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PaddleDetection
未验证 提交 a1b84233 编写于 作者: F Feng Ni 提交者： GitHub
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[Doc][PaddleYOLO] update docs for YOLOv8 and YOLOv6 v3.0 (#7632) 

* update docs for yolov8 and yolov6 3.0, test=document_fix

* fix readme Quote, test=document_fix
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README_cn.md
浏览文件 @ a1b84233
......@@ -86,6 +86,7 @@ PaddleDetection整理工业、农业、林业、交通、医疗、金融、能
- 新增[少样本迁移学习](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/few-shot);
- 新增[半监督检测模型](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/semi_det);
- 新增[YOLOv8](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8);
- 更新[YOLOv6-v3.0](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6);
- [🎗️产业特色模型|产业工具](#️产业特色模型产业工具-1)：
- 发布**旋转框检测模型**[PP-YOLOE-R](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rotate/ppyoloe_r)：Anchor-free旋转框检测SOTA模型，精度速度双高、云边一体，s/m/l/x四个模型适配不用算力硬件、部署友好，避免使用特殊算子，能够轻松使用TensorRT加速；
- 发布**小目标检测模型**[PP-YOLOE-SOD](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/smalldet)：基于切图的端到端检测方案、基于原图的检测模型，精度达VisDrone开源最优；
......@@ -116,7 +117,7 @@ PaddleDetection整理工业、农业、林业、交通、医疗、金融、能
- **⚽️2022卡塔尔世界杯专题**
- `文章传送门`：[世界杯决赛号角吹响！趁周末来搭一套足球3D+AI量化分析系统吧！](https://mp.weixin.qq.com/s/koJxjWDPBOlqgI-98UsfKQ)
<div align="center">
<img src="https://user-images.githubusercontent.com/61035602/208036574-f151a7ff-a5f1-4495-9316-a47218a6576b.gif" height = "250" caption='' />
<p></p>
......@@ -124,7 +125,7 @@ PaddleDetection整理工业、农业、林业、交通、医疗、金融、能
- **🔍旋转框小目标检测专题**
- `文章传送门`：[Yes, PP-YOLOE！80.73mAP、38.5mAP，旋转框、小目标检测能力双SOTA！](https://mp.weixin.qq.com/s/6ji89VKqoXDY6SSGkxS8NQ)
<div align="center">
<img src="https://user-images.githubusercontent.com/61035602/208037368-5b9f01f7-afd9-46d8-bc80-271ccb5db7bb.png" height = "220" caption='' />
<p></p>
......@@ -258,10 +259,10 @@ PaddleDetection整理工业、农业、林业、交通、医疗、金融、能
<td>
<ul>
<li><a href="ppdet/modeling/losses/smooth_l1_loss.py">Smooth-L1</a></li>
<li><a href="ppdet/modeling/losses/detr_loss.py">Detr Loss</a></li>
<li><a href="ppdet/modeling/losses/detr_loss.py">Detr Loss</a></li>
<li><a href="ppdet/modeling/losses/fairmot_loss.py">Fairmot Loss</a></li>
<li><a href="ppdet/modeling/losses/fcos_loss.py">Fcos Loss</a></li>
<li><a href="ppdet/modeling/losses/gfocal_loss.py">GFocal Loss</a></li>
<li><a href="ppdet/modeling/losses/gfocal_loss.py">GFocal Loss</a></li>
<li><a href="ppdet/modeling/losses/jde_loss.py">JDE Loss</a></li>
<li><a href="ppdet/modeling/losses/keypoint_loss.py">KeyPoint Loss</a></li>
<li><a href="ppdet/modeling/losses/solov2_loss.py">SoloV2 Loss</a></li>
......@@ -293,7 +294,7 @@ PaddleDetection整理工业、农业、林业、交通、医疗、金融、能
</ul>
<li><b>Common</b></li>
<ul>
<ul>
<ul>
<li><a href="ppdet/modeling/backbones/resnet.py#L41">Sync-BN</a></li>
<li><a href="configs/gn/README.md">Group Norm</a></li>
<li><a href="configs/dcn/README.md">DCNv2</a></li>
......@@ -350,13 +351,14 @@ PaddleDetection整理工业、农业、林业、交通、医疗、金融、能
<li><a href="configs/cascade_rcnn/README.md">Cascade-RCNN</a></li>
<li><a href="configs/rcnn_enhance">PSS-Det</a></li>
<li><a href="configs/retinanet/README.md">RetinaNet</a></li>
<li><a href="https://github.com/PaddlePaddle/PaddleYOLO">YOLOv3</a></li>
<li><a href="https://github.com/PaddlePaddle/PaddleYOLO">YOLOv5</a></li>
<li><a href="https://github.com/PaddlePaddle/PaddleYOLO">YOLOX</a></li>
