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English | [简体中文](./CHANGELOG.md)
# Version Update Information
## Last Version Information
### 2.6(02.15/2023)
- Featured model
- Release rotated object detector PP-YOLOE-R：SOTA Anchor-free rotated object detection model with high accuracy and efficiency. It has a series of models, named s/m/l/x, for cloud and edge devices and avoids using special operators to be deployed friendly with TensorRT.
- Release small object detector PP-YOLOE-SOD: End-to-end detection pipeline based on sliced images and SOTA model on VisDrone based on original images.
- Release crowded object detector: Crowded object detection model with top accuracy on SKU dataset.
- Functions in different scenarios
- Release real-time object detection model on edge device in PP-Human v2. The model reaches 45.7mAP and 80FPS on Jetson AGX
- Release real-time object detection model on edge device in PP-Vehicle. The model reaches 53.5mAP and 80FPS on Jetson AGX
- Support multi-stream deployment in PP-Human v2 and PP-Vehicle. Achieved 20FPS in 4-stream deployment on Jetson AGX
- Support retrograde and press line detection in PP-Vehicle
- Cutting-edge algorithms
- Release YOLOv8 and YOLOv6 3.0 in YOLO Family
- Release object detection algorithm DINO, YOLOF
- Rich ViTDet series including PP-YOLOE+ViT_base, Mask RCNN + ViT_base, Mask RCNN + ViT_large
- Release MOT algorithm CenterTrack
- Release oriented object detection algorithm FCOSR
- Release instance segmentation algorithm QueryInst
- Release 3D keypoint detection algorithm Metro3d
- Release distillation algorithm FGD，LD，CWD and PP-YOLOE+ distillation with improvement of 1.1+ mAP
- Release SSOD algorithm DenseTeacher and adapt for PP-YOLOE+
- Release few shot finetuning algorithm, including Co-tuning and Contrastive learning
- Framework capabilities
- New functions
- Release Grad-CAM for heatmap visualization. Support Faster RCNN, Mask RCNN, PP-YOLOE, BlazeFace, SSD, RetinaNet.
- Improvement and fixes
- Support python 3.10
- Fix EMA for no-grad parameters
- Simplify PP-YOLOE architecture
- Support AdamW for Paddle 2.4.1
### 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.