Upload folder using huggingface_hub

.gitattributes

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README.md

@@ -1,3 +1,164 @@
 ---
 license: mit
+library_name: pytorch
+tags:
+- sonar
+- passive-sonar
+- signal-processing
+- ocean-acoustics
+- additive-models
+- low-rank-modeling
+- masked-modeling
+- swellex96
+- pytorch
+model-index:
+- name: SonarKAD v1.0.2
+  results:
+  - task:
+      type: other
+      name: Masked additive-low-rank sonar field regression
+    dataset:
+      name: SWellEx-96 S5 VLA tonal field
+      type: other
+    metrics:
+    - name: RMSE
+      type: rmse
+      value: 3.138
+      unit: dB
+    - name: Explained variance
+      type: explained_variance
+      value: 0.776
+  - task:
+      type: other
+      name: Masked additive-low-rank sonar field regression
+    dataset:
+      name: SWellEx-96 S59 VLA tonal field
+      type: other
+    metrics:
+    - name: RMSE
+      type: rmse
+      value: 3.409
+      unit: dB
+    - name: Explained variance
+      type: explained_variance
+      value: 0.653
 ---
+
+# SonarKAD v1.0.2
+
+This repository stores the public output bundle for **SonarKAD**, a masked additive--low-rank diagnostic model for passive-sonar range--frequency fields.
+
+SonarKAD first fits smooth range and frequency marginals, projects candidate low-rank residuals away from the observed additive subspace, and then selects the smallest interaction rank that improves blocked validation within a one-standard-error rule.
+
+## What is included
+
+The expected uploaded tree is:
+
+```text
+results/selected_models_summary.json
+results/selected_models/swellex96_s5_vla/selected_model_manifest.json
+results/selected_models/swellex96_s5_vla/selected_K01/run/sonarkad_model.pt
+results/selected_models/swellex96_s5_vla/selected_K01/run/components.pt
+results/selected_models/swellex96_s5_vla/selected_K01/run/results_cv.json
+results/selected_models/swellex96_s5_vla/rank_ablation/rank_ablation.csv
+results/selected_models/swellex96_s59_vla/selected_model_manifest.json
+results/selected_models/swellex96_s59_vla/selected_K00/run/sonarkad_model.pt
+results/selected_models/swellex96_s59_vla/selected_K00/run/components.pt
+results/selected_models/swellex96_s59_vla/selected_K00/run/results_cv.json
+results/selected_models/swellex96_s59_vla/rank_ablation/rank_ablation.csv
+results/figure_method_overview.png
+results/figure_swellex96_data_overview.png
+results/figure_swellex96_summary.png
+results/figure_swellex96_decomposition.png
+results/table_metrics_two_events.csv
+results/table_diagnostics_two_events.csv
+results/transfer_study/transfer_summary.json
+validation/**
+```
+
+Checkpoint files are PyTorch dictionaries produced by the GitHub code release. Logs, manifests, rank traces, transfer outputs, and figure records are included so that the selected-rank decisions can be audited without rerunning training.
+
+## Results reported by the SPL manuscript
+
+| Event | Selected rank | RMSE (dB) | Explained variance | Interaction fraction |
+|---|---:|---:|---:|---:|
+| SWellEx-96 S5 VLA | 1 | 3.138 +/- 0.368 | 0.776 +/- 0.025 | 0.095 +/- 0.047 |
+| SWellEx-96 S59 VLA | 0 | 3.409 +/- 0.244 | 0.653 +/- 0.063 | 0.000 +/- 0.000 |
+
+S5 is treated as the cleaner tow; S59 is interference-stressed. Under the same protocol, SonarKAD keeps a compact rank-one residual for S5 and rejects residual coupling for S59.
+
+## Data
+
+The experiments use SWellEx-96 vertical-line-array tonal data. Raw SWellEx-96 files are **not** redistributed in this model repository. The GitHub repository contains the preprocessing code and expects users to place the raw `.sio`, range, VLA-position, and CTD files under `data/` according to `configs/config.yaml`.
+
+Default preprocessing uses:
+
+- sampling rate: 1500 Hz;
+- 4096-point Hann STFT;
+- 2.7307 s window and 1.3653 s hop;
+- median pooling over 21 VLA channels after array-order correction;
+- high T-49-13 tones from 49 Hz to 388 Hz;
+- five blocked folds with 30 frames per block.
+
+## How to load a bundle
+
+Install from GitHub:
+
+```bash
+git clone https://github.com/soundai2016/SonarKAD.git
+cd SonarKAD
+python -m pip install -e .
+```
+
+Load the S5 selected model from this Hugging Face repository:
+
+```python
+import numpy as np
+from huggingface_hub import hf_hub_download
+from sonarkad import load_sonarkad_model_bundle, predict_rl
+
+bundle_path = hf_hub_download(
+    repo_id="soundai2016/SonarKAD",
+    filename="results/selected_models/swellex96_s5_vla/selected_K01/run/sonarkad_model.pt",
+    repo_type="model",
+)
+
+model, meta = load_sonarkad_model_bundle(bundle_path, device="auto")
+
+r_m = np.linspace(meta["normalization"]["r_min_m"], meta["normalization"]["r_max_m"], 100)
+f_hz = np.linspace(meta["normalization"]["f_min_hz"], meta["normalization"]["f_max_hz"], 64)
+rr, ff = np.meshgrid(r_m, f_hz, indexing="ij")
+rl_db = predict_rl(model, r_m=rr, f_hz=ff, normalization=meta["normalization"])
+print(rl_db.shape)
+```
+
+## Reproduce outputs
+
+From the GitHub checkout:
+
+```bash
+python -m pip install -e ".[tracking]"
+bash run_all.sh
+```
+
+For multi-GPU training:
+
+```bash
+DEVICE=cuda GPUS=0,1 FORCE=1 RANKS=0,1,2,4,8,16 bash run_all.sh
+```
+
+## Intended use
+
+SonarKAD is intended for research diagnostics on passive-sonar range--frequency fields. The output bundle supports:
+
+- reproducing manuscript figures and tables;
+- auditing blocked-CV rank selection;
+- inspecting additive range/frequency components and the retained residual interaction;
+- running inference inside the normalized range/frequency support saved in each bundle.
+
+## Limitations
+
+- The saved bundles are event-specific diagnostics, not a universal ocean-acoustics predictor.
+- Predictions outside the stored normalization range are clipped by the inference helper and should not be interpreted physically.
+- Rank selection depends on the configured mask, block partition, preprocessing, and validation rule.
+- SWellEx-96 raw data must be obtained from the original data source.

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+  "dataset": {
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+    "array": "VLA",
+    "sio_path": "data/J1341145.vla.21els.sio",
+    "range_table_path": "data/RangeEventS59",
+    "range_file_hint": "SproulToVLA.S59",
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+    "hidden_mlp": 64,
+    "device": "cuda",
+    "amp": true,
+    "progress_bar": true,
+    "compile": false,
+    "matmul_precision": "high",
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+    "pin_memory": true,
+    "early_stopping": {
+      "enabled": true,
+      "val_frac_frames": 0.1,
+      "patience": 120,
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results/rank_ablation_swellex96_s59_vla/K00/cv/fold_00/logs/training_config_snapshot.json

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results/rank_ablation_swellex96_s59_vla/K00/cv/fold_00/logs/training_history.csv

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