{
  "test_id": "3.3",
  "ticket": "SAIL-167",
  "name": "gate0-dinov3-probe",
  "generated_at": "2026-09-07T14:10:59.002824+00:00",
  "status": "blocked_backbone_access_cpu_data_audit_completed",
  "product_question": "Can DINOv3 find the faint draft stripes SailScan misses?",
  "verdict": "NO-GO on building a grouping head yet; DINOv3 capability remains untested.",
  "bottom_line": "No measured improvement to a sailor's scan. The backbone could not be downloaded, so stripe recovery and accuracy improvement are unknown, not zero. Obtain authorized weights and run the high-resolution probe before investing in a grouping head.",
  "what_it_means_for_sailscan": "This test has not shown that SailScan would find one more faint stripe or give a more accurate sail reading. A clean set of 200 human-retraced photos and 40 separate validation photos is ready for the probe. The next useful spend is the small probe after weight access is granted; the full stripe-finder has no supporting evidence yet.",
  "execution": {
    "mode": "reduced_cpu_analysis_only",
    "training_photos_used": 0,
    "backbone_forward_passes": 0,
    "probe_optimizer_steps": 0,
    "activation_maps_scored": 0,
    "gpu_hours": 0,
    "pods_created": 0,
    "model_substitution": false,
    "weights_promoted": false,
    "deployed": false,
    "committed": false
  },
  "access": {
    "evidence_note": "HTTP checks performed by the experiment session on 2026-09-07; this CPU audit script does not repeat network checks.",
    "hf_auth_check": {
      "endpoint": "https://huggingface.co/api/whoami-v2",
      "http_status": 200
    },
    "model_requests": [
      {
        "model": "facebook/dinov3-vits16-pretrain-lvd1689m",
        "config_http_status": 403,
        "weights_head_http_status": 403
      },
      {
        "model": "facebook/dinov3-vitb16-pretrain-lvd1689m",
        "config_http_status": 403
      }
    ],
    "reason": "The configured Hugging Face account is not in the authorized list for these gated repositories.",
    "cache_search": "No DINOv3 files in local Hugging Face/torch caches or inference/models.",
    "runpod_api_http_status": 200,
    "runpod_note": "Initial Python client received 403/1010; standard client User-Agent returned 200. Cloud API access is available. No experiment GPU was allocated because weights are inaccessible. Existing service pods were untouched; new GPU capacity was not tested."
  },
  "holdout": {
    "file": "server/inference/test-holdout-ids-canonical.json",
    "freeze_date": "2026-08-25",
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    "sha256": "a49d9139d6eac54951c4c3e257770855192a75f8c941799ef4141c49a4c11243",
    "selected_overlap": 0,
    "selected_byte_hash_overlap": 0,
    "holdout_source_records_hashed": 364,
    "missing_holdout_image_records": [],
    "identity_rule": "Strip north__ prefix, exclude canonical photo IDs, then exclude source-byte SHA256 duplicates. No sailId split. Re-encoded duplicates are not detected by the byte hash."
  },
  "data_audit": {
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    "patch_stride": 16,
    "mask_width_pixels": 5,
    "mask_note": "Rasterized human polylines, not measured paint width or DINOv3 predictions. Padding is excluded from valid pixel denominator.",
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  },
  "model_results": {
    "pixel_average_precision": null,
    "centerline_recall": null,
    "faint_stripe_recovery": null,
    "false_positive_change": null,
    "scan_accuracy_improvement": null,
    "latency_ms": null
  },
  "not_run": [
    "DINOv3 weight download",
    "1024px+ DINOv3 feature extraction",
    "Frozen-backbone linear-probe training",
    "Activation map inspection on confirmed faint-stripe misses",
    "DINOv3 holdout scoring",
    "Product metric comparison",
    "RunPod experiment GPU allocation"
  ],
  "next_run_protocol": {
    "backbone": "facebook/dinov3-vitb16-pretrain-lvd1689m; pin resolved model revision and file SHA256 after authorized download",
    "device": "Dedicated RunPod CUDA pod; never train locally",
    "environment": "Per-run venv; ultralytics==8.4.92 per requirements-training.txt; freeze all installed versions. Ultralytics does not implement this probe.",
    "packaging": "Dereference symlinks with tar -h or copy2(follow_symlinks=True); verify archive members are regular files and compare image checksums.",
    "guard": "Require --holdout-ids test-holdout-ids-canonical.json at every training entry point; assert disjoint canonical IDs and image hashes before optimization.",
    "preprocess": "ImageOps.exif_transpose, aspect-preserving short-edge 1024, pad to 16; bypass default low-resolution processor resize/crop; exclude padding from loss/scoring.",
    "probe": "Freeze backbone, eval mode, no_grad feature extraction. Train only one 1x1 linear classifier on spatial patch features; bilinear upsample logits to pixel mask; no nonlinear grouping or coordinate inputs. Use weighted BCE; fit weights on training split only.",
    "validation": "Select threshold on 40 nonholdout validation photos only. Report pixel AP, centerline recall at matched false-positive rate, and per-photo bootstrap intervals. Score confirmed faint misses separately from other misses and battens/rigging negatives.",
    "decision": "Build grouping head only if independent faint-miss recovery improves at matched false-positive rate. Activation quality alone does not establish better counts, twist, entry or exit. A failed small-backbone probe cannot reject all DINOv3 variants."
  },
  "sources": [
    "https://github.com/facebookresearch/dinov3",
    "https://huggingface.co/facebook/dinov3-vitb16-pretrain-lvd1689m",
    "https://huggingface.co/docs/transformers/model_doc/dinov3",
    "https://github.com/facebookresearch/dinov3/blob/main/LICENSE.md"
  ]
}