| ================================================================= |
| BASE TIER SOUP ANALYSIS |
| Device: cuda |
| ================================================================= |
|
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| Loading checkpoint... |
| Loaded: mAP=0.825 cv=0.3117 epoch=19 |
| clip_l14_openai loaded |
| dinov2_b14 loaded |
| siglip_b16_384 loaded |
|
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| Running inference on 5000 val images... |
| Done: fused=torch.Size([5000, 128]) tri=torch.Size([5000, 256]) |
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| ================================================================= |
| SCAN 1: ANCHOR GEOMETRY |
| ================================================================= |
| Anchor pairwise cosine: |
| mean=0.0356 std=0.1896 |
| max=0.9542 min=-0.9093 |
| Max neighbor cosine per anchor: |
| mean=0.6949 std=0.2730 |
| max=0.9542 min=0.0639 |
| Anchor norms: mean=1.000000 std=0.000000 |
| Anchor spectral: eff_rank=65.7/128 |
| sv_max=5.0231 sv_10=2.8655 sv_50=0.9697 sv_min=0.125017 |
| Anchor pentachoron CV: 0.2478 |
| mean_vol=0.074751 std_vol=0.018524 |
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| ================================================================= |
| SCAN 2: ANCHOR UTILIZATION |
| ================================================================= |
| Active anchors: 1/256 (0.4%) |
| Visit counts: mean=19.5 std=312.5 |
| max=5000 min=0 |
| top 10: [5000, 0, 0, 0, 0, 0, 0, 0, 0, 0] |
| bottom 10: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] |
| Anchor entropy: -0.0000 / 5.5452 (-0.0%) |
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| Per-anchor embedding density: |
| Intra-cluster cosine: mean=0.9693 std=0.0000 |
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| ================================================================= |
| SCAN 3: PROJECTOR ANALYSIS |
| ================================================================= |
|
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| clip_l14: |
| norm: mean=1.000000 (should be 1.0) |
| self-sim off-diag: 0.9668 |
| eff_dim: 24.1/128 |
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| dinov2_b14: |
| norm: mean=1.000000 (should be 1.0) |
| self-sim off-diag: 0.9678 |
| eff_dim: 25.3/128 |
|
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| siglip_b16: |
| norm: mean=1.000000 (should be 1.0) |
| self-sim off-diag: 0.9501 |
| eff_dim: 23.8/128 |
|
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| Expert agreement (cosine in 128-d): |
| clip_l14 × dinov2_b14 : mean=0.9898 std=0.0066 min=0.8730 |
| clip_l14 × siglip_b16 : mean=0.9893 std=0.0052 min=0.9307 |
| dinov2_b14 × siglip_b16 : mean=0.9855 std=0.0081 min=0.8920 |
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| Per-expert nearest anchor agreement: |
| clip_l14 × dinov2_b14 : same_anchor=1.0000 (100.0%) |
| clip_l14 × siglip_b16 : same_anchor=1.0000 (100.0%) |
| dinov2_b14 × siglip_b16 : same_anchor=1.0000 (100.0%) |
|
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| Projector weight comparison: |
| clip_l14 : norm=37.4114 eff_rank=30.5/128 |
| dinov2_b14 : norm=36.3149 eff_rank=23.0/128 |
| siglip_b16 : norm=39.2079 eff_rank=29.0/128 |
| clip_l14 × dinov2_b14 weight_cos=0.0046 |
| clip_l14 × siglip_b16 weight_cos=-0.0049 |
| dinov2_b14 × siglip_b16 weight_cos=-0.0055 |
|
|
| ================================================================= |
| SCAN 4: PATCHWORK COMPARTMENTS |
