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利用paddlehub中预训练模型xception71_imagenet(https://www.paddlepaddle.org.cn/hubdetail?name=xception71_imagenet&en_category=ImageClassification)进行图像分类。
Xception 全称为 Extreme Inception,是 Google 于 2016年提出的 Inception V3 的改进模型。Xception 采用了深度可分离卷积(depthwise separable convolution) 来替换原来 Inception V3 中的卷积操作,整体的网络结构是带有残差连接的深度可分离卷积层的线性堆叠。该PaddleHub Module结构为Xception71,基于ImageNet-2012数据集训练,接受输入图片大小为224 x 224 x 3,支持直接通过命令行或者 Python 接口进行预测。
本模型基于 ServiceBoot微服务引擎 进行服务化封装,参见: 《CubeAI模型开发指南》
$ sh pip-install-reqs.sh
$ serviceboot start
或
$ python3 run_model_server.py
一键式本地容器化部署和运行,参见: 《CubeAI模型独立部署指南》 或 CubeAI Docker Builder
本模型服务可一键发布至 CubeAI智立方平台 进行共享和部署,参见: 《CubeAI模型发布指南》
本模型提供了1个API接口:
API接口1:
API端点: /api/dataa
HTTP方法: POST
HTTP请求体:
{
"action": "predict",
"args": {
"img": <压缩图像的base64编码字符串(或其Data URL表示)>
}
}
HTTP响应体:
{
"status": "ok"|"err",
"value": <物体名称>
}
Paddle图像分类
Text TypeScript Python HTML Shell other
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