Industrial vision detection scenarios often involve high algorithmic complexity, typically requiring multiple algorithms and models. Traditionally, this necessitates external code or software to establish logical connections between AI models, manually transferring image sets. A process that is complex, laborious and a challenge to holistic management. AIDI simplifies this process with its graphical algorithm workflow orchestration tool: Algorithm Canvas. This intuitive tool allows users to design the entire algorithm process, from image input to result output, in a visual workflow, significantly reducing the complexity of algorithm development.

The "training recommendation set" feature can recommend suitable datasets to users based on factors such as previous success cases, dataset coverage, diversity, or the complexity of specific problems, according to the specific application scenario or needs. Users no longer need to manually analyze data features one by one; with a simple click, the system will intelligently select and recommend training images.

Incremental training is supported, allowing the model to learn only what is added as new data arrives, rather than having to train from scratch each time. Only learning what is added helps the model adapt quickly to new situations and significantly reduces model iteration time.

Multi-dimensional training sample management function is provided to ensure the balanced distribution of defective training samples in all dimensions. Equipped with a variety of defect label screening methods, users can orderly manage multiple categories of defect labels to adjust the sample set and improve the training effect.

In industrial vision detection scenarios, algorithm solutions often involve multiple models. Traditionally, secondary development requires coding to establish logical relationships between models. AIDI simplifies this by directly exporting Visual Studio projects, eliminating the need to write code or configure dependency libraries. This enables direct use for vision detection algorithm integration tasks, making it accessible for junior engineers, significantly lowering technical barriers, reducing integration time, and improving model development efficiency.

Provide environmental monitoring tools for real-time monitoring of system environmental status, which helps to promptly identify and resolve problems that may affect the normal operation of programs.