<li><a href="https://github.com/PaddlePaddle/PaddleYOLO">YOLOv6</a></li>
<li><a href="https://github.com/PaddlePaddle/PaddleYOLO">YOLOv7</a></li>
<li><a href="https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8">YOLOv8</a></li>
<li><a href="https://github.com/PaddlePaddle/PaddleYOLO">RTMDet</a></li>
<li><a href="configs/yolov3/README.md">YOLOv3</a></li>
<li><a href="configs/yolof/README.md">YOLOF</a></li>
<li><a href="configs/yolox/README.md">YOLOX</a></li>
<li><a href="https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov5">YOLOv5</a></li>
<li><a href="https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov6">YOLOv6</a></li>
<li><a href="https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov7">YOLOv7</a></li>
<li><a href="https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov8">YOLOv8</a></li>
<li><a href="https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/rtmdet">RTMDet</a></li>
<li><a href="configs/ppyolo/README_cn.md">PP-YOLO</a></li>
<li><a href="configs/ppyolo#pp-yolo-tiny">PP-YOLO-Tiny</a></li>
<li><a href="configs/picodet">PP-PicoDet</a></li>
......@@ -368,6 +370,7 @@ PaddleDetection整理工业、农业、林业、交通、医疗、金融、能
<li><a href="configs/ssd/README.md">SSD</a></li>
<li><a href="configs/centernet">CenterNet</a></li>
<li><a href="configs/fcos">FCOS</a></li>
<li><a href="configs/rotate/fcosr">FCOSR</a></li>
<li><a href="configs/ttfnet">TTFNet</a></li>
<li><a href="configs/tood">TOOD</a></li>
<li><a href="configs/gfl">GFL</a></li>
......@@ -468,7 +471,7 @@ PaddleDetection整理工业、农业、林业、交通、医疗、金融、能
- Cascade-Faster-RCNN为Cascade-Faster-RCNN-ResNet50vd-DCN，PaddleDetection将其优化到COCO数据mAP为47.8%时推理速度为20FPS
- PP-YOLOE是对PP-YOLO v2模型的进一步优化，L版本在COCO数据集mAP为51.6%，Tesla V100预测速度78.1FPS
- PP-YOLOE+是对PPOLOE模型的进一步优化，L版本在COCO数据集mAP为53.3%，Tesla V100预测速度78.1FPS
- YOLOX和YOLOv5均为基于PaddleDetection复现算法，YOLOv5代码在PaddleYOLO中，参照PaddleYOLO_MODEL
- YOLOX和YOLOv5均为基于PaddleDetection复现算法，YOLOv5代码在[PaddleYOLO](https://github.com/PaddlePaddle/PaddleYOLO)中，参照[PaddleYOLO_MODEL](docs/feature_models/PaddleYOLO_MODEL.md)
- 图中模型均可在[📱模型库](#模型库)中获取
</details>
......@@ -805,4 +808,7 @@ PP-Vehicle囊括四大交通场景核心功能：车牌识别、属性识别、
@misc{ppdet2019,
title={PaddleDetection, Object detection and instance segmentation toolkit based on PaddlePaddle.},
author={PaddlePaddle Authors},
howpublished = {\url{https://github.com/PaddlePaddle/PaddleDetection}},
\ No newline at end of file
howpublished = {\url{https://github.com/PaddlePaddle/PaddleDetection}},
year={2019}
}
```
README_en.md
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......@@ -47,7 +47,7 @@
- 💡 Cutting-edge algorithms：
- Release [PaddleYOLO](https://github.com/PaddlePaddle/PaddleYOLO) which overs classic and latest models of [YOLO family](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/docs/MODEL_ZOO_en.md): 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, and YOLOv7
- Release [PaddleYOLO](https://github.com/PaddlePaddle/PaddleYOLO) which overs classic and latest models of [YOLO family](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/docs/MODEL_ZOO_en.md): 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](configs/vitdet) backbone network, with a 55.7% mAP accuracy on COCO dataset; newly add multi-object tracking model [OC-SORT](configs/mot/ocsort); newly add [ConvNeXt](configs/convnext) backbone network.
