update

README.md

@@ -982,523 +982,6 @@ We thank the authors of all included models for their open-source implementation
 - [timm](https://github.com/huggingface/pytorch-image-models) for pretrained backbone weights.
 - University of Peradeniya for computational resources.
 
-## Audit Appendix: Dataset Config Facts
-
-### Dataset Fact 1
-Config `custom_cd` is defined in `configs/datasets/custom_cd.yaml`.
-Root pattern is `${DATA_ROOT}/Custom-CD`.
-Image size is `256`.
-Channels are `3`.
-Mask folder is `Mask`.
-Pre-change folder is `A`.
-Post-change folder is `B`.
-Splits are `train`, `val`, and `test`.
-Batch size is `8`.
-Workers are `4`.
-Evaluation threshold is `0.5`.
-
-### Dataset Fact 2
-Config `dsifn_cd` is defined in `configs/datasets/dsifn_cd.yaml`.
-Root pattern is `${DATA_ROOT}/DSIFN-CD/DSIFN`.
-Image size is `256`.
-Channels are `3`.
-Mask folder is `mask`.
-Pre-change folder is `t1`.
-Post-change folder is `t2`.
-Splits are `train`, `val`, and `test`.
-Batch size is `8`.
-Workers are `4`.
-Evaluation threshold is `0.5`.
-
-### Dataset Fact 3
-Config `kate_cd` is defined in `configs/datasets/kate_cd.yaml`.
-Root pattern is `${DATA_ROOT}/KATE-CD-256`.
-Image size is `256`.
-Channels are `3`.
-Mask folder is `label`.
-Pre-change folder is `A`.
-Post-change folder is `B`.
-Splits are `train`, `val`, and `test`.
-Batch size is `8`.
-Workers are `4`.
-Evaluation threshold is `0.5`.
-
-### Dataset Fact 4
-Config `levir_cd` is defined in `configs/datasets/levir_cd.yaml`.
-Root pattern is `${DATA_ROOT}/LEVIR-CD-plus-256`.
-Image size is `256`.
-Channels are `3`.
-Mask folder is `Mask`.
-Pre-change folder is `A`.
-Post-change folder is `B`.
-Splits are `train`, `val`, and `test`.
-Batch size is `8`.
-Workers are `4`.
-Evaluation threshold is `0.5`.
-
-### Dataset Fact 5
-Config `levir_cd_test_as_val` is defined in `configs/datasets/levir_cd_test_as_val.yaml`.
-Root pattern is `${DATA_ROOT}/LEVIR-CD-plus-256-test-as-val`.
-Image size is `256`.
-Channels are `3`.
-Mask folder is `Mask`.
-Pre-change folder is `A`.
-Post-change folder is `B`.
-Splits are `train`, `val`, and `test`.
-Protocol is `test_as_val_literature`.
-Batch size is `8`.
-Workers are `4`.
-
-### Dataset Fact 6
-Config `sysu_cd` is defined in `configs/datasets/sysu_cd.yaml`.
-Root pattern is `${DATA_ROOT}/SYSU-CD-folders`.
-Image size is `256`.
-Channels are `3`.
-Mask folder is `Mask`.
-Pre-change folder is `A`.
-Post-change folder is `B`.
-Splits are `train`, `val`, and `test`.
-Batch size is `8`.
-Workers are `4`.
-Evaluation threshold is `0.5`.
-
-### Dataset Fact 7
-Config `whu_cd` is defined in `configs/datasets/whu_cd.yaml`.
-Root pattern is `${DATA_ROOT}/WHU-CD`.
-Image size is `256`.
-Channels are `3`.
-Mask folder is `OUT`.
-Pre-change folder is `A`.
-Post-change folder is `B`.
-Splits are `train`, `val`, and `test`.
-Batch size is `8`.
-Workers are `4`.
-Evaluation threshold is `0.5`.
-
-### Dataset Fact 8
-Config `wildfire_s2` is defined in `configs/datasets/wildfire_s2.yaml`.
-Root pattern is `${DATA_ROOT}/WildFireS2`.
-Image size is `256`.
-Channels are `3`.
