models: s/m/l/x, and support deployment base on TensorRT & ONNX - Spport AMP training and training speed is 33% faster than PP-YOLOv2 - PP-PicoDet: - Release enhanced models of PP-PicoDet, mAP promoted ~2% on COCO and inference speed accelerated 63% on CPU - Release PP-PicoDet-XS model with 0.7M parameters - Post-processing integrated into the network to optimize deployment pipeline - PP-Human： - Release PP-Human human analysis pipeline，including pedestrian detection, attribute recognition, human tracking, multi-camera tracking, human statistics, action recognition. Supporting deployment with TensorRT - Release StrongBaseline model for attribute recognition - Release Centroid model for ReID - Release ST-GCN model for falldown action recognition - Model richness: - Publish YOLOX object detection model, release series models: nano/tiny/s/m/l/x, and YOLOX-x achieves mAP as 51.8% on COCO val2017 dataset - Function Optimize： - Optimize 20% training speed when training with EMA, improve saving method of EMA weights - Support saving inference results in COCO format - Deployment Optimize： - Support export ONNX model by Paddle2ONNX for all RCNN models - Supoort export model with fused decode OP for SSD models to enhance inference speed in edge side - Support export NMS to TensorRT model, optmize inference speed on TensorRT ### 2.3(11.03/2021) - Feature models: - Object detection: The lightweight object detection model PP-PicoDet, performace and inference speed reaches SOTA on mobile side - Keypoint detection: The lightweight keypoint detection model PP-TinyPose for mobile side - Model richness: - Object detection: - Publish Swin-Transformer object detection model - Publish TOOD(Task-aligned One-stage Object Detection) model - Publish GFL(Generalized Focal Loss) object detection model - Publish Sniper optimization method for tiny object detection, supporting Faster RCNN and PP-YOLO series models - Publish PP-YOLO optimized model PP-YOLO-EB for EdgeBoard - Multi-object tracking: - Publish Real-time tracking system PP-Tracking - Publish high-precision, small-scale and lightweight model based on FairMot - Publish real-time tracking model zoo for pedestrian, head and vehicle tracking, including scenarios such as aerial surveillance, autonomous driving, dense crowds, and tiny object tracking - DeepSort support PP-YOLO, PP-PicoDet as object detector - Keypoint detection: - Publish Lite HRNet model - Inference deployment: - Support NPU deployment for YOLOv3 series - Support C++ deployment for FairMot - Support C++ and PaddleLite deployment for keypoint detection series model - Documents: - Add series English documents ### 2.2(08.10/2021) - Model richness: - Publish the Transformer test model: DETR, Deformable DETR, Sparse RCNN - Key point test new Dark model, release Dark HRNet model - Publish the MPII dataset HRNet keypoint detection model - Release head and vehicle tracking vertical model - Model optimization: - AlignConv optimization model was released by S2ANet, and DOTA dataset mAP was optimized to 74.0 - Inference deployment - Mainstream models support batch size>1 predictive deployment, including YOLOv3, PP-YOLO, Faster RCNN, SSD, TTFNet, FCOS - New addition of target tracking models (JDE, Fair Mot, Deep Sort) Python side prediction deployment support, and support for TensorRT prediction - FairMot joint key point detection model deployment Python side predictive deployment support - Added support for key point detection model combined with PP-YOLO prediction deployment - Documents: - New TensorRT version notes to Windows Predictive Deployment documentation - FAQ documents are updated - Bug fixes: - Fixed PP-YOLO series model training convergence problem - Fixed the problem of no label data training when batch_size > 1 ### 2.1(05.20/2021) - Model richness enhancement: - Key point model: HRNet, HigherHRNet - Publish the multi-target tracking model: DeepSort, FairMot, JDE - Basic framework Capabilities: - Supports training without labels - Forecast deployment: - Paddle Inference YOLOv3 series model support batch_size>1 prediction - Rotating frame detection S2ANet model prediction deployment is open - Incremental quantization model benchmark - Add dynamic graph model and static graph model: Paddle-Lite demo - Detection model compression: - Release PP-YOLO series model compression model - Documents: - Update quick start, forecast deployment and other tutorial documentation - Added ONNX model export tutorial - Added the mobile deployment document ### 2.0(04.15/2021) **Description:** Since version 2.0, dynamic graphs are used as the default version of Paddle Detection, the original `dygraph` directory is switched to the root directory, and the original static graph implementation is moved to the `static` directory. - Enhancement of dynamic graph model richness: - PP-YOLOv2 and PP-YOLO tiny models were published. The accuracy of PP-YOLOv2 COCO Test dataset reached 49.5%, and the prediction speed of V100 reached 68.9 FPS - Release the rotary frame detection model S2ANet - Release the two-phase utility model PSS-Det - Publish the face detection model Blazeface - New basic module: - Added SENet, GhostNet, and Res2Net backbone networks - Added VisualDL training visualization support - Added single precision calculation and PR