"PaddlePaddle from Beginner to Alchemist" - Part 14: Deploying Prediction Models on Servers
This article introduces the process of building an image recognition interface using Flask. First, a simple Flask program is used to set up the root path and file upload functionality; subsequently, the image prediction API is implemented, which loads the model and performs inference. After uploading an image, users can directly obtain the classification result and confidence. The entire process includes steps such as environment preparation, code writing, and deployment, making it suitable for beginners to learn the development method of image processing services. Key points: 1. **Flask Setup**: Create the root path and file upload functionality. 2. **Model Loading**: Load the model from PaddlePaddle
Read MoreMy Learning Journey with PaddlePaddle - Note 13: Deploying PaddlePaddle to a Website Server
This tutorial provides a detailed introduction to using PaddlePaddle for basic image classification tasks and deploying the resulting model to a web service. Below is a summary of the tutorial content and some improvement suggestions: ### Summary 1. **Environment Preparation**: - Install necessary libraries such as PaddlePaddle and Flask. - Set up the development environment. 2. **Data Preprocessing**: - Read and preprocess images, including converting to grayscale and resizing. 3. **Model Construction and Training**
Read More