-Mask folder is `label`.
-Pre-change folder is `A`.
-Post-change folder is `B`.
-Splits are `train`, `val`, and `test`.
-Batch size is `8`.
-Workers are `4`.
-Evaluation threshold is `0.5`.
-
-## Audit Appendix: Model Config Facts
-
-### Model Fact 1
-Model `bifa` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/bifa.yaml`.
-Training script is `train/train_bifa.py`.
-Source path is `BiFA`.
-Framework is `pytorch`.
-Backbone is `MixTransformer`.
-Optimizer is `adam`.
-Learning rate is `0.0001`.
-Epochs are `200`.
-Loss is `ce_dice`.
-
-### Model Fact 2
-Model `bit_cd` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/bit_cd.yaml`.
-Training script is `train/train_bit_cd.py`.
-Source path is `BIT_CD`.
-Framework is `pytorch`.
-Backbone is `ResNet-18 + base_transformer_pos_s4_dd8`.
-Optimizer is `sgd`.
-Learning rate is `0.01`.
-Epochs are `200`.
-Loss is `cross_entropy`.
-
-### Model Fact 3
-Model `cdmamba` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/cdmamba.yaml`.
-Training script is `train/train_cdmamba.py`.
-Source path is `CDMamba`.
-Framework is `pytorch`.
-Backbone is `Mamba`.
-Optimizer is `adam`.
-Learning rate is `0.0001`.
-Epochs are `200`.
-Loss is `ce_dice`.
-
-### Model Fact 4
-Model `cgnet` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/cgnet.yaml`.
-Training script is `train/train_cgnet.py`.
-Source path is `model_repos/CGNet-CD`.
-Framework is `pytorch`.
-Backbone is `null` in YAML and implemented by upstream CGNet.
-Optimizer is `adam`.
-Learning rate is `0.0001`.
-Epochs are `200`.
-Loss is `original_cgnet_loss`.
-
-### Model Fact 5
-Model `change3d` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/change3d.yaml`.
-Training script is `train/train_change3d.py`.
-Source path is `Change3D`.
-Framework is `pytorch`.
-Backbone is `X3D-L`.
-Optimizer is `adam`.
-Learning rate is `0.0002`.
-Epochs are `200`.
-Loss is `bce_dice`.
-
-### Model Fact 6
-Model `changeformer` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/changeformer.yaml`.
-Training script is `train/train_changeformer.py`.
-Source path is `ChangeFormer`.
-Framework is `pytorch`.
-Backbone is `MiT-b4`.
-Optimizer is `adamw`.
-Learning rate is `0.00006`.
-Epochs are `200`.
-Loss is `cross_entropy`.
-
-### Model Fact 7
-Model `changemamba` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/changemamba.yaml`.
-Training script is `train/train_changemamba.py`.
-Source path is `model_repos/ChangeMamba`.
-Framework is `mmsegmentation`.
-Backbone is `VMamba`.
-Optimizer is `adamw`.
-Learning rate is `0.00006`.
-Epochs are `200`.
-Loss is `mmseg_decode_head_loss`.
-
-### Model Fact 8
-Model `changer` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/changer.yaml`.
-Training script is `train/train_changer.py`.
-Source path is `model_repos/open-cd`.
-Framework is `mmsegmentation`.
-Backbone is `ResNet-18`.
-Optimizer is `adamw`.
-Learning rate is `0.00006`.
-Epochs are `200`.
-Loss is `mmseg_decode_head_loss`.
-
-### Model Fact 9
-Model `dsamnet` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/dsamnet.yaml`.
-Training script is `train/train_dsamnet.py`.
-Source path is `model_repos/DSAMNet`.
-Framework is `pytorch`.
-Backbone is `ResNet`.
-Optimizer is `adam`.
-Learning rate is `0.0001`.
-Epochs are `200`.
-Loss is `contrastive_triplet_bce_original`.
-
-### Model Fact 10
-Model `dsifn` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/dsifn.yaml`.
-Training script is `train/train_dsifn.py`.
-Source path is `IFNet`.
-Framework is `pytorch`.
