paddlepaddle/paddle-inference-demo

Paddle Inference Demo is a collection of example projects showing how to take a trained AI model and put it to work in a real application. When you build an AI model, it starts as a training project. To actually use that model in a product, you need a deployment engine that can take in real data and quickly return predictions. This repo provides ready-to-run examples for that deployment step. The examples are organized into two main folders: one for Python and one for C++. Both folders cover the same kinds of tasks. You will find sample code for image classification (sorting images into categories), image segmentation (identifying which pixels in an image belong to which object), and object detection (finding and labeling items within a picture). There are also examples for natural language processing models like ERNIE and BERT, which handle text understanding. Additional examples show how to use multi-threading to process multiple requests at once and how to leverage specialized acceleration for faster results. This project is designed for teams already working with PaddlePaddle, an open-source deep learning framework, who need to move their models from experimentation into production. For example, if your company has trained a model to detect defects on a manufacturing line, you could use these examples as a starting point to build the actual service that runs the model on server hardware. The C++ examples would appeal to teams building high-performance applications, while the Python examples suit those who want to prototype quickly. The README is fairly brief and assumes you already have some background knowledge of the underlying inference engine. It points you to external documentation if you are completely new to the system, but it does not provide a beginner-friendly walkthrough within the repo itself. If you are using an older version of the framework, the README notes that you should look at a specific older branch of the project for compatible examples.