| ================================================================= |
| Comp 0: 32 anchors |
| Comp 1: 32 anchors |
| Comp 2: 32 anchors |
| Comp 3: 32 anchors |
| Comp 4: 32 anchors |
| Comp 5: 32 anchors |
| Comp 6: 32 anchors |
| Comp 7: 32 anchors |
|
|
| Patchwork output: torch.Size([5000, 512]) |
| norm: mean=11.6381 std=0.5046 |
| comp 0: norm=2.8604 std_across_dims=0.0010 |
| comp 1: norm=3.7652 std_across_dims=0.1596 |
| comp 2: norm=2.3303 std_across_dims=0.0057 |
| comp 3: norm=3.4802 std_across_dims=0.2053 |
| comp 4: norm=3.3465 std_across_dims=0.1143 |
| comp 5: norm=5.9651 std_across_dims=0.4720 |
| comp 6: norm=6.0775 std_across_dims=0.4946 |
| comp 7: norm=3.2188 std_across_dims=0.0718 |
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| ================================================================= |
| SCAN 5: TRIANGULATION PATTERNS |
| ================================================================= |
| Triangulation distances (1-cosine): |
| mean=0.8988 std=0.1301 |
| min=0.0038 max=1.2538 |
| Nearest anchor distance: |
| mean=0.0156 std=0.0042 |
| max=0.0419 min=0.0038 |
| Anchors within cos>0.5 per image: |
| mean=1.0 std=0.0 |
| Top-10 nearest anchor distances: |
| k=0: mean=0.0156 std=0.0042 |
| k=1: mean=0.6646 std=0.0218 |
| k=2: mean=0.6806 std=0.0185 |
| k=3: mean=0.6909 std=0.0173 |
| k=4: mean=0.6977 std=0.0167 |
| k=5: mean=0.7033 std=0.0162 |
| k=6: mean=0.7081 std=0.0158 |
| k=7: mean=0.7126 std=0.0154 |
| k=8: mean=0.7166 std=0.0150 |
| k=9: mean=0.7204 std=0.0147 |
|
|
| ================================================================= |
| SCAN 6: PER-CLASS ANCHOR AFFINITY |
| ================================================================= |
|
|
| Top-3 anchors per class (first 20 classes): |
| person (n=2693): 65(2693/2693) 1(0/2693) 0(0/2693) |
| bicycle (n= 149): 65(149/149) 1(0/149) 0(0/149) |
| car (n= 535): 65(535/535) 1(0/535) 0(0/535) |
| motorcycle (n= 159): 65(159/159) 1(0/159) 0(0/159) |
| airplane (n= 97): 65(97/97) 1(0/97) 0(0/97) |
| bus (n= 189): 65(189/189) 1(0/189) 0(0/189) |
| train (n= 157): 65(157/157) 1(0/157) 0(0/157) |
| truck (n= 250): 65(250/250) 1(0/250) 0(0/250) |
| boat (n= 121): 65(121/121) 1(0/121) 0(0/121) |
| traffic light (n= 191): 65(191/191) 1(0/191) 0(0/191) |
| fire hydrant (n= 86): 65(86/86) 1(0/86) 0(0/86) |
| stop sign (n= 69): 65(69/69) 1(0/69) 0(0/69) |
| parking meter (n= 37): 65(37/37) 1(0/37) 0(0/37) |
| bench (n= 235): 65(235/235) 1(0/235) 0(0/235) |
| bird (n= 125): 65(125/125) 1(0/125) 0(0/125) |
| cat (n= 184): 65(184/184) 1(0/184) 0(0/184) |
| dog (n= 177): 65(177/177) 1(0/177) 0(0/177) |
| horse (n= 128): 65(128/128) 1(0/128) 0(0/128) |
| sheep (n= 65): 65(65/65) 1(0/65) 0(0/65) |
| cow (n= 87): 65(87/87) 1(0/87) 0(0/87) |
|
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| Anchor specialization: |
| classes per anchor: mean=80.0 std=nan |
| max=80 min=80 |
|
|
| ================================================================= |
| SCAN 7: FUSED EMBEDDING GEOMETRY |
| ================================================================= |
| Norms: mean=1.000000 std=0.000000 |
| Self-sim (off-diag): 0.9693 |
| Effective dim: 23.6/128 |
| top-5 SVs explain 44.1% |
| top-10 SVs explain 70.2% |
| top-20 SVs explain 99.2% |
| top-50 SVs explain 100.0% |
| top-100 SVs explain 100.0% |
| Pentachoron CV: 0.3529 |
|
|
| ================================================================= |
| SCAN 8: EXPERT CONTRIBUTION |