From 599c0d0cef07ceba1e19d83ccdeb3ac6c739b1ec Mon Sep 17 00:00:00 2001 From: Kaipeng Deng Date: Tue, 2 Nov 2021 19:44:26 +0800 Subject: [PATCH] polish changelog (#4422) --- README_cn.md | 6 +----- README_en.md | 6 +----- docs/CHANGELOG.md | 4 +++- docs/CHANGELOG_en.md | 35 +++++++++++++++++++++++++++++++++-- 4 files changed, 38 insertions(+), 13 deletions(-) diff --git a/README_cn.md b/README_cn.md index 7386cbcaa..caf5b510b 100644 --- a/README_cn.md +++ b/README_cn.md @@ -299,11 +299,7 @@ PaddleDetection为基于飞桨PaddlePaddle的端到端目标检测套件，提 ## 版本更新 -v2.2版本已经在`08/2021`发布，全新发布Transformer检测系列模型，新增关键点检测Dark HRNet模型，新增人头、车辆跟踪垂类模型，发布旋转框检测S2ANet优化模型，主流模型支持batch size > 1预测部署，详细内容请参考[版本更新文档](docs/CHANGELOG.md) - -v2.1版本已经在`05/2021`发布，全新发布关键点检测和多目标跟踪能力，支持无标注框检测，发布PPYOLO系列模型压缩模型，新增ONNX模型导出教程，详细内容请参考[版本更新文档](docs/CHANGELOG.md)。 - -v2.0版本已经在`04/2021`发布，全面支持动态图版本，新增支持BlazeFace, PSSDet等系列模型和大量骨干网络，发布PP-YOLO v2, PP-YOLO tiny和旋转框检测S2ANet模型。支持模型蒸馏、VisualDL，新增动态图预测部署benchmark，详细内容请参考[版本更新文档](docs/CHANGELOG.md)。 +版本更新内容请参考[版本更新文档](docs/CHANGELOG.md) ## 许可证书 diff --git a/README_en.md b/README_en.md index 19e2233c6..1b54a3f36 100644 --- a/README_en.md +++ b/README_en.md @@ -305,11 +305,7 @@ The relationship between COCO mAP and FPS on Qualcomm Snapdragon 865 of represen ## Updates -v2.2 was released at `08/2021`, release Transformer detection models, release Dark HRNet keypoint detection model, release tracking models of head and vehicle, release optimized S2ANet model, inference with batch size > 1 supported for main architectures. Please refer to [change log](docs/CHANGELOG_en.md) for details. - -v2.1 was released at `05/2021`, Release Keypoint Detection and Multi-Object Tracking. Release model compression for PPYOLO series. Update documents such as export ONNX model. Please refer to [change log](docs/CHANGELOG_en.md) for details. - -v2.0 was released at `04/2021`, fully support dygraph version, which add BlazeFace, PSS-Det and plenty backbones, release `PP-YOLOv2`, `PP-YOLO tiny` and `S2ANet`, support model distillation and VisualDL, add inference benchmark, etc. Please refer to [change log](docs/CHANGELOG_en.md) for details. +Updates please refer to [change log](docs/CHANGELOG_en.md) for details. ## License diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md index 9432d4622..617deeaee 100644 --- a/docs/CHANGELOG.md +++ b/docs/CHANGELOG.md @@ -1,3 +1,5 @@ +简体中文 | [English](./CHANGELOG_en.md) + # 版本更新信息 ## 最新版本信息 @@ -17,7 +19,7 @@ - 发布针对EdgeBoard优化的PP-YOLO-EB模型 - 跟踪 - - 发布FairMOT高精度模型、小尺度模型和轻量级模型 + - 发布FairMot高精度模型、小尺度模型和轻量级模型 - 发布行人、人头和车辆实跟踪垂类模型库，覆盖航拍监控、自动驾驶、密集人群、极小目标等场景 - DeepSORT模型适配PP-YOLO, PP-PicoDet等更多检测器 diff --git a/docs/CHANGELOG_en.md b/docs/CHANGELOG_en.md index dd349d08e..f65e85fed 100644 --- a/docs/CHANGELOG_en.md +++ b/docs/CHANGELOG_en.md @@ -1,7 +1,38 @@ +English | [简体中文](./CHANGELOG.md) + # Version Update Information ## Last Version Information +### 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 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: @@ -13,7 +44,7 @@ - Model optimization: - AlignConv optimization model was released by S2ANet, and DOTA dataset mAP was optimized to 74.0 -- Predict deployment +- 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 @@ -23,7 +54,7 @@ - New TensorRT version notes to Windows Predictive Deployment documentation - FAQ documents are updated -- Problem fixes: +- Bug fixes: - Fixed PP-YOLO series model training convergence problem - Fixed the problem of no label data training when batch_size > 1 -- GitLab