curve drawing function - The YOLO models support THE NHWC data format - Forecast deployment: - Publish forecast benchmark data for major models - Adaptive to TensorRT6, support TensorRT dynamic size input, support TensorRT int8 quantitative prediction - 7 types of models including PP-YOLO, YOLOv3, SSD, TTFNet, FCOS, Faster RCNN are deployed in Python/CPP/TRT prediction on Linux, Windows and NV Jetson platforms - Detection model compression: - Distillation: Added dynamic map distillation support and released YOLOv3-MobileNetV1 distillation model - Joint strategy: new dynamic graph prunning + distillation joint strategy compression scheme, and release YOLOv3-MobileNetV1 prunning + distillation compression model - Problem fix: Fixed dynamic graph quantization model export problem - Documents: - New English document of dynamic graph: including homepage document, getting started, quick start, model algorithm, new dataset, etc - Added both English and Chinese installation documents of dynamic diagrams - Added configuration file templates and description documents of dynamic graph RCNN series and YOLO series ## Historical Version Information ### 2.0-rc(02.23/2021) - Enhancement of dynamic graph model richness: - Optimize networking and training mode of RCNN models, and improve accuracy of RCNN series models (depending on Paddle Develop or version 2.0.1) - Added support for SSDLite, FCOS, TTFNet, SOLOv2 series models - Added pedestrian and vehicle vertical object detection models - New dynamic graph basic module: - Added MobileNetV3 and HRNet backbone networks - Improved roi-align calculation logic for RCNN series models (depending on Paddle Develop or version 2.0.1) - Added support for Synchronized Batch Norm - Added support for Modulated Deformable Convolution - Forecast deployment: - Publish dynamic diagrams in python, C++, and Serving deployment solution and documentation. Support Faster RCNN, Mask RCNN, YOLOv3, PPYOLO, SSD, TTFNet, FCOS, SOLOv2 and other models to predict deployment - Dynamic graph prediction deployment supports TensorRT mode FP32, FP16 inference acceleration - Detection model compression: - Prunning: Added dynamic graph prunning support, and released YOLOv3-MobileNetV1 prunning model - Quantization: Added quantization support of dynamic graph, and released quantization models of YOLOv3-MobileNetV1 and YOLOv3-MobileNetV3 - Documents: - New Dynamic Diagram tutorial documentation: includes installation instructions, quick start, data preparation, and training/evaluation/prediction process documentation - New advanced tutorial documentation for dynamic diagrams: includes documentation for model compression and inference deployment - Added dynamic graph model library documentation ### v2.0-beta(12.20/2020) - Dynamic graph support: - Support for Faster-RCNN, Mask-RCNN, FPN, Cascade Faster/Mask RCNN, YOLOv3 and SSD models, trial version. - Model upgrade: - Updated PP-YOLO Mobile-Netv3 large and small models with improved accuracy, and added prunning and distillation models. - New features: - Support VisualDL visual data preprocessing pictures. - Bug fix: - Fix Blaze Face keypoint prediction bug. ### v0.5.0(11/2020) - Model richness enhancement: - SOLOv2 series models were released, in which the SOLOv2-Light-R50-VD-DCN-FPN model achieved 38.6 FPS on a single gpu V100, accelerating by 24%, and the accuracy of COCO verification set reached 38.8%, improving by 2.4 absolute percentage points. - Added Android mobile terminal detection demo, including SSD, YOLO series model, can directly scan code installation experience. - Mobile terminal model optimization: - Added to PACT's new quantization strategy, YOLOv3 Mobilenetv3 is 0.7% better than normal quantization on COCO datasets. - Ease of use and functional components: - Enhance the function of generate_proposal_labels operator to avoid nan risk of the model. - Fixed several problems with deploy python and C++ prediction. - Unified COCO and VOC datasets under the evaluation process, support the output of a single class of AP and P-R curves. - PP-YOLO supports rectangular input images. - Documents: - Added object detection whole process tutorial, added Jetson platform deployment tutorial. ### v0.4.0(07/2020) - Model richness enhancement: - The PPYOLO model was released. The accuracy of COCO dataset reached 45.2%, and the prediction speed of single gpu V100 reached 72.9 FPS, which was better than that of YOL Ov4 model. - New TTFNet model, base version aligned with competing products, COCO dataset accuracy up to 32.9%. - New HTC model, base version aligned with competing products, COCO dataset accuracy up to 42.2%. - BlazeFace key point detection model was added, with an accuracy of 85.2% in Wider-Face's Easy-Set. - ACFPN model was added, and the accuracy of COCO dataset reached 39.6%. - General object detection model (including 676 classes) on the publisher side. On the COCO dataset with the same strategy, when V100 is 19.5FPS, the COCO mAP can reach 49.4%. - Mobile terminal model optimization: - Added SSD Lite series optimization models, including Ghost Net Backbone, FPN components, etc., with accuracy improved by 0.5% and 1.5%. - Ease of use and functional components: - Add GridMask, Random Erasing data enhancement method. - Added support for Matrix