GHOST-Science-Club/tree-classification-irim
Mar 2025 – Apr 2025
2 Months active
Languages Used
PythonYAML
Technical Skills
Code RefactoringComputer VisionDeep LearningMachine LearningPyTorchPyTorch Lightning
addobosz
PROFILE
Addobosz
Adam Dobosz developed a configurable, multi-architecture image classification framework for the GHOST-Science-Club/tree-classification-irim repository, focusing on deep learning and computer vision challenges. He migrated the core model from ResNet to a Vision Transformer, integrating pre-trained ImageNet weights and optional layer freezing to balance accuracy and computational efficiency. Adam also introduced a model factory supporting ResNet18, InceptionV3, and ViT, with adaptive preprocessing and architecture-aware initialization. His work included Inception-specific training optimizations, robust configuration management using YAML, and extensive local testing. Implemented primarily in Python with PyTorch and PyTorch Lightning, these changes improved maintainability and cross-architecture compatibility.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
13Total
Bugs
0
Commits
13
Features
3
Lines of code
288
Activity Months2
Your Network
5 people
Work History
April 2025
10 Commits • 2 Features
Apr 1, 2025Delivered a configurable, multi-architecture image classifier framework and Inception-specific training optimizations for the GHOST-Science-Club/tree-classification-irim repo in April 2025. Implemented a model factory and config-driven classifier to support ResNet18, InceptionV3, and ViT with architecture-aware preprocessing and initialization. Added Inception-specific training enhancements to enable auxiliary logits and robust handling of Inception outputs, improving stability and cross-architecture compatibility. Completed extensive local testing across architectures, updated configuration (config.yaml), and resolved quality issues (flake8) to raise reliability and maintainability. This work accelerates future feature delivery and enables broader deployment with lower risk.
10 Commits • 2 Features
Apr 1, 2025Delivered a configurable, multi-architecture image classifier framework and Inception-specific training optimizations for the GHOST-Science-Club/tree-classification-irim repo in April 2025. Implemented a model factory and config-driven classifier to support ResNet18, InceptionV3, and ViT with architecture-aware preprocessing and initialization. Added Inception-specific training enhancements to enable auxiliary logits and robust handling of Inception outputs, improving stability and cross-architecture compatibility. Completed extensive local testing across architectures, updated configuration (config.yaml), and resolved quality issues (flake8) to raise reliability and maintainability. This work accelerates future feature delivery and enables broader deployment with lower risk.
April 2025
March 2025
3 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for the GHOST-Science-Club/tree-classification-irim workstream. The team delivered a migration from a ResNet-based classifier to a Vision Transformer (ViT) with a robust training setup, improving potential accuracy through transfer learning and streamlined experimentation. Cleaned legacy code to reduce maintenance overhead and ensure training updates target the correct ViT heads.
March 2025
3 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for the GHOST-Science-Club/tree-classification-irim workstream. The team delivered a migration from a ResNet-based classifier to a Vision Transformer (ViT) with a robust training setup, improving potential accuracy through transfer learning and streamlined experimentation. Cleaned legacy code to reduce maintenance overhead and ensure training updates target the correct ViT heads.
Activity
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Quality Metrics
Correctness86.2%
Maintainability90.8%
Architecture84.6%
Performance81.6%
AI Usage21.6%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
Bug FixingCode RefactoringComputer VisionConfiguration ManagementDeep LearningImage PreprocessingMachine LearningModel ArchitectureModel TrainingPyTorchPyTorch LightningPythonTransformers
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