-Backbone is `VGG-16`.
-Optimizer is `adam`.
-Learning rate is `0.0001`.
-Epochs are `200`.
-Loss is `dsifn_original`.
-
-### Model Fact 11
-Model `elgcnet` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/elgcnet.yaml`.
-Training script is `train/train_elgcnet.py`.
-Source path is `model_repos/elgcnet`.
-Framework is `pytorch`.
-Backbone is `ResNet-18`.
-Optimizer is `sgd`.
-Learning rate is `0.01`.
-Epochs are `200`.
-Loss is `cross_entropy`.
-
-### Model Fact 12
-Model `fc_ef` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/fc_ef.yaml`.
-Training script is `train/train_fc_variants.py`.
-Source path is `model_repos/fully_convolutional_change_detection`.
-Framework is `pytorch`.
-Backbone is `null`.
-Optimizer is `adam`.
-Learning rate is `0.0001`.
-Epochs are `200`.
-Loss is `bce_dice`.
-
-### Model Fact 13
-Model `fc_siam_conc` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/fc_siam_conc.yaml`.
-Training script is `train/train_fc_variants.py`.
-Source path is `model_repos/fully_convolutional_change_detection`.
-Framework is `pytorch`.
-Backbone is `null`.
-Optimizer is `adam`.
-Learning rate is `0.0001`.
-Epochs are `200`.
-Loss is `bce_dice`.
-
-### Model Fact 14
-Model `fc_siam_diff` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/fc_siam_diff.yaml`.
-Training script is `train/train_fc_variants.py`.
-Source path is `model_repos/fully_convolutional_change_detection`.
-Framework is `pytorch`.
-Backbone is `null`.
-Optimizer is `adam`.
-Learning rate is `0.0001`.
-Epochs are `200`.
-Loss is `bce_dice`.
-
-### Model Fact 15
-Model `hanet` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/hanet.yaml`.
-Training script is `train/train_hanet.py`.
-Source path is `model_repos/HANet-CD`.
-Framework is `pytorch`.
-Backbone is `null` in YAML and implemented by upstream HANet.
-Optimizer is `adam`.
-Learning rate is `0.0001`.
-Epochs are `200`.
-Loss is `original_hanet_loss`.
-
-### Model Fact 16
-Model `ifnet` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/ifnet.yaml`.
-Training script is `train/train_ifnet.py`.
-Source path is `IFNet`.
-Framework is `pytorch`.
-Backbone is `VGG-16`.
-Optimizer is `adam`.
-Learning rate is `0.0001`.
-Epochs are `200`.
-Loss is `dsifn_cd_loss`.
-
-### Model Fact 17
-Model `rsm_cd` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/rsm_cd.yaml`.
-Training script is `train/train_rsm_cd.py`.
-Source path is `RSM-CD/change_detection_mamba`.
-Framework is `pytorch`.
-Backbone is `VMamba/RSM-CD tiny`.
-Optimizer is `adamw`.
-Learning rate is `0.001`.
-Epochs are `200`.
-Loss is `fccdn_loss_without_seg`.
-
-### Model Fact 18
-Model `schanger` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/schanger.yaml`.
-Training script is `train/train_schanger.py`.
-Source path is `SChanger`.
-Framework is `pytorch`.
-Backbone is `SChanger-base`.
-Optimizer is `adamw`.
-Learning rate is `0.00006`.
-Epochs are `200`.
-Loss is `bce_deep_supervision`.
-
-### Model Fact 19
-Model `siam_nestedunet` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/siam_nestedunet.yaml`.
-Training script is `train/train_siam_nestedunet.py`.
-Source path is `Siam-NestedUNet`.
-Framework is `pytorch`.
-Backbone is `UNet++`.
-Optimizer is `adam`.
-Learning rate is `0.00015`.
-Epochs are `200`.
-Loss is `cross_entropy`.
-
-### Model Fact 20
-Model `stanet` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/stanet.yaml`.
-Training script is `train/train_stanet.py`.
-Source path is `STANet`.
-Framework is `pytorch`.
-Backbone is `ResNet-18 + PAM`.
-Optimizer is `adam`.
