- Replace bbox-prompted segmentation with OWLv2 text-guided object detection
- Refactor HashPipeline from nn.Module to plain class with modular stage methods
- Add detect_batch, segment_batch, filter_batch for explicit pipeline stages
- Rename forward to forward_batch with text_labels API instead of bboxes
- Add mask_scoring_config, score_threshold, postprocess_threshold configuration
- Update model_loader to expose Dinov2Model type annotation
- Refactor model_loader.py: improve return type annotations from tuple[Any, Any] to tuple[AutoImageProcessor, AutoModel]
- Refactor proposal/core.py: add input validation for mask array dimensionality, handle 2D masks and batch dimensions gracefully
- Refactor proposal_segament.ipynb: replace inline model loading with centralized load_owlv2_model() and load_sam_model() functions, use batched detect_objects_batch() and generate_proposals_batch() APIs
- switch OWLv2 loader return type to Owlv2ForObjectDetection
- add detect_objects and detect_objects_batch with two-stage score filtering
- add DetectionResult typed dict and conversion helper for post-processed outputs
- export new detection APIs from proposal module
- Replace SAM2AutomaticMaskGenerator pipeline with Sam2Processor+Sam2Model
- Freeze SAM model parameters at load time, removing torch.no_grad() at call sites
- Rewrite proposal/core.py to use bbox prompts instead of automatic point sampling
- Add bboxes parameter to all HashPipeline public methods (forward, forward_dataset, extract_features, extract_features_dataset)
- Extract mask filtering logic (_filter_masks) from proposal into pipeline
- Rename object_score/ to filter/
- Add load_owlv2_model to model_loader
- Rename notebooks/test.py to habitat_sim_setup.py
- Remove dino_compressor.py and segament_compressor.py
- Rewrite pipeline.py to inline DINO into HashPipeline
- Maintain backward compatibility: SAMHashPipeline alias
- Update tests and benchmark.py
