Simple and easy to use, with no-code operation
Achieve data annotation, model training, and deployment with zero coding. Built-in adaptive algorithms eliminate the need for parameter tuning, making it easy for even beginners to get started.
InsWorksESAI
Deep Learning Platform
InsWorks ESAI is an AI vision platform designed for industrial intelligent inspection. It integrates a wide range of task modules—including classification, object detection, instance segmentation, and cascading—and provides comprehensive AI development capabilities spanning data management, model training, and model optimization. Through visual training workflows, flexible model management, and efficient inference deployment, it helps users quickly build stable, high-precision industrial AI inspection applications.
InsWorks ESAISoftware Features
Supports multiple models and cascading
Supports eight major computer vision tasks, including image classification, object detection, semantic segmentation, and instance segmentation, and enables model cascading to address diverse application needs.
Comprehensive data management and analysis capabilities
Provides sample filtering, pass/fail navigation analysis, and confusion matrices based on inference results.
Supports filtering and sorting by annotation level for training sets, test sets, confusion datasets, and custom sets.
Efficient Cross-Platform Deployment
Compatible with multiple languages, including C++ and C#, as well as CPU and GPU platforms, the optimized inference engine delivers millisecond-level response times to meet large-scale real-time processing demands.
End-to-End Tool Support
From data import and model training to integration and deployment, we complete the full development cycle for industrial AI vision applications.Import image
Supports importing images in formats such as PNG, JPG, JPEG, BMP, and TIF.
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Key Capabilities for Improving Development Efficiency
Intelligent Annotation
The software supports intelligent annotation of positive and negative points, intelligent annotation via selection boxes, and detection-to-annotation conversion. Combined with rapid manual verification, it reduces repetitive tasks, improves annotation efficiency, and lowers training costs.
Defect Generation
Automatically generates diverse defect samples to mitigate the shortage of defect data, enrich training data, and improve model training performance and generalization ability.
Skip Navigation
Quickly identify samples that have been over-detected or missed, as well as anomalous data, to assist with model analysis and continuous optimization, thereby improving detection stability.
Cross-platform deployment
WindowsNVIDIA Orin
Supports deployment across multiple platforms, including Windows and NVIDIA, enabling rapid model migration and flexible adaptation to edge computing and industrial field applications.
Deployment is supported in C++ and C#, and the solution can be integrated into mainstream vision processing platforms such as VDE.
Ultra-Fast Inference
- No-code AI model deployment supports pixel-level defect detection and enables rapid implementation of industrial vision applications.
- Deeply optimizes AI inference performance, balancing detection accuracy and inference speed.
APPLICATION CASES
ESAI Application Cases
Click on “Case Studies” to view the complete solution details and page-by-page presentation from the project materials.
Photovoltaics
Solar Panel Defect Detection
Detects various types of solar panel defects, including scratches, damage, foreign objects, dirt, chipped edges, and hidden cracks.
Integrated Circuits
PCB Defect Detection
Uses deep learning models to identify complex PCB manufacturing defects and improve inspection reliability.
Lithium-ion Batteries
Battery Pack Appearance Inspection
Builds an AI inspection model for battery pack appearance defects, covering a wide range of anomaly types.
Automotive Manufacturing
Visual Inspection of Automotive Parts
Using good-part learning to reduce reliance on defect samples, this approach detects surface defects in metal parts and automotive components.
Food & Pharmaceuticals
Tablet Omission Detection
Detects missing tablets and assembly anomalies in blister packs to ensure packaging integrity.
Logistics
Logistics Carrier Inspection
Identifies the status of logistics containers and pallets, providing AI capabilities for sorting, handling, and operational monitoring.