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⚡️FastDeploy
⚡️FastDeploy是一款简单易用的推理部署工具箱。覆盖业界主流优质预训练模型并提供开箱即用的开发体验，包括图像分类、目标检测、图像分割、人脸检测、人体关键点识别、文字识别等多任务，满足开发者多场景，多硬件、多平台的快速部署需求。
支持模型
| 任务场景 | 模型 | X64 CPU | Nvidia-GPU | Nvidia-GPU TensorRT |
|---|---|---|---|---|
| 图像分类 | PaddleClas/ResNet50 | √ | √ | √ |
| PaddleClas/PPLCNet | √ | √ | √ | |
| PaddleClas/EfficientNet | √ | √ | √ | |
| PaddleClas/GhostNet | √ | √ | √ | |
| PaddleClas/MobileNetV1 | √ | √ | √ | |
| PaddleClas/MobileNetV2 | √ | √ | √ | |
| PaddleClas/ShuffleNetV2 | √ | √ | √ | |
| 目标检测 | PaddleDetection/PPYOLOE | √ | √ | √ |
| PaddleDetection/PicoDet | √ | √ | √ | |
| PaddleDetection/YOLOX | √ | √ | √ | |
| PaddleDetection/YOLOv3 | √ | √ | √ | |
| PaddleDetection/PPYOLO | √ | √ | - | |
| PaddleDetection/PPYOLOv2 | √ | √ | - | |
| PaddleDetection/FasterRCNN | √ | √ | - |
快速开始
安装FastDeploy Python
用户根据开发环境选择安装版本，更多安装环境参考安装文档.
pip install https://bj.bcebos.com/paddlehub/fastdeploy/wheels/fastdeploy_python-0.2.0-cp38-cp38-manylinux1_x86_64.whl
准备目标检测模型和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
tar xvf ppyoloe_crn_l_300e_coco.tgz
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
加载模型预测
import fastdeploy.vision as vis
import cv2
model = vis.detection.PPYOLOE("ppyoloe_crn_l_300e_coco/model.pdmodel",
"ppyoloe_crn_l_300e_coco/model.pdiparams",
"ppyoloe_crn_l_300e_coco/infer_cfg.yml")
im = cv2.imread("000000014439.jpg")
result = model.predict(im.copy())
print(result)
vis_im = fd.vision.vis_detection(im, result, score_threshold=0.5)
cv2.imwrite("vis_image.jpg", vis_im)
预测完成，可视化结果保存至vis_image.jpg，同时输出检测结果如下
DetectionResult: [xmin, ymin, xmax, ymax, score, label_id]
415.047363,89.311523, 506.009613, 283.863129, 0.950423, 0
163.665710,81.914894, 198.585342, 166.760880, 0.896433, 0
581.788635,113.027596, 612.623474, 198.521713, 0.842597, 0
267.217224,89.777321, 298.796051, 169.361496, 0.837951, 0
104.465599,45.482410, 127.688835, 93.533875, 0.773348, 0
...
更多部署示例
FastDeploy提供了大量部署示例供开发者参考，支持模型在CPU、GPU以及TensorRT的部署
Acknowledge
本项目中SDK生成和下载使用了EasyEdge中的免费开放能力，再次表示感谢。
License
FastDeploy遵循Apache-2.0开源协议。