-Learning rate is `0.001`.
-Epochs are `200`.
-Loss is `bcl_contrastive`.
-
-### Model Fact 21
-Model `tinycd` is registered in `configs/models/registry.yaml`.
-Config path is `configs/models/tinycd.yaml`.
-Training script is `train/train_tinycd.py`.
-Source path is `model_repos/Tiny_model_4_CD`.
-Framework is `pytorch`.
-Backbone is `EfficientNet-B4`.
-Optimizer is `adamw`.
-Learning rate is `0.0001`.
-Epochs are `200`.
-Loss is `bce_dice`.
-
-## Audit Appendix: Utility Roles
-
-### Utility Fact 1
-`utils/config.py` provides generic YAML and JSON loading.
-It also provides `deep_merge`.
-It exposes a simple model registry loader.
-It does not expand `${DATA_ROOT}`.
-
-### Utility Fact 2
-`utils/config_loader.py` provides the current dataset and model config API.
-It expands `${DATA_ROOT}`.
-It checks `/new-home/buddhiw/Datasets` by default.
-It checks the sibling `mamba-cd/datasets` fallback.
-It attaches `_config_path` and `_dataset_name`.
-
-### Utility Fact 3
-`utils/dataset_list_generator.py` writes `list/train.txt`, `list/val.txt`, and `list/test.txt`.
-It scans image folders such as `A`, `T1`, `t1`, `img`, `images`, or `image`.
-It is idempotent unless `overwrite=True`.
-
-### Utility Fact 4
-`utils/env_checker.py` checks imports for torch, torchvision, numpy, Pillow, OpenCV, PyYAML, tqdm, timm, einops, mmengine, mmcv, mmseg, and gdown.
-It prints install hints.
-It does not auto-install packages.
-
-### Utility Fact 5
-`utils/legacy_config_writer.py` prepares list-based generated views for legacy trainers.
-It writes generated JSON configs for BiFA and CDMamba.
-It converts `0/1` masks to lossless `0/255` PNG data where needed.
-
-### Utility Fact 6
-`utils/metrics.py` implements binary metrics.
-It supports class-logit tensors.
-It supports thresholded probability maps.
-It returns F1, IoU, OA, precision, recall, kappa, and confusion counts.
-
-### Utility Fact 7
-`utils/results.py` writes JSON and append-only CSV-like result files.
-It adds a UTC timestamp when appending a result row.
-
-### Utility Fact 8
-`utils/results_writer.py` writes `metrics_<split>.json`.
-It scans `results/*/*/metrics_test.json`.
-It regenerates `results/comparison_table.csv`.
-
-### Utility Fact 9
-`utils/weight_downloader.py` manages VMamba Zenodo/GDrive downloads.
-It warms timm models.
-It warms torchvision models.
-It logs downloads to `results/download_log.jsonl`.
-
-## Audit Appendix: Wrapper Modes
-
-### Wrapper Fact 1
-`bifa` uses the shared wrapper and launches `BiFA/train_cd.py` for non-default datasets.
-It writes generated JSON through `utils/legacy_config_writer.py`.
-It is subprocess mode.
-
-### Wrapper Fact 2
-`bit_cd` uses the shared wrapper and launches `BIT_CD/main_cd.py`.
-It uses generated legacy list views for non-default datasets.
-It is subprocess mode.
-
-### Wrapper Fact 3
-`cdmamba` uses the shared wrapper and launches `CDMamba/train_cd.py`.
-It writes generated JSON through `utils/legacy_config_writer.py`.
-It is subprocess mode.
-
-### Wrapper Fact 4
-`changeformer` uses the shared wrapper and launches `ChangeFormer/main_cd.py`.
-It uses generated legacy list views.
-It is subprocess mode.
-
-### Wrapper Fact 5
-`dsamnet` launches `model_repos/DSAMNet/train.py`.
-It passes direct train and validation folder paths.
-It warms torchvision ResNet weights before training.
-It is subprocess mode.
-
-### Wrapper Fact 6
-`cgnet` launches `model_repos/CGNet-CD/train_CGNet.py`.
