
Worked on the GHOST-Science-Club/tree-classification-irim repository to deliver a flexible, multi-architecture image classification framework over two months. Migrated the core model from ResNet to Vision Transformer, integrating pre-trained ImageNet weights and configurable layer freezing to balance accuracy and efficiency. Developed a model factory and configuration-driven system supporting ResNet18, InceptionV3, and ViT, with adaptive preprocessing and architecture-aware initialization. Enhanced Inception training by enabling auxiliary logits and robust output handling, improving stability across models. Used Python, PyTorch, and YAML to implement these features, focusing on maintainability, cross-architecture compatibility, and streamlined experimentation for future development and deployment.
Delivered 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.
Delivered 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.
March 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 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.

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