Make Hugging Face release checkpoint-only; point code users to GitHub

README.md

@@ -4,56 +4,22 @@ library_name: pytorch
 tags:
 - video-anomaly-detection
 - weakly-supervised-learning
-- transfer-learning
+- checkpoints
 ---
-# PACT-VAD: controlled multi-target temporal transfer
 
-This repository distributes the **180 original validation-selected target checkpoints**
-for three datasets, six conditions (B, R, P, PW, PLR, PTSA), and ten seeds.
-The checkpoint manifest binds every file to its stored canonical SHA-256 and byte size.
-It is a separate release from the historical five-seed `mtastan/PATT-Net` repository.
+# PACT-VAD checkpoints
 
-Selection uses the earliest epoch attaining maximum validation video AUC. Primary
-test metrics are frame ROC-AUC for UCF-Crime and ShanghaiTech and frame AP for
-XD-Violence. The stored UCF evaluator covers **1,109,504 frames**, not 1,109,888.
-The evidence JSON supplies metrics and selected epochs; no new training was performed
-to produce this release. Original PyTorch serialization is preserved to retain hashes.
+This repository distributes the validation-selected PACT-VAD model weights used
+in the accompanying controlled transfer study.
 
-## Interpretation
+- Target checkpoints: 3 datasets × 6 conditions × 10 seeds = 180 files
+- Source checkpoint: the fixed PreVAD temporal backbone used by the transfer runs
+- Selection rule: earliest epoch attaining maximum validation video AUC
 
-This is a controlled exploratory transfer study, not a SOTA or universal-benefit claim.
-Only ShanghaiTech PW minus P survives the declared five-contrast within-dataset Holm
-adjustment: +3.435 AUC points, 95% paired-bootstrap interval [2.449,4.477], adjusted
-p=0.0098, positive in all ten seeds. Negative and uncertain conditions are retained.
-Historical test-epoch maxima are not primary results. Test labels do not select these
-checkpoints or enter the training objective.
+Source code, configurations, the canonical metric ledger, and manuscript sources:
 
-## Use and limits
+https://github.com/tastangh/PACT-VAD
 
-Model/evaluation code and split contracts are supplied in `code/pact_vad_code.zip`
-with a per-file manifest. Architecture uses frozen CLIP
-ViT-L/14 768-dimensional descriptors and a temporal receiver. Loading weights alone
-does not reproduce features, frame alignment, or metrics. Historical feature archives
-and raw frame-score vectors are not part of this release. Ground-truth arrays must be
-retrieved from their original distributors under applicable terms.
-
-**Compact checkpoints:** 90 P/PW/PTSA files deliberately omit the frozen source.
-Retrieve `source/prevad_temporal_e02.pth` from this release and use the strict loader
-in `src/released_checkpoint.py`; `code/README.md` provides an example. All 180 files
-passed strict CPU state loading and a synthetic forward pass. This is not dataset
-metric reevaluation.
-
-Use `huggingface_hub.hf_hub_download` with the manifest path and pin the repository
-revision. Check file SHA-256 against `checkpoint_manifest.json` before loading.
-Load only trusted weights on CPU using PyTorch's `weights_only=True` option where
-supported; instantiate the condition-specific architecture from the project source.
-The release is research material, not a validated operational surveillance system.
-
-## Contributions
-
-Hamza Osman İlhan (Associate Professor, PhD): Conceptualization, Methodology,
-Supervision; technical guidance and architectural ideas.
-Mehmet Taştan (master's student): Software, Validation; implementation and testing.
-These contribution roles were provided by the project author. Manuscript funding,
-competing interests, ethics, final author approval and publication readiness remain
-separate declarations and are not certified by this checkpoint release.
+The checkpoint manifest records the dataset, condition, seed, selected epoch,
+file size, and SHA-256 digest for every target checkpoint. Dataset videos and
+historical feature arrays are not distributed here.

