The AI Trainer Competition has become a landmark event in the field of intelligent technology education.
This year’s Hebei Provincial Industrial Skills Competition – AI Trainer Competition focuses on cultivating practical abilities in artificial intelligence, from data collection and labeling to model training, validation, and deployment.

It aims to bridge the gap between theoretical knowledge and real-world applications, while promoting innovation in AI education and talent development.
More than just a skills contest, it serves as a training ground for future AI professionals who will shape the intelligent era.

Professional and Ethical Foundations

A strong foundation in professional ethics and AI fundamentals is the cornerstone of every AI trainer.
The competition’s first stage evaluates participants on their understanding of responsible AI, data privacy, and legal compliance in artificial intelligence applications.

Competitors are expected to:

• Understand national laws and regulations related to AI and data use
• Follow ethical guidelines for AI model development
• Recognize the importance of fairness, transparency, and accountability in algorithms

By emphasizing ethical AI practice, the AI Trainer Competition reinforces the idea that every AI professional should combine technical ability with moral responsibility, ensuring technology serves humanity responsibly.

Data Collection and Annotation

In the AI Trainer Competition, one of the core abilities tested is data collection and annotation. Participants are required to collect and prepare datasets for existing object models such as fruits, cups, and other common items. These real-world objects are captured using industrial cameras or vision modules integrated within the AI Experiment Box, ensuring the data is both diverse and high-quality.

The collected image data is then annotated and categorized to train computer vision models. Competitors learn to label object boundaries, assign class names, and convert the datasets into training formats recognized by deep learning frameworks. This process not only strengthens their understanding of AI data workflows, but also allows them to experience how well-prepared data directly affects model performance and accuracy.

By working with tangible objects like fruits and cups, participants bridge the gap between AI algorithms and real-world recognition tasks, demonstrating how artificial intelligence can perceive, classify, and interact with its environment — an essential foundation for future applications in smart manufacturing, robotics, and automation.

Model Training and Conversion

The AI Trainer Competition places strong emphasis on AI model training—a key ability that separates theory from true technical practice.

Contestants use machine learning and deep learning frameworks such- as TensorFlow, PyTorch, or MindSpore to train models, tune hyperparameters, and evaluate accuracy.
They must also demonstrate model conversion, adapting trained models for deployment on various hardware platforms like edge devices, embedded systems, or robotic controllers.

This task highlights the growing importance of cross-platform AI deployment, ensuring that models are lightweight, efficient, and ready for real-world use.
Participants who excel in this area showcase an essential skill set for the next generation of AI engineers.

Visual Detection and Model Integration

Another major component of the AI Trainer Competition is visual detection and AI model integration.
Here, participants apply their trained models to perform object recognition, image classification, and real-time visual inspection.

They must deploy AI models into a visual system capable of detecting targets through camera input, and even trigger hardware responses such as switching devices on/off, activating fans, or measuring environmental data.

This module evaluates the participant’s ability to:

• Deploy AI models in a real-time environment
• Integrate AI with industrial equipment
• Solve practical problems through intelligent vision

The challenge represents the real-world workflow of AI-driven visual inspection systems, bridging the fields of computer vision and industrial automation.

Robotics Programming and Target Grasping

One of the most distinctive parts of this year’s AI Trainer Competition is the integration of robotics programming.
Contestants must connect their trained vision models to robotic arms capable of object detection, trajectory planning, and target grasping.

By linking AI perception with mechanical execution, participants simulate intelligent manufacturing environments—where machines not only “see” but also “act.”

This section tests:

• Robotic programming and motion planning skills
• The ability to fuse vision-based AI with robot control
• Understanding of closed-loop AI systems

Such tasks reflect real industrial scenarios—from smart assembly lines to automated sorting—where AI trainers play a crucial role in developing and maintaining AI-powered robotic systems.

AI Applications and Industry Trends

Beyond technical execution, the AI Trainer Competition also assesses participants’ understanding of AI applications and emerging industry trends.

Contestants analyze how artificial intelligence transforms multiple industries, including:

• Smart Manufacturing: predictive maintenance and defect detection
• Smart Cities: traffic management and public safety optimization
• Education: adaptive learning platforms and AI teaching assistants
• Healthcare: medical image analysis and disease prediction

This section encourages competitors to think beyond code and data—to envision how AI reshapes industries, careers, and human life.
It fosters a forward-looking mindset that blends innovation, cross-disciplinary collaboration, and sustainable development.

Safety and Standardized Operations

In any AI-related competition or industry application, safety and standardized operation are essential.
Participants are required to follow strict rules for laboratory safety, equipment handling, and data protection.

The AI Trainer Competition enforces:

• Safe use of robotic and electrical equipment
• Compliance with cybersecurity and data privacy standards
• Adherence to clean, organized, and responsible working behavior

By including this component, the competition ensures that AI trainers not only master technical competence but also embody professional discipline and workplace responsibility—qualities that are highly valued in the global AI workforce.

The AI Trainer Competition used the AI Experiment Box as the main competition platform, providing participants with hands-on experience in AI data collection, model training, and robot operation. This integration of AI technology with real-world industrial applications not only enhances participants’ technical capabilities but also strengthens the connection between AI education and intelligent manufacturing. By applying theoretical knowledge in practical tasks, the competition successfully cultivates a new generation of AI talents who can transform innovation into productivity.