NMS. - EMA(Exponential Moving Average) training support. - The new multi-machine training method, the average acceleration ratio of two machines to single machine is 80%, multi-machine training support needs to be further verified. ### v0.3.0(05/2020) - Model richness enhancement: - Efficientdet-D0 model added, speed and accuracy is better than competing products. - Added YOLOv4 prediction model, precision aligned with competing products; Added YOLOv4 fine tuning training on Pascal VOC datasets with accuracy of 85.5%. - YOLOv3 added MobileNetV3 backbone network, COCO dataset accuracy reached 31.6%. - Add Anchor-free model FCOS, the accuracy is better than competing products. - Anchor-free model Cornernet Squeeze was added, the accuracy was better than competing products, and the accuracy of COCO dataset of optimized model was 38.2% and +3.7%, 5% faster than YOL Ov3 Darknet53. - The CascadeRCNN-ResNet50vd model, which is a practical object detection model on the server side, is added, and its speed and accuracy are better than that of the competitive EfficientDet. - Mobile terminal launched three models: - SSSDLite model: SSDLite-Mobilenetv3 small/large model, with better accuracy than competitors. - YOLOv3 Mobile solution: The YOLOv3-MobileNetv3 model accelerates 3.5 times after compression, which is faster and more accurate than the SSD Lite model of competing products. - RCNN Mobile terminal scheme: CascadeRCNN-MobileNetv3, after series optimization, launched models with input images of 320x320 and 640x640 respectively, with high cost performance for speed and accuracy. - Anticipate deployment refactoring: - New Python prediction deployment process, support for RCNN, YOLO, SSD, Retina Net, face models, support for video prediction. - Refactoring C++ predictive deployment to improve ease of use. - Ease of use and functional components: - Added Auto Augment data enhancement. - Upgrade the detection library document structure. - Support shape matching automatically by transfer learning. - Optimize memory footprint during mask branch evaluation. ### v0.2.0(02/2020) - The new model: - Added CBResNet model. - Added LibraRCNN model. - The accuracy of YOLOv3 model was further improved, and the accuracy based on COCO data reached 43.2%, 1.4% higher than the previous version. - New Basic module: - Trunk network: CBResNet is added. - Loss module: Loss of YOLOv3 supports fine-grained OP combinations. - Regular module: Added the Drop Block module. - Function optimization and improvement: - Accelerate YOLOv3 data preprocessing and increase the overall training speed by 40%. - Optimize data preprocessing logic to improve ease of use. - dd face detection prediction benchmark data. - Added C++ prediction engine Python API prediction example. - Detection model compression: - prunning: Release MobileNet-YOLOv3 prunning scheme and model, based on VOC data FLOPs 69.6%, mAP + 1.4%, based on COCO DATA FLOPS 28.8%, mAP + 0.9%; Release ResNet50vd-DCN-YOLOv3 clipped solution and model based on COCO datasets 18.4%, mAP + 0.8%. - Distillation: Release MobileNet-YOLOv3 distillation scheme and model, based on VOC data mAP + 2.8%, COCO data mAP + 2.1%. - Quantification: Release quantification models of YOLOv3 Mobile Net and Blaze Face. - Prunning + distillation: release MobileNet-YOLOv3 prunning + distillation solution and model, 69.6% based on COCO DATA FLOPS, 64.5% based on TensorRT prediction acceleration, 0.3% mAP; Release ResNet50vd-DCN-YOLOv3 tailoring + distillation solution and model, 43.7% based on COCO Data FLOPS, 24.0% based on TensorRT prediction acceleration, mAP + 0.6%. - Search: Open source Blaze Face Nas complete search solution. - Predict deployment: - Integrated TensorRT, support FP16, FP32, INT8 quantitative inference acceleration. - Document: - Add detailed data preprocessing module to introduce documents and implement custom data Reader documents. - Added documentation on how to add algorithm models. - Document deployment to the web site: https://paddledetection.readthedocs.io ### 12/2019 - Add Res2Net model. - Add HRNet model. - Add GIOU loss and DIOU loss。 ### 21/11/2019 - Add CascadeClsAware RCNN model. - Add CBNet, ResNet200 and Non-local model. - Add SoftNMS. - Add Open Image V5 dataset and Objects365 dataset model ### 10/2019 - Added enhanced YOLOv3 model with accuracy up to 41.4%. - Added Face detection models BlazeFace and Faceboxes. - Rich COCO based models, accuracy up to 51.9%. - Added CA-Cascade-RCNN, one of the best single models to win on Objects365 2019 Challenge. - Add pedestrian detection and vehicle detection pre-training models. - Support FP16 training. - Added cross-platform C++ inference deployment scheme. - Add model compression examples. ### 2/9/2019 - Add GroupNorm model. - Add CascadeRCNN+Mask model. ### 5/8/2019 - Add Modulated Deformable Convolution series model ### 29/7/2019 - Add detection library Chinese document - Fixed an issue where R-CNN series model training was evaluated simultaneously - Add ResNext101-vd + Mask R-CNN + FPN models - Added YOLOv3 model based on VOC dataset ### 3/7/2019 - First release of PaddleDetection Detection library and Detection model library - models：Faster R-CNN, Mask R-CNN, Faster R-CNN+FPN, Mask R-CNN+FPN, Cascade-Faster-RCNN+FPN, RetinaNet, YOLOv3, 和SSD.