code/README.md

@@ -1,58 +0,0 @@
-# PACT-VAD reproducibility code snapshot
-
-This is a file-hash-bound snapshot, not a claim of bit-identical historical training.
-It contains model/evaluator source, configurations, splits, statistical analysis and
-CPU tests. Raw videos, feature arrays and third-party annotation datasets are excluded.
-Checkpoint metrics remain the stored observations, not a new evaluation.
-
-## CPU checks
-
-```powershell
-python -m venv .venv
-.venv\Scripts\python -m pip install -r requirements-dev.txt
-.venv\Scripts\python -m pytest -q
-.venv\Scripts\python tools/analyze_pact_10seed.py
-.venv\Scripts\python tools/fetch_paa_ground_truth.py
-```
-
-The legacy checkpoint manifest is included for its contract test; absent legacy
-weights are skipped. Tests requiring undistributed PreVAD annotations and the obsolete
-five-seed manuscript are excluded from this snapshot. Canonical weights are described
-by the Hub's root `checkpoint_manifest.json`.
-GT downloader pins author-hosted arrays and verifies historical hashes; no upstream
-redistribution license is implied. Feature extraction/training are optional expensive
-operations and were not run when preparing this release.
-
-## Load canonical checkpoint files
-
-Ninety P/PW/PTSA files omit the frozen source to save space. They require the exact
-`source/prevad_temporal_e02.pth` distributed in the same model repository. Ordinary
-strict loading of the compact state alone fails; this is not corruption.
-
-```python
-import json, sys
-from huggingface_hub import hf_hub_download
-sys.path.insert(0, 'src')
-from released_checkpoint import load_released_checkpoint
-repo = 'mtastan/PACT-VAD'
-revision = '<PINNED_RELEASE_COMMIT>'
-manifest = json.load(open(hf_hub_download(repo, 'checkpoint_manifest.json', revision=revision)))
-entry = next(r for r in manifest['checkpoints']
-             if r['dataset'] == 'ShanghaiTech' and r['condition'] == 'PW' and r['seed'] == 1)
-target = hf_hub_download(repo, entry['path_in_repo'], revision=revision)
-source = hf_hub_download(repo, 'source/prevad_temporal_e02.pth', revision=revision)
-cfg = json.load(open('configs/prevad_adaptation_shanghaitech_10seed_v1.json'))['model']
-model, metadata = load_released_checkpoint(target, cfg, entry['condition'],
-    entry['checkpoint_sha256'], source,
-    'f3c3ecd73a046beab6bdf4fcff6c2850c0428afa3361236d20ed21ea91d9473a')
-```
-
-The loader checks hashes before safe CPU deserialization, reconstructs only the
-explicitly omitted source, and enforces exact model keys/shapes. It never reads test
-labels. All 180 original checkpoint states passed strict loading and a four-snippet
-synthetic CPU forward; that check does not recompute dataset AUC/AP.
-
-`evaluate.py` and `evaluate_multitarget.py` retain the historical B/R/P run-directory
-interfaces. They are not six-condition Hub download runners. Use the strict loader
-for PW/PLR/PTSA, preserve the exact dataset manifests and evaluator contracts, and do
-not infer metric reproducibility without the historical features.

code/manifest.json

@@ -1,292 +0,0 @@
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evidence/checkpoint_cpu_audit.json

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-{
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evidence/statistical_analysis.json

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-{
-  "schema_version": "1.0",
-  "paper_id": "PACT-VAD-PAA-2026",
-  "source_ledger": "results/evaluations/pact_10seed_selected_runs.json",
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-  "analysis_class": "exploratory paired-seed analysis",
-  "primary_metrics": {
-    "UCF-Crime": "frame_auc",
-    "ShanghaiTech": "frame_auc",
-    "XD-Violence": "frame_ap"
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-    "UCF-Crime": 1109504,
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-  "inference": {
-    "test": "exact two-sided paired sign-flip permutation",
-    "interval": "paired-seed percentile bootstrap with 100,000 resamples",
-    "multiplicity": "Holm adjustment across five contrasts within each dataset",
-    "effect_size": "paired Cohen dz"
-  },
-  "summaries": [
-    {
-      "dataset": "UCF-Crime",
-      "condition": "B",
-      "primary_metric": "frame_auc",
-      "n": 10,
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-      "exploratory_reject_0_05_after_holm": false
-    },
-    {
-      "dataset": "XD-Violence",
-      "primary_metric": "frame_ap",
-      "contrast": "PTSA-P",
-      "n": 10,
-      "mean_difference_pp": 0.9177605325136806,
-      "sample_std_difference_pp": 5.785539205278465,
-      "paired_cohen_dz": 0.15863007750018482,
-      "bootstrap_percentile_95_ci_pp": [
-        -2.3597143500990683,
-        4.400735321882423
-      ],
-      "exact_two_sided_sign_flip_p": 0.6171875,
-      "positive_seeds": 5,
-      "zero_seeds": 0,
-      "paired_differences_pp": [
-        1.5893649854901604,
-        2.973767489094925,
-        9.894136036583179,
-        -5.771213072979265,
-        -1.5655987664038085,
-        -0.5537027174259967,
-        4.0724211073075,
-        -5.973926632876814,
-        9.393896394596934,
-        -4.881539498250009
-      ],
-      "holm_adjusted_p_within_dataset": 0.6875,
-      "exploratory_reject_0_05_after_holm": false
-    }
-  ],
-  "limitations": [
-    "Contrasts and multiplicity families were specified after the experiments and are exploratory.",
-    "Ten paired seeds quantify optimizer/initialization variability, not dataset sampling uncertainty.",
-    "Seed pairing does not make results independent replications.",
-    "Stored metrics were audited but not recomputed from raw frame-score vectors in this package.",
-    "UCF-Crime uses a 1,109,504-frame evaluator variant; comparisons require the same ground-truth contract."
-  ]
-}