mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-10 02:50:19 +08:00
51 lines
3.0 KiB
Markdown
51 lines
3.0 KiB
Markdown
# PaddleDetection模型部署
|
||
|
||
## 模型版本说明
|
||
|
||
- [PaddleDetection Release/2.4](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4)
|
||
|
||
## 支持模型列表
|
||
|
||
目前FastDeploy支持如下模型的部署
|
||
|
||
- [PP-YOLOE(含PP-YOLOE+)系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe)
|
||
- [PicoDet系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet)
|
||
- [PP-YOLO系列模型(含v2)](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo)
|
||
- [YOLOv3系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3)
|
||
- [YOLOX系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolox)
|
||
- [FasterRCNN系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/faster_rcnn)
|
||
- [MaskRCNN系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/mask_rcnn)
|
||
|
||
## 导出部署模型
|
||
|
||
在部署前，需要先将PaddleDetection导出成部署模型，导出步骤参考文档[导出模型](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/deploy/EXPORT_MODEL.md)
|
||
|
||
**注意**
|
||
- 在导出模型时不要进行NMS的去除操作，正常导出即可
|
||
- 导出模型时，不要添加`fuse_normalize=True`参数
|
||
|
||
## 下载预训练模型
|
||
|
||
为了方便开发者的测试，下面提供了PaddleDetection导出的各系列模型，开发者可直接下载使用。
|
||
|
||
其中精度指标来源于PaddleDetection中对各模型的介绍，详情各参考PaddleDetection中的说明。
|
||
|
||
|
||
| 模型 | 参数大小 | 精度 | 备注 |
|
||
|:---------------------------------------------------------------- |:----- |:----- | :------ |
|
||
| [picodet_l_320_coco_lcnet](https://bj.bcebos.com/paddlehub/fastdeploy/picodet_l_320_coco_lcnet.tgz) |23MB | Box AP 42.6% |
|
||
| [ppyoloe_crn_l_300e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz) |200MB | Box AP 51.4% |
|
||
| [ppyolo_r50vd_dcn_1x_coco](https://bj.bcebos.com/paddlehub/fastdeploy/ppyolo_r50vd_dcn_1x_coco.tgz) | 180MB | Box AP 44.8% | 暂不支持TensorRT |
|
||
| [ppyolov2_r101vd_dcn_365e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/ppyolov2_r101vd_dcn_365e_coco.tgz) | 282MB | Box AP 49.7% | 暂不支持TensorRT |
|
||
| [yolov3_darknet53_270e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/yolov3_darknet53_270e_coco.tgz) |237MB | Box AP 39.1% | |
|
||
| [yolox_s_300e_coco](https://bj.bcebos.com/paddlehub/fastdeploy/yolox_s_300e_coco.tgz) | 35MB | Box AP 40.4% | |
|
||
| [faster_rcnn_r50_vd_fpn_2x_coco](https://bj.bcebos.com/paddlehub/fastdeploy/faster_rcnn_r50_vd_fpn_2x_coco.tgz) | 160MB | Box AP 40.8%| 暂不支持TensorRT |
|
||
| [mask_rcnn_r50_1x_coco](https://bj.bcebos.com/paddlehub/fastdeploy/mask_rcnn_r50_1x_coco.tgz) | 128M | Box AP 37.4%, Mask AP 32.8%| 暂不支持TensorRT、ORT |
|
||
|
||
|
||
## 详细部署文档
|
||
|
||
- [Python部署](python)
|
||
- [C++部署](cpp)
|
||
- [服务化部署](serving)
|