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Commit History
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Refactor Gradio app for model selection and checkpoint handling
- Updated `app.py` to support multiple GNN models and download checkpoints from Hugging Face Hub. - Enhanced user interface to allow selection between baseline, GNN 1, and GNN 2 models. - Added a new `PROJECT_REPORT.md` documenting project overview, data processing, and model training details.36fca7c -
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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Fix infer imports for HF Space
6333fb2 -
Add HF Space configuration header to README
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Enhance README.md with inference instructions and Gradio UI details
- Added sections for CLI inference commands for both baseline and GNN checkpoints. - Included instructions for launching the Gradio app and using it to upload images and select models. - Updated the README to provide clearer guidance on inference processes.cca0e97 -
Add Gradio app for inference and model selection
- Introduced app.py for a Gradio interface allowing users to upload images and select between baseline and GNN models for inference. - Implemented ModelRegistry for caching loaded models and managing checkpoints. - Added README_SPACE.md to guide users on setting up the Hugging Face Space with the necessary files and configurations. - Created requirements-space.txt for dependencies specific to the Gradio app. - Developed scripts for single-image inference and comparison of baseline vs GNN model metrics. - Enhanced existing README.md with updated training and evaluation instructions for both models.419fc11 -
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 -
first commit
Ashish Mehtaecc9495