Build a Fast Mask‑Detection Model with Baidu EasyDL – No Coding Required

What Is Baidu EasyDL?

Baidu EasyDL is a zero‑threshold AI development platform built on the PaddlePaddle open‑source deep‑learning framework, offering ready‑to‑use models for image, text, speech, and tabular data, along with one‑click data labeling, training, and deployment.

Step 1: Requirement Analysis

The goal is to detect if a person is wearing a mask, using either images or video. EasyDL provides both image‑classification and object‑detection models for this purpose.

Step 2: Create the Model

Choose the image‑classification type and set up a new model named “Mask Detection”.

Step 3: Collect Training Data

Gather roughly 50 images of masked faces and 50 of unmasked faces, plus a few extra for validation.

Step 4: Build the Dataset and Annotate

Upload the images to the dataset and label them as “mask” or “no mask”. EasyData can be used to clean low‑quality images and accelerate labeling.

Step 5: Train the Model

Select a training algorithm, start training, and monitor the accuracy. If the result is unsatisfactory, add more samples or refine the data.

Step 6: Optimize and Iterate

Use EasyDL’s data‑cleaning tools to improve dataset quality, retrain, and optionally enable cloud‑service data feedback for continuous improvement.

Step 7: Publish the API

After training, publish the model as an API with a single click, making it accessible to backend services or edge devices.

Beyond this specific use case, EasyDL has been applied to high‑accuracy waste‑sorting models, AI‑powered pole‑inspection systems, and more, allowing developers without deep AI expertise to quickly prototype and monetize AI solutions.