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2024-08-27 14:42:45 +08:00
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# PP-YOLOE
## 模型库
### Objects365数据集模型库
| 模型 | Epoch | 机器个数 | GPU个数 | 每GPU图片个数 | 骨干网络 | 输入尺寸 | Box AP<sup>0.5 | Params(M) | FLOPs(G) | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件 |
|:---------------:|:-----:|:-----------:|:-----------:|:-----------:|:---------:|:----------:|:--------------:|:---------:|:---------:|:-------------:|:-----------------------:| :--------:|:--------:|
| PP-YOLOE+_s | 60 | 3 | 8 | 8 | cspresnet-s | 640 | 18.1 | 7.93 | 17.36 | 208.3 | 333.3 | [model](https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_s_obj365_pretrained.pdparams) | [config](./ppyoloe_plus_crn_s_60e_objects365.yml) |
| PP-YOLOE+_m | 60 | 4 | 8 | 8 | cspresnet-m | 640 | 25.0 | 23.43 | 49.91 | 123.4 | 208.3 | [model](https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_m_obj365_pretrained.pdparams) | [config](./ppyoloe_plus_crn_m_60e_objects365.yml) |
| PP-YOLOE+_l | 60 | 3 | 8 | 8 | cspresnet-l | 640 | 30.8 | 52.20 | 110.07 | 78.1 | 149.2 | [model](https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_l_obj365_pretrained.pdparams) | [config](./ppyoloe_plus_crn_l_60e_objects365.yml) |
| PP-YOLOE+_x | 60 | 4 | 8 | 8 | cspresnet-x | 640 | 32.7 | 98.42 | 206.59 | 45.0 | 95.2 | [model](https://bj.bcebos.com/v1/paddledet/models/pretrained/ppyoloe_crn_x_obj365_pretrained.pdparams) | [config](./ppyoloe_plus_crn_x_60e_objects365.yml) |
**注意:**
- 多机训练细节见[文档](../../../docs/tutorials/DistributedTraining_cn.md)
- Objects365数据集下载请参考[objects365官网](http://www.objects365.org/overview.html)。具体种类列表可下载由PaddleDetection团队整理的[objects365_detection_label_list.txt](https://bj.bcebos.com/v1/paddledet/data/objects365/objects365_detection_label_list.txt)并存放在`dataset/objects365/`，每一行即表示第几个种类。inference或导出模型时需要读取到种类数，如果没有标注json文件时，可以进行如下更改`configs/datasets/objects365_detection.yml`：
```
TestDataset:
!ImageFolder
# anno_path: annotations/zhiyuan_objv2_val.json
anno_path: objects365_detection_label_list.txt
dataset_dir: dataset/objects365/
```