fieldsoftheworld/ftw-baselines
Oct 2024 – Feb 2026
5 Months active
Languages Used
PythonShellJinjaYAML
Technical Skills
Deep LearningGeospatial AnalysisPython DevelopmentCLI DevelopmentCommand Line Interface (CLI)Computer Vision
Caleb Robinson
PROFILE
Caleb Robinson
Caleb Robinson contributed to the fieldsoftheworld/ftw-baselines repository by developing and refining machine learning pipelines for geospatial data analysis. He implemented features such as flexible inference workflows, RGB-only training modes, and multi-architecture training configurations, focusing on reproducibility and robust evaluation across diverse datasets. Using Python, PyTorch, and CLI tooling, Caleb enhanced data loading, model evaluation, and visualization, including PCA analysis and consensus scoring for image crops. His work addressed compatibility with evolving libraries, improved experiment configuration, and streamlined data processing. The depth of his contributions enabled faster experimentation, more reliable inference, and broader model coverage, supporting data-driven decision-making.
Overall Statistics
Feature vs Bugs
89%Features
Repository Contributions
11Total
Bugs
1
Commits
11
Features
8
Lines of code
3,922
Activity Months5
Your Network
11 peopleShared Repositories
11
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for fieldsoftheworld/ftw-baselines: Delivered RGB-only training capabilities for Window A/B subsets with percentage-based training and inference-time consensus scoring. Updated training datasets and inference modules to support the new mode, and implemented edges computation for train split. Achieved code quality improvements through linting and reliability improvements for disagreement map handling. This work enables more efficient experimentation, improved inference reliability, and lays groundwork for broader deployment.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for fieldsoftheworld/ftw-baselines: Delivered RGB-only training capabilities for Window A/B subsets with percentage-based training and inference-time consensus scoring. Updated training datasets and inference modules to support the new mode, and implemented edges computation for train split. Achieved code quality improvements through linting and reliability improvements for disagreement map handling. This work enables more efficient experimentation, improved inference reliability, and lays groundwork for broader deployment.
February 2026
November 2025
1 Commits • 1 Features
Nov 1, 2025Month: 2025-11. Focused on delivering a feature to enhance image data processing and model training across architectures in fieldsoftheworld/ftw-baselines. The work prioritized business value by improving evaluation robustness across diverse datasets and enabling faster experimentation across model families. No major bugs fixed this month.
November 2025
1 Commits • 1 Features
Nov 1, 2025Month: 2025-11. Focused on delivering a feature to enhance image data processing and model training across architectures in fieldsoftheworld/ftw-baselines. The work prioritized business value by improving evaluation robustness across diverse datasets and enabling faster experimentation across model families. No major bugs fixed this month.
October 2025
3 Commits • 3 Features
Oct 1, 2025Month 2025-10 – ftw-baselines: Three high-impact features were delivered to expand experimentation flexibility, data modalities, and cross-country evaluation, paired with refactors to improve reliability and reproducibility. The updates reduce manual steps, accelerate iteration cycles, and broaden model coverage, directly supporting stronger baselines and data-driven decision-making.
3 Commits • 3 Features
Oct 1, 2025Month 2025-10 – ftw-baselines: Three high-impact features were delivered to expand experimentation flexibility, data modalities, and cross-country evaluation, paired with refactors to improve reliability and reproducibility. The updates reduce manual steps, accelerate iteration cycles, and broaden model coverage, directly supporting stronger baselines and data-driven decision-making.
October 2025
September 2025
5 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for fieldsoftheworld/ftw-baselines focusing on delivering a more robust evaluation/inference workflow, cleaning up nonfunctional features, and enabling deeper analysis tooling to accelerate experimentation and business value.
September 2025
5 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for fieldsoftheworld/ftw-baselines focusing on delivering a more robust evaluation/inference workflow, cleaning up nonfunctional features, and enabling deeper analysis tooling to accelerate experimentation and business value.
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024 focused on updating compatibility with TorchGeo 0.6 for the fieldsoftheworld/ftw-baselines project and refining the Mask I/O workflow to deliver more reliable data products. The work improves stability and integration with downstream ML pipelines while maintaining a lean change set.
1 Commits • 1 Features
Oct 1, 2024October 2024 focused on updating compatibility with TorchGeo 0.6 for the fieldsoftheworld/ftw-baselines project and refining the Mask I/O workflow to deliver more reliable data products. The work improves stability and integration with downstream ML pipelines while maintaining a lean change set.
October 2024
Activity
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Quality Metrics
Correctness83.6%
Maintainability81.8%
Architecture79.0%
Performance74.6%
AI Usage38.2%
Skills & Technologies
Programming Languages
JinjaPythonShellYAML
Technical Skills
Backend DevelopmentCI/CDCLI DevelopmentCommand Line Interface (CLI)Computer VisionData AugmentationData EngineeringData EvaluationData LoadingData ScienceData VisualizationDataset ManagementDeep LearningFull Stack DevelopmentGeospatial Analysis
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline