Repository Guidelines

Project Structure & Module Organization

Source lives in src/ with packages: src/dataset/ (dataset abstractions + Crack500 loader), src/model/ (HF adapters, Trainer wrappers, predictor + CLI), src/model_configuration/ (dataclass configs + registry), src/evaluation/ (metrics, pipeline evaluator, CLI), src/visualization/ (overlay/galleries + pipeline-driven CLI), and src/tasks/ (task configs + pipeline runner for train→eval→viz). Datasets stay in crack500/, and experiment artifacts should land in results/<prompt_type>/....

Build, Test, and Development Commands

Install dependencies with pip install -r requirements.txt inside the sam2 env. The CLI wrappers now call the TaskRunner: python run_bbox_evaluation.py --data_root ./crack500 --test_file ./crack500/test.txt --expand_ratio 0.05 executes bbox evaluate + visualize, while python run_point_evaluation.py --point_configs 1 3 5 sweeps multi-point setups. Reusable pipelines can be launched via the TOML templates (tasks/bbox_eval.toml, tasks/point_eval.toml) using python -m src.tasks.run_task --task_file <file>. HF-native commands remain available for fine-tuning (python -m src.model.train_hf ...), metrics (python -m src.evaluation.run_pipeline ...), and overlays (python -m src.visualization.run_pipeline_vis ...).

Coding Style & Naming Conventions

Follow PEP 8 with 4-space indents, <=100-character lines, snake_case functions, PascalCase classes, and explicit type hints. Keep logic within its package (dataset readers under src/dataset/, Trainer utilities inside src/model/) and prefer pathlib, f-strings, and concise docstrings that clarify SAM2-specific heuristics.

Refactor & HF Integration Roadmap

Dataset module: generalize loaders so Crack500 and future benchmarks share a dataset interface emitting HF dicts (pixel_values, prompt_boxes).
Model + configuration: wrap SAM2 checkpoints with transformers classes, ship reusable configs, and add HF fine-tuning utilities (LoRA/PEFT optional).
Evaluation & visualization: move metric code into src/evaluation/ and visual helpers into src/visualization/, both driven by a shared HF pipeline API.
Benchmarks: add scripts that compare pre-trained vs fine-tuned models and persist summaries to results/<dataset>/<model_tag>/evaluation_summary.json.

Testing Guidelines

Treat python run_bbox_evaluation.py --skip_visualization as regression test, then spot-check overlays via --num_vis 5. Run python -m src.evaluation.run_pipeline --config_name sam2_bbox_prompt --max_samples 16 so dataset→pipeline→evaluation is exercised end-to-end, logging IoU/Dice deltas against committed summaries.

Commit & Pull Request Guidelines

Adopt short, imperative commit titles (dataset: add hf reader). Describe scope and runnable commands in PR descriptions, attach metric/visual screenshots from results/.../visualizations/, and note any new configs or checkpoints referenced. Highlight where changes sit in the planned module boundaries so reviewers can track the refactor’s progress.

Data & Configuration Tips

Never commit Crack500 imagery or SAM2 weights—verify .gitignore coverage before pushing. Add datasets via config entries instead of absolute paths, and keep results/<prompt_type>/<experiment_tag>/ naming so HF sweeps can traverse directories predictably.

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