- 📋 Industrial applications: Newly add [Smart Fitness](https://aistudio.baidu.com/aistudio/projectdetail/4385813), [Fighting recognition](https://aistudio.baidu.com/aistudio/projectdetail/4086987?channelType=0&channel=0),[ and Visitor Analysis](https://aistudio.baidu.com/aistudio/projectdetail/4230123?channelType=0&channel=0).
......@@ -118,14 +118,21 @@
<li>PSS-Det</li>
<li>RetinaNet</li>
<li>YOLOv3</li>
<li>PP-YOLOv1/v2</li>
<li>YOLOF</li>
<li>YOLOX</li>
<li>YOLOv5</li>
<li>YOLOv6</li>
<li>YOLOv7</li>
<li>YOLOv8</li>
<li>RTMDet</li>
<li>PP-YOLO</li>
<li>PP-YOLO-Tiny</li>
<li>PP-PicoDet</li>
<li>PP-YOLOv2</li>
<li>PP-YOLOE</li>
<li>PP-YOLOE+</li>
<li>PP-YOLOE-R</li>
<li>PP-YOLOE-SOD</li>
<li>YOLOX</li>
<li>YOLOF</li>
<li>PP-YOLOE-R</li>
<li>SSD</li>
<li>CenterNet</li>
<li>FCOS</li>
......@@ -133,7 +140,7 @@
<li>TTFNet</li>
<li>TOOD</li>
<li>GFL</li>
<li>PP-PicoDet</li>
<li>GFLv2</li>
<li>DETR</li>
<li>Deformable DETR</li>
<li>Swin Transformer</li>
......
docs/MODEL_ZOO_cn.md
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......@@ -28,7 +28,7 @@
## 通用设置
- 所有模型均在COCO17数据集中训练和测试。
- [YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov5)、[YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov6)和[YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov7)这3类模型的代码在[PaddleYOLO](https://github.com/PaddlePaddle/PaddleYOLO)中，**PaddleYOLO库开源协议为GPL 3.0**。
- [YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov5)、[YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov6)、[YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov7)和[YOLOv8](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov8)这几类模型的代码在[PaddleYOLO](https://github.com/PaddlePaddle/PaddleYOLO)中，**PaddleYOLO库开源协议为GPL 3.0**。
- 除非特殊说明，所有ResNet骨干网络采用[ResNet-B](https://arxiv.org/pdf/1812.01187)结构。
- **推理时间(fps)**: 推理时间是在一张Tesla V100的GPU上通过'tools/eval.py'测试所有验证集得到，单位是fps(图片数/秒), cuDNN版本是7.5，包括数据加载、网络前向执行和后处理, batch size是1。
......@@ -171,7 +171,7 @@ Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型
请参考[YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov5)
### YOLOv6
### YOLOv6(v3.0)
请参考[YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov6)
......@@ -179,6 +179,10 @@ Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型
请参考[YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov7)
### YOLOv8
请参考[YOLOv8](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov8)
### RTMDet
请参考[RTMDet](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/rtmdet)
......
docs/MODEL_ZOO_en.md
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......@@ -28,7 +28,7 @@
## General Settings
- All models were trained and tested in the COCO17 dataset.
- The codes of [YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov5),[YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov6) and [YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov7) can be found in [PaddleYOLO](https://github.com/PaddlePaddle/PaddleYOLO). Note that **the LICENSE of PaddleYOLO is GPL 3.0**.
- The codes of [YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov5),[YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov6),[YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov7) and [YOLOv8](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov8) can be found in [PaddleYOLO](https://github.com/PaddlePaddle/PaddleYOLO). Note that **the LICENSE of PaddleYOLO is GPL 3.0**.
- Unless special instructions, all the ResNet backbone network using [ResNet-B](https://arxiv.org/pdf/1812.01187) structure.
- **Inference time (FPS)**: The reasoning time was calculated on a Tesla V100 GPU by `tools/eval.py` testing all validation sets in FPS (number of pictures/second). CuDNN version is 7.5, including data loading, network forward execution and post-processing, and Batch size is 1.
