Seymour
Marcel's Seymour is a system for transforming raw image data into structured, labeled datasets through automated segmentation and human-in-the-loop learning. It is designed for settings where labeled data is limited and domain expertise is required, enabling technical users to interact with algorithmically generated segments rather than manually constructed annotations. By combining geometric image analysis with active learning, the system supports the creation of task-specific datasets that reflect real-world structure.
Who uses Seymour
Seymour is designed for complex, unstructured image data in scientific, geospatial, and industrial domains where labeled data is limited and object definitions are domain specific. It is particularly useful in workflows that require expert input and iteration on labeling strategies.
Capabilities
- End-to-end pipeline from image ingestion to quantitative analysis
- Automated segmentation and featurization of image data
- Object-level labeling through an interactive user interface
- Active learning to prioritize uncertain or high-value samples
- Modular microservice architecture
Advantages
- Rapidly develop classification models from limited labeled data
- Use of expert knowledge through targeted labeling and feedback
- Leverages geometric structure and feature representations
- Interpretable predictions with rigorous uncertainty quantification
Book a consultation
Every engagement starts with a deep exploration of current data structures, problem type, and solution requirements. We will work together to get a shared understanding of where you currently are and where we need to go together.
In particular:
- What type of data, both images and labeled data structure?
- Human expertise and human labelers?
- Project goals?
Contact us
Have a question or want to work with us? Send us a message and we'll get back to you.