Document tested installation and full six-metric validation

README.md

@@ -24,10 +24,13 @@ The dataset is `data/test.zip` in ImageFolder format. `metadata.jsonl` also expo
 
 ## Install the evaluation environment
 
-Download and unpack `evaluation.zip`. On Linux with an NVIDIA GPU, a compatible driver, `python3`, `git`, `curl`, `ffmpeg`, and `ffprobe`, run:
+On Linux with an NVIDIA GPU, a compatible driver, `python3`, `git`, `curl`, `unzip`, `ffmpeg`, and `ffprobe`, run:
 
 ```bash
-R=/absolute/path/to/helixbench_runtime
+mkdir -p helixbench && cd helixbench
+curl -fL --retry 3 -o evaluation.zip https://huggingface.co/datasets/NoizAI/Helixbench/resolve/main/evaluation.zip
+unzip -q evaluation.zip
+R="$PWD/runtime"
 bash evaluation/install.sh "$R"
 "$R/env/bin/python" evaluation/download_weights.py "$R"
 "$R/env/bin/helixbench" verify-package
@@ -48,4 +51,4 @@ Provide one MP4 for every sample ID, at 768×512, 121 frames/24 fps, stereo 48 k
 
 The included GT features are verified by SHA256, so GT videos are not needed for KL/FAD. `--gt` remains available if you have the exact GT media and want to extract those features again. The entrypoint produces exactly six metrics; it does not combine them into a single score.
 
-`data/test/metadata.jsonl` and all 1,015 PNG files are SHA-checked against the frozen release manifest before upload. The package's `verify-package` checks the frozen cohort, Spatial vocabulary and scoring engine hashes. Evaluation code is based on the private GitHub Helixbench repository at commit `1f9b199`, with an AV-only data adaptation and packaged GT features. It has not been checked against every historical model end to end; the original full1015 pipeline was previously validated on an A10G with historical AudioX outputs.
+`data/test/metadata.jsonl` and all 1,015 PNG files are SHA-checked against the frozen release manifest before upload. The package's `verify-package` checks the frozen cohort, Spatial vocabulary and scoring engine hashes. Evaluation code is based on the private GitHub Helixbench repository at commit `1f9b199`, with an AV-only data adaptation and packaged GT features. A fresh install on a single NVIDIA A10G completed all six metrics for 1,015 historical AudioX videos, followed by `aggregate` and `verify`. Compared with the prior A10G run of the same videos, KL, FAD, DeSync, IB, and CLAP matched exactly. Spatial differed by 0.0589 points because two of the 150 clips had different frame-level detector/track results; the category counts and all-150 denominator matched. Other historical models have not been rerun from this public package.

RELEASE_FILES.json

@@ -1,15 +1,15 @@
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   "README.md": {
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-    "sha256": "3ec3876373f75f259132429595faa84e494a346bbbabbb28e0915bf4c7947d28"
+    "bytes": 4406,
+    "sha256": "a0fc046023317fda7748372d79efde6741d882e53e9ad17f6546634a69268fcb"
   },
   "data/test.zip": {
     "bytes": 568170324,
     "sha256": "c6a48f1535d36f45a01cce2cb1e6eb4117319aa984c80b067ba1e89497c7d38c"
   },
   "evaluation.zip": {
-    "bytes": 6431892,
-    "sha256": "0d6373079f273ee786244d3799f8ac9cdb0cea33a74f3996ec3ab198fff6d90a"
+    "bytes": 6711168,
+    "sha256": "ec776bb1ff33126f821ce6dc02c77a23c87027ceae8cda1a3b92438ce2a44007"
   },
   "evaluation/download_weights.py": {
     "bytes": 3708,
@@ -24,8 +24,8 @@
     "sha256": "6bb4f7b39e9236cea462cfaed2b0399499ede5869e09e4ca695c98182b1bdac1"
   },
   "evaluation/provenance/DEPENDENCIES.md": {
-    "bytes": 1765,
-    "sha256": "14ba27d13793f1706efb34be949df5dc6f476a4e43b81a33b7461c492edf5117"
+    "bytes": 2176,
+    "sha256": "3f07a14222fab3f08eb87205fe3edcb7111edeab4c7f3b14fd2824f377af6336"
   },
   "evaluation/pyproject.toml": {
     "bytes": 489,

evaluation.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0d6373079f273ee786244d3799f8ac9cdb0cea33a74f3996ec3ab198fff6d90a
-size 6431892
+oid sha256:ec776bb1ff33126f821ce6dc02c77a23c87027ceae8cda1a3b92438ce2a44007
+size 6711168

evaluation/provenance/DEPENDENCIES.md

@@ -21,5 +21,10 @@ The packaged GT PaSST and VGGish features were extracted with these models.
 PaSST and VGGish run on CPU FP32. CLAP, ImageBind, Synchformer, and
 Grounding DINO require a compatible NVIDIA GPU. The installation script pins
 the Python and major runtime versions used in the historical A10G validation.
-The public package has not yet passed a complete fresh-environment six-metric
-run; installation and score validation are separate checks.
+The public package completed a fresh A10G install and six-metric run on 1,015
+historical AudioX videos on 2026-10-12. The first five aggregate metrics matched
+the previous A10G run exactly. Spatial differed by 0.0589 points across two of
+150 clips despite identical input videos and extracted frames; small detector
+output changes affected the selected motion tracks. The Spatial rule and all-150
+denominator were unchanged. The frozen `environment.freeze.txt` from the test
+runtime can be used to inspect the installed transitive package versions.