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Commit History
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Update training configuration and dependencies
- Modified `requirements.txt` to use flexible versions of PyTorch and related libraries. - Adjusted training parameters in `train.yaml`, reducing epochs from 20 to 8 and batch sizes from 32 and 64 to 8. - Enhanced `train_gnn.py` with gradient accumulation and mixed precision training support. - Fixed data type handling in `graph_models.py` for message passing layer.1508177
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Add data pipeline scripts and configuration files for Visual Genome processing
Ashish Mehta- Introduced .gitignore to exclude unnecessary files and directories. - Added requirements.txt for project dependencies. - Implemented dataset.yaml with download, extraction, and processing configurations. - Created multiple scripts for data handling: download, extract, process, and build splits. - Developed run_baselines.py and eval_baseline.py for baseline model evaluation. - Enhanced README.md with data pipeline instructions and expected outputs. - Established src/data module for data utilities and processing functions.4687353
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Add initial project structure and configuration files for Adjective-Aware Object Embedding
Ashish Mehta- Created README.md detailing project objectives, structure, and execution strategy. - Added dataset.yaml for dataset configuration, including attribute taxonomy and splits. - Introduced eval.yaml for evaluation metrics and visualization settings. - Established experiment.yaml for defining baseline and proposed runs. - Configured model.yaml for encoder settings and GNN architecture. - Developed train.yaml for training parameters and optimization strategies. - Drafted plan_draft.md outlining project goals, architecture, and data preparation strategies.8e53dc6