deepinv/deepinv
Feb 2025 – Sep 2025
3 Months active
Languages Used
PythonC++
Technical Skills
Bug FixingModel TrainingImage ProcessingNumerical MethodsScientific ComputingSoftware Engineering
Johannes Hertrich
PROFILE
Johannes Hertrich
Contributed to the deepinv repository by developing and enhancing core features for scientific computing and image processing workflows. Delivered end-to-end BSDS500 dataset integration, including download utilities, documentation, and seamless support for image rotation evaluation. Improved the tomography module by implementing backward path calculations and an autograd-enabled Radon transform, refining metric handling for SSIM and PSNR, and adding boundary interpolation options. Addressed model training stability by fixing GaussianMixtureModel weight handling and updating the NFTrainer loss API. Work emphasized reproducibility, maintainability, and robust benchmarking, utilizing Python and C++ with a focus on dataset management, numerical methods, and rigorous testing practices.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
2
Lines of code
425
Activity Months3
Your Network
68 peopleWork History
September 2025
1 Commits • 1 Features
Sep 1, 2025September 2025: Delivered BSDS500 Dataset Integration in deepinv, expanding data ingestion and benchmarking capabilities. Implemented dataset support end-to-end with download/extract utilities, tests, and documentation updates; integrated with the library’s image rotation workflow to enable end-to-end evaluation. Improved code quality and test reliability with formatting (Black), import cleanup, pathlib-based file handling, and rotation tests for CBSD68. Updated user guide and changelog to reflect the new dataset and usage. This work reduces setup friction, enhances reproducibility, and broadens the library’s applicability for research and production use.
1 Commits • 1 Features
Sep 1, 2025September 2025: Delivered BSDS500 Dataset Integration in deepinv, expanding data ingestion and benchmarking capabilities. Implemented dataset support end-to-end with download/extract utilities, tests, and documentation updates; integrated with the library’s image rotation workflow to enable end-to-end evaluation. Improved code quality and test reliability with formatting (Black), import cleanup, pathlib-based file handling, and rotation tests for CBSD68. Updated user guide and changelog to reflect the new dataset and usage. This work reduces setup friction, enhances reproducibility, and broadens the library’s applicability for research and production use.
September 2025
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for deepinv/deepinv. Key features delivered: Tomography Module Enhancements including backward path calculations, an autograd wrapper for Radon transform to ensure exact adjoint computations, and metric improvements (SSIM/PSNR) with boundary interpolation options for filtered back-projection. Commit: f881df22045f53474922f64ce7b553e9cfa1925f. Major bugs fixed: No explicit bug IDs provided; changes address adjoint computation consistency and metric boundary handling, with updated tests and documentation. Overall impact and accomplishments: Strengthened end-to-end differentiable tomography workflow, enabling more reliable optimization, improved reconstruction quality, and more robust benchmarking. Technologies/skills demonstrated: Python, differentiable programming with autograd, Radon transform, tomography algorithms, unit testing, documentation.
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for deepinv/deepinv. Key features delivered: Tomography Module Enhancements including backward path calculations, an autograd wrapper for Radon transform to ensure exact adjoint computations, and metric improvements (SSIM/PSNR) with boundary interpolation options for filtered back-projection. Commit: f881df22045f53474922f64ce7b553e9cfa1925f. Major bugs fixed: No explicit bug IDs provided; changes address adjoint computation consistency and metric boundary handling, with updated tests and documentation. Overall impact and accomplishments: Strengthened end-to-end differentiable tomography workflow, enabling more reliable optimization, improved reconstruction quality, and more robust benchmarking. Technologies/skills demonstrated: Python, differentiable programming with autograd, Radon transform, tomography algorithms, unit testing, documentation.
February 2025
1 Commits
Feb 1, 2025February 2025 monthly summary for deepinv/deepinv: focused on stabilizing the GaussianMixtureModel within the training loop. Delivered a bug fix for incorrect GaussianMixtureModel weight handling and updated NFTrainer.compute_loss API to align with the training loop, addressing model update and iteration handling issues. This work improves training stability, reproducibility, and maintainability. Commit referenced: 4042de3aa5d0510dd817122d407a926e0ca422b8 (Patch prior bugs #396).
1 Commits
Feb 1, 2025February 2025 monthly summary for deepinv/deepinv: focused on stabilizing the GaussianMixtureModel within the training loop. Delivered a bug fix for incorrect GaussianMixtureModel weight handling and updated NFTrainer.compute_loss API to align with the training loop, addressing model update and iteration handling issues. This work improves training stability, reproducibility, and maintainability. Commit referenced: 4042de3aa5d0510dd817122d407a926e0ca422b8 (Patch prior bugs #396).
February 2025
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Quality Metrics
Correctness86.6%
Maintainability80.0%
Architecture80.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Bug FixingDataset ManagementDocumentationImage ProcessingModel TrainingNumerical MethodsScientific ComputingSoftware EngineeringTesting
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