# 模型库和基线

## 测试环境

- Python 3.7
- PaddlePaddle 每日版本
- CUDA 10.1
- cuDNN 7.5
- NCCL 2.4.8

## 通用设置

- 所有模型均在COCO17数据集中训练和测试。
- 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。

## 训练策略

- 我们采用和[Detectron](https://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md#training-schedules)相同的训练策略。
- 1x 策略表示：在总batch size为8时，初始学习率为0.01，在8 epoch和11 epoch后学习率分别下降10倍，最终训练12 epoch。
- 2x 策略为1x策略的两倍，同时学习率调整位置也为1x的两倍。

## ImageNet预训练模型

Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型均通过标准的Imagenet-1k数据集训练得到，ResNet和MobileNet等是采用余弦学习率调整策略或SSLD知识蒸馏训练得到的高精度预训练模型，可在[PaddleClas](https://github.com/PaddlePaddle/PaddleClas)查看模型细节。


## 基线

### Faster R-CNN

请参考[Faster R-CNN](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/faster_rcnn/)

### Mask R-CNN

请参考[Mask R-CNN](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/mask_rcnn/)

### Cascade R-CNN

请参考[Cascade R-CNN](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/cascade_rcnn)

### YOLOv3

请参考[YOLOv3](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/yolov3/)

### SSD

请参考[SSD](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/ssd/)

### FCOS

请参考[FCOS](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/fcos/)

### SOLOv2

请参考[SOLOv2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/solov2/)

### PP-YOLO

请参考[PP-YOLO](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/ppyolo/)

### TTFNet

请参考[TTFNet](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/ttfnet/)

### Group Normalization

请参考[Group Normalization](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/gn/)

### Deformable ConvNets v2

请参考[Deformable ConvNets v2](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/dcn/)

### HRNets

请参考[HRNets](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/hrnet/)

### Res2Net

请参考[Res2Net](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/res2net/)

### GFL

请参考[GFL](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/gfl)

### PicoDet

请参考[PicoDet](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/picodet)

### PP-YOLOE

请参考[PP-YOLOE](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/ppyoloe)

### YOLOX

请参考[YOLOX](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/yolox)

### YOLOv5

请参考[YOLOv5](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov5)

### YOLOv6

请参考[YOLOv6](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov6)

### YOLOv7

请参考[YOLOv7](https://github.com/PaddlePaddle/PaddleYOLO/tree/develop/configs/yolov7)


## 旋转框检测

### S2ANet

请参考[S2ANet](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/dota/)


## 关键点检测

### PP-TinyPose

请参考[PP-TinyPose](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/keypoint/tiny_pose)

## HRNet

请参考[HRNet](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/keypoint/hrnet)

## HigherHRNet

请参考[HigherHRNet](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/keypoint/higherhrnet)


## 多目标跟踪

### DeepSORT

请参考[DeepSORT](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/mot/deepsort)

### JDE

请参考[JDE](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/mot/jde)

### FairMOT

请参考[FairMOT](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/mot/fairmot)

### ByteTrack

请参考[ByteTrack](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/mot/bytetrack)