Hyperspec: Streamlined Data Management and Performance-Driven Models

Hyperspec: Streamlined Data Management and Performance-Driven Models

Hyperspec’s offerings to customers encompasses various key features to help businesses accelerate adoption of autonomous systems.

Data Management and Model Performance Improvement

We provide a comprehensive data management system that enables efficient handling of data. This includes conducting annotations, performing training, and ensuring the verification and validation of model performance. Once the model has shown improvement, we offer a streamlined process to deploy it back onto a robotic fleet. In essence, our solution combines the best of Scale.ai and Databricks technology.

Bias-Free Data Balancing and Training Optimization

A crucial aspect of our approach involves balancing the data to eliminate biases. We strive to deliver optimized training processes that maximize performance. Our integrated trip management view provides a comprehensive overview of the collected data, including detailed error analysis for each dataset. Furthermore, we offer processes that enhance the annotation workflows, emphasizing a self-serve model where customers can leverage their own annotation teams while utilizing our tools and visualizations to expedite the annotation workflow.

Automated Labeling for Object Detection and Scene Segmentation

Additionally, we provide automated labeling capabilities, specifically for object detection and scene segmentation. This allows for automatic labeling of 2D bounding boxes, cuboids, spines, and pixel segmentations. We specialize in creating models that excel in depth perception, scene segmentation, vectorization, and object classification.

Seamless Integration and Model Versioning

These components can be seamlessly integrated into a customer’s existing computing infrastructure. Furthermore, we offer tools for verification and validation, empowering customers to decide whether to deploy newer versions of the model and compare their performance with previous iterations.

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