......@@ -170,7 +170,7 @@ Please refer to [Model Zoo for PaddleYOLO](https://github.com/PaddlePaddle/Paddl
Please refer to [YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov5)
### YOLOv6
### YOLOv6(v3.0)
Please refer to [YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov6)
......@@ -178,6 +178,10 @@ Please refer to [YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop
Please refer to [YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov7)
### YOLOv8
Please refer to [YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov8)
### RTMDet
Please refer to [RTMDet](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/rtmdet)
......
docs/feature_models/PaddleYOLO_MODEL_en.md
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......@@ -5,11 +5,12 @@
## Introduction
- [Introduction](#Introduction)
- [ModelZoo](#ModelZoo)
- [PP-YOLOE+](#PP-YOLOE+)
- [PP-YOLOE](#PP-YOLOE)
- [YOLOX](#YOLOX)
- [YOLOv5](#YOLOv5)
- [YOLOv6](#YOLOv6)
- [YOLOv7](#YOLOv7)
- [YOLOv8](#YOLOv8)
- [RTMDet](#RTMDet)
- [VOC](#VOC)
- [UserGuide](#UserGuide)
......@@ -18,10 +19,11 @@
## Introduction
**PaddleYOLO** is a YOLO Series toolbox based on [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection), **only relevant codes of YOLO series models are included**. It supports `YOLOv3`,`PP-YOLO`,`PP-YOLOv2`,`PP-YOLOE`,`PP-YOLOE+`,`YOLOX`,`YOLOv5`,`YOLOv6`,`YOLOv7`,`RTMDet` and so on. Welcome to use and build it together!
**PaddleYOLO** is a YOLO Series toolbox based on [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection), **only relevant codes of YOLO series models are included**. It supports `YOLOv3`,`PP-YOLO`,`PP-YOLOv2`,`PP-YOLOE`,`PP-YOLOE+`,`YOLOX`,`YOLOv5`,`YOLOv6`,`YOLOv7`,`YOLOv8`,`RTMDet` and so on. Welcome to use and build it together!
## Updates
* 【2023/01/10】Support [YOLOv8](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8) inference and deploy;
* 【2022/09/29】Support [RTMDet](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/rtmdet) inference and deploy;
* 【2022/09/26】Release [`PaddleYOLO`](https://github.com/PaddlePaddle/PaddleYOLO);
* 【2022/09/19】Support the new version of [`YOLOv6`](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6), including n/t/s/m/l model;
......@@ -29,13 +31,13 @@
**Notes：**
- The Licence of **PaddleYOLO** is **GPL 3.0**, the codes of [YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5),[YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov7) and [YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6) will not be merged into [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection). Except for these three YOLO models, other YOLO models are recommended to use in [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection), **which will be the first to release the latest progress of PP-YOLO series detection model**;
- The Licence of **PaddleYOLO** is **GPL 3.0**, the codes of [YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov5),[YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6),[YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov7) and [YOLOv8](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8) will not be merged into [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection). Except for these three YOLO models, other YOLO models are recommended to use in [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection), **which will be the first to release the latest progress of PP-YOLO series detection model**;
- To use **PaddleYOLO**, **PaddlePaddle-2.3.2 or above is recommended**，please refer to the [official website](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html) to download the appropriate version. **For Windows platforms, please install the paddle develop version**;
- Training **Custom dataset** please refer to [doc](#CustomDataset) and [issue](https://github.com/PaddlePaddle/PaddleYOLO/issues/43). Please **ensure COCO trained weights are loaded as pre-train** at first. We recommend to use YOLO detection model **with a total `batch_size` at least greater than `64` to train**. If the resources are insufficient, please **use the smaller model** or **reduce the input size of the model**. To ensure high detection accuracy, **you'd better never try to using single GPU or total `batch_size` less than `32` for training**;
## ModelZoo
### [PP-YOLOE+](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/ppyoloe)
### [PP-YOLOE](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/ppyoloe)
<details>
<summary> Baseline </summary>
......@@ -147,13 +149,10 @@
| Model | Input Size | images/GPU | Epoch | TRT-FP16-Latency(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) | download | config |
| :------------- | :------- | :-------: | :------: | :---------: | :-----: |:-----: | :-----: |:-----: | :-------------: | :-----: |
| YOLOv6-n | 416 | 32 | 400e | 1.0 | 31.1 | 45.3 | 4.74 | 5.16 |[model](https://paddledet.bj.bcebos.com/models/yolov6_n_416_400e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_n_416_400e_coco.yml) |
