The synthetic data solution is designed to address the frequent issue of insufficient data for AI model training in digital evolution. It offers voluminous synthetic 3D data, even when the initial dataset is small. This system ensures diverse data, covering different types of defects and circumstances, providing the AI model with superior generalization and sharp recognition abilities. It enhances detection accuracy and potency, besides saving time and costs because it is independent of extensive real data collection.
- Addressing Data Scarcity: Even with limited data, synthetic data technology generates a large volume of 3D virtual realistic data, expanding the dataset and overcoming the common challenge of insufficient training data for AI models in digital transformation.
- Providing Diverse Data: Synthetic 3D data covers various types of defects and scenarios, offering a diverse dataset that enhances the generalization and distinct recognition capabilities of AI models.
- Improved Accuracy and Efficiency: By leveraging synthetic data technology, we can generate highly realistic virtual data based on the logic derived from real defects. This enables AI models to accurately identify defects and enhance detection performance.
- Time and Cost Savings: Eliminating the need to collect a large amount of real data, synthetic data technology rapidly generates a substantial amount of data, significantly saving time and resources required for model training and validation.
- Extensive Application : The system can generate a vast number of scenarios, making it applicable in a wide array of contexts.
The synthetic data solution is designed to address the frequent issue of insufficient data for AI model training in digital evolution. It offers voluminous synthetic 3D data, even when the initial dataset is small. This system ensures diverse data, covering different types of defects and circumstances, providing the AI model with superior generalization and sharp recognition abilities. It enhances detection accuracy and potency, besides saving time and costs because it is independent of extensive real data collection.
- Addressing Data Scarcity: Even with limited data, synthetic data technology generates a large volume of 3D virtual realistic data, expanding the dataset and overcoming the common challenge of insufficient training data for AI models in digital transformation.
- Providing Diverse Data: Synthetic 3D data covers various types of defects and scenarios, offering a diverse dataset that enhances the generalization and distinct recognition capabilities of AI models.
- Improved Accuracy and Efficiency: By leveraging synthetic data technology, we can generate highly realistic virtual data based on the logic derived from real defects. This enables AI models to accurately identify defects and enhance detection performance.
- Time and Cost Savings: Eliminating the need to collect a large amount of real data, synthetic data technology rapidly generates a substantial amount of data, significantly saving time and resources required for model training and validation.
- Extensive Application : The system can generate a vast number of scenarios, making it applicable in a wide array of contexts.
- Data Synthesizer:
Generating countless data to train our defect detection model.
- Digital Twins:
3D simulated testing reduces the development cost of optimization processes.
