In the contemporary landscape, industrial enterprises encounter many intricate image classification challenges that significantly influence their operations. These challenges encompass a broad spectrum of concerns, such as defect detection, quality control, component recognition, anomaly detection, efficient inventory management, verifying the accuracy of barcodes, labels, packaging, and many more.

These multifaceted image classification challenges directly impact operational efficiency, product quality, and safety within industrial enterprises. 

Recognizing the significance of these issues, our client approached us with the visionary idea of developing a comprehensive image classification solution. The aim is to harness the power of advanced technology to effectively address these challenges, thereby enhancing productivity, ensuring safety, and optimizing their industrial processes.

Image Classification Features

1. Real-time edge classification with NCS2

Instead of relying on cloud servers or powerful local machines for image processing, the app leverages the power of the NCS2 for on-the-spot, real-time image classification. This ensures speedy results with minimal latency, making it ideal for situations that demand immediate feedback.

Our team integrated the Intel NCS2 with the core application, optimizing the machine-learning models with the OpenVINO toolkit. This ensures the models run efficiently on the NCS2, providing users instantaneous classifications. The backend seamlessly detects the presence of the NCS2 and reroutes image processing tasks directly to it.

2. Model optimization

Our specialists used the OpenVINO toolkit to optimize and adapt the machine learning models specifically for the NCS2. Multiple iterations ensured the models remained accurate and efficient.

3. Adaptive learning with user feedback

The application is not just static; it learns and adapts. If a user disagrees with a classification, they can provide feedback, and over time, this feedback is used to refine and improve the classification model.

Our team created an intuitive feedback mechanism in the UI, allowing users to flag incorrect classifications quickly.