| YOLOv6-n | 640 | 32 | 400e | 1.3 | 36.1 | 51.9 | 4.74 | 12.21 |[model](https://paddledet.bj.bcebos.com/models/yolov6_n_400e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_n_400e_coco.yml) |
| *YOLOv6-t | 640 | 32 | 400e | 2.1 | 40.7 | 57.4 | 10.63 | 27.29 |[model](https://paddledet.bj.bcebos.com/models/yolov6_t_400e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_t_400e_coco.yml) |
| *YOLOv6-s | 640 | 32 | 400e | 2.6 | 43.4 | 60.5 | 18.87 | 48.35 |[model](https://paddledet.bj.bcebos.com/models/yolov6_s_400e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_s_400e_coco.yml) |
| *YOLOv6-m | 640 | 32 | 300e | 5.0 | 49.0 | 66.5 | 37.17 | 88.82 |[model](https://paddledet.bj.bcebos.com/models/yolov6_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_m_300e_coco.yml) |
| *YOLOv6-l | 640 | 32 | 300e | 7.9 | 51.0 | 68.9 | 63.54 | 155.89 |[model](https://paddledet.bj.bcebos.com/models/yolov6_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_l_300e_coco.yml) |
| *YOLOv6-l-silu | 640 | 32 | 300e | 9.6 | 51.7 | 69.6 | 58.59 | 142.66 |[model](https://paddledet.bj.bcebos.com/models/yolov6_l_silu_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_l_silu_300e_coco.yml) |
| *YOLOv6-n | 640 | 16 | 300e(+300e) | 2.0 | 37.5 | 53.1 | 5.07 | 12.49 |[model](https://paddledet.bj.bcebos.com/models/yolov6_n_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_n_300e_coco.yml) |
| *YOLOv6-s | 640 | 32 | 300e(+300e) | 2.7 | 44.8 | 61.7 | 20.18 | 49.36 |[model](https://paddledet.bj.bcebos.com/models/yolov6_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_s_300e_coco.yml) |
| *YOLOv6-m | 640 | 32 | 300e(+300e) | - | 49.5 | 66.9 | 37.74 | 92.47 |[model](https://paddledet.bj.bcebos.com/models/yolov6_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_m_300e_coco.yml) |
| *YOLOv6-l(silu) | 640 | 32 | 300e(+300e) | - | 52.2 | 70.2 | 59.66 | 149.4 |[model](https://paddledet.bj.bcebos.com/models/yolov6_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov6/yolov6_l_300e_coco.yml) |
</details>
......@@ -162,13 +161,10 @@
| Model | Input Size | Exported weights(w/o NMS) | ONNX(w/o NMS) |
| :-------- | :--------: | :---------------------: | :----------------: |
| yolov6-n | 416 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_416_400e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_416_400e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_416_400e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_416_400e_coco_wo_nms.onnx) |
| yolov6-n | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_400e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_400e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_400e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_400e_coco_wo_nms.onnx) |
| yolov6-t | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_t_400e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_t_400e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_t_400e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_t_400e_coco_wo_nms.onnx) |
| yolov6-s | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_400e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_400e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_400e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_400e_coco_wo_nms.onnx) |
| yolov6-m | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_m_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_m_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_m_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_m_300e_coco_wo_nms.onnx) |
| yolov6-l | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_300e_coco_wo_nms.onnx) |
| yolov6-l-silu | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_silu_300e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_silu_300e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_silu_300e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_silu_300e_coco_wo_nms.onnx) |
| yolov6-n | 640 | [(w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_300e_coco_w_nms.zip) &#124; [(w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_300e_coco_wo_nms.zip) | [(w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_300e_coco_w_nms.onnx) &#124; [(w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_n_300e_coco_wo_nms.onnx) |
| yolov6-s | 640 | [(w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_300e_coco_w_nms.zip) &#124; [(w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_300e_coco_wo_nms.zip) | [(w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_300e_coco_w_nms.onnx) &#124; [(w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_s_300e_coco_wo_nms.onnx) |
| yolov6-m | 640 | [(w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_m_300e_coco_w_nms.zip) &#124; [(w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_m_300e_coco_wo_nms.zip) | [(w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_m_300e_coco_w_nms.onnx) &#124; [(w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_m_300e_coco_wo_nms.onnx) |