-It builds matched split views under `generated_dataset_views/`.
-It warms torchvision ResNet-50 weights before training.
-It is subprocess mode.
-
-### Wrapper Fact 7
-`hanet` launches `model_repos/HANet-CD/trainHCX.py`.
-It writes a generated metadata JSON.
-It builds matched split views under `generated_dataset_views/`.
-It is subprocess mode.
-
-### Wrapper Fact 8
-`tinycd` launches `model_repos/Tiny_model_4_CD/training.py`.
-It builds TinyCD list views.
-It warms timm EfficientNet-B4.
-It is subprocess mode.
-
-### Wrapper Fact 9
-`changer` launches `model_repos/open-cd/tools/train.py`.
-It writes an Open-CD config under `generated_configs/`.
-It requires mmengine, mmcv, and mmsegmentation.
-It is subprocess mode.
-
-### Wrapper Fact 10
-`changemamba` launches `model_repos/ChangeMamba/changedetection/script/train_MambaBCD.py`.
-It builds ChangeMamba `T1`, `T2`, and `GT` views.
-It uses VMamba-Tiny weights from Zenodo record `14037770`.
-It is subprocess mode.
-
-### Wrapper Fact 11
-`fc_ef`, `fc_siam_conc`, and `fc_siam_diff` use `train/fc_adapter.py`.
-They train directly in this repository process.
-They use `datasets/cd_dataset.py`.
-They save validation metrics to `results/<model>/<dataset>/metrics_val.json`.
-
-### Wrapper Fact 12
-`run_training.py` skips missing dataset roots before launching wrappers.
-It skips completed model-dataset pairs when `metrics_test.json` exists.
-It supports `--dry-run`, `--resume`, `--eval-only`, `--gpu`, and `--force`.
-
-### Wrapper Fact 13
-`evaluate.py` can resolve datasets and checkpoints.
-It regenerates the comparison CSV.
-It still needs architecture-specific model loaders.
-
-## Audit Appendix: Clone Status
-
-### Cloned External Repositories
-`model_repos/CGNet-CD` exists.
-`model_repos/ChangeMamba` exists.
-`model_repos/DSAMNet` exists.
-`model_repos/HANet-CD` exists.
-`model_repos/TinyCD` exists.
-`model_repos/Tiny_model_4_CD` exists.
-`model_repos/elgcnet` exists.
-`model_repos/fully_convolutional_change_detection` exists.
-`model_repos/open-cd` exists.
-
-### Root-Level Model Repositories
-`BIT_CD` exists.
-`BiFA` exists.
-`CDMamba` exists.
-`Change3D` exists.
-`ChangeFormer` exists.
-`IFNet` exists.
-`RSM-CD` exists.
-`SChanger` exists.
-`Siam-NestedUNet` exists.
-`STANet` exists.
-
-### Missing or Unconfirmed Clone Targets
-`model_repos/DSIFN` is not present.
-The official DSIFN URL for that exact clone target was not confirmed from audited files.
-`setup.py` therefore does not guess it.
-`SChanger` has a root-level copy, but the setup script marks its clone URL as unconfirmed.
-
-## Audit Appendix: Command Reference
 
 ### Setup Commands
 `python setup.py`
@@ -1534,22 +1017,6 @@ Fetches managed pretrained weights.
 `python utils/weight_downloader.py --list`
 Lists managed VMamba weight files.
 
-## Audit Appendix: Result Provenance
-
-### Current Result State
-No `metrics_test.json` files were found during the audit.
-`results/comparison_table.csv` contains only a header row.
-Fresh benchmark numbers must therefore be produced by new training and evaluation runs.
-
-### Historical Result State
-The old README contained WildFire-S2 checkpoint numbers for `change3d`, `bit_cd`, `changeformer`, and `stanet`.
-Those numbers remain in this README only with a dagger marker.
-They are not presented as current unified-harness results.
-
-### Reporting Rule
-A number in the Results section must come from either `metrics_test.json` or a daggered historical note.
-Unknown values are shown as `—`.
-Empty cells are avoided.
 
 ## Audit Appendix: Environment Snapshot