- Release PP-YOLOE object detection models, achieve mAP as 51.4% on COCO test dataset and 78.1 FPS on Nvidia V100, 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
- Function Optimize:
- Support AMP training, enable with `--amp`
- 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