| yolov6-l(silu) | 640 | [(w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_300e_coco_w_nms.zip) &#124; [(w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_300e_coco_wo_nms.zip) | [(w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_300e_coco_w_nms.onnx) &#124; [(w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov6/yolov6_l_300e_coco_wo_nms.onnx) |
</details>
......@@ -209,6 +205,36 @@
</details>
### [YOLOv8](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8)
<details>
<summary> Baseline </summary>
| Model | Input Size | images/GPU | Epoch | TRT-FP16-Latency(ms) | mAP<sup>val<br>0.5:0.95 | mAP<sup>val<br>0.5 | Params(M) | FLOPs(G) | download | config |
| :------------- | :------- | :-------: | :------: | :------------: | :---------------------: | :----------------: |:---------: | :------: |:---------------: |:-----: |
| *YOLOv8-n | 640 | 16 | 500e | 2.4 | 37.3 | 53.0 | 3.16 | 8.7 | [model](https://paddledet.bj.bcebos.com/models/yolov8_n_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8/yolov8_n_300e_coco.yml) |
| *YOLOv8-s | 640 | 16 | 500e | 3.4 | 44.9 | 61.8 | 11.17 | 28.6 | [model](https://paddledet.bj.bcebos.com/models/yolov8_s_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8/yolov8_s_300e_coco.yml) |
| *YOLOv8-m | 640 | 16 | 500e | 6.5 | 50.2 | 67.3 | 25.90 | 78.9 | [model](https://paddledet.bj.bcebos.com/models/yolov8_m_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8/yolov8_m_300e_coco.yml) |
| *YOLOv8-l | 640 | 16 | 500e | 10.0 | 52.8 | 69.6 | 43.69 | 165.2 | [model](https://paddledet.bj.bcebos.com/models/yolov8_l_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8/yolov8_l_300e_coco.yml) |
| *YOLOv8-x | 640 | 16 | 500e | 15.1 | 53.8 | 70.6 | 68.23 | 257.8 | [model](https://paddledet.bj.bcebos.com/models/yolov8_x_300e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8/yolov8_x_300e_coco.yml) |
| *YOLOv8-P6-x | 1280 | 16 | 500e | 55.0 | - | - | 97.42 | 522.93 | [model](https://paddledet.bj.bcebos.com/models/yolov8p6_x_500e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8/yolov8p6_x_500e_coco.yml) |
</details>
<details>
<summary> Deploy Models </summary>
| Model | Input Size | Exported weights(w/o NMS) | ONNX(w/o NMS) |
| :-------- | :--------: | :---------------------: | :----------------: |
| YOLOv8-n | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_n_500e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_n_500e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_n_500e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_n_500e_coco_wo_nms.onnx) |
| YOLOv8-s | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_s_500e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_s_500e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_s_500e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_s_500e_coco_wo_nms.onnx) |
| YOLOv8-m | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_m_500e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_m_500e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_m_500e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_m_500e_coco_wo_nms.onnx) |
| YOLOv8-l | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_l_500e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_l_500e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_l_500e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_l_500e_coco_wo_nms.onnx) |
| YOLOv8-x | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_x_500e_coco_w_nms.zip) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_x_500e_coco_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_x_500e_coco_w_nms.onnx) &#124; [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/yoloseries/yolov8/yolov8_x_500e_coco_wo_nms.onnx) |
</details>
### [RTMDet](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/rtmdet)
<details>
......@@ -254,7 +280,6 @@
| Model | Input Size | images/GPU | Epoch | TRT-FP16-Latency(ms) | mAP(0.50,11point) | Params(M) | FLOPs(G) | download | config |
| :-----------: | :-------: | :-------: | :------: | :------------: | :---------------: | :------------------: |:-----------------: | :------: | :------: |
| YOLOv5-s | 640 | 16 | 60e | 3.2 | 80.3 | 7.24 | 16.54 | [model](https://paddledet.bj.bcebos.com/models/yolov5_s_60e_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/voc/yolov5_s_60e_voc.yml) |
| YOLOv6-s | 640 | 32 | 40e | 2.7 | 84.7 | 18.87 | 48.35 | [model](https://paddledet.bj.bcebos.com/models/yolov6_s_40e_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/voc/yolov6_s_40e_voc.yml) |
| YOLOv7-tiny | 640 | 32 | 60e | 2.6 | 80.2 | 6.23 | 6.90 | [model](https://paddledet.bj.bcebos.com/models/yolov7_tiny_60e_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/voc/yolov7_tiny_60e_voc.yml) |
| YOLOX-s | 640 | 8 | 40e | 3.0 | 82.9 | 9.0 | 26.8 | [model](https://paddledet.bj.bcebos.com/models/yolox_s_40e_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/voc/yolox_s_40e_voc.yml) |
| PP-YOLOE+_s | 640 | 8 | 30e | 2.9 | 86.7 | 7.93 | 17.36 | [model](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_30e_voc.pdparams) | [config](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/voc/ppyoloe_plus_crn_s_30e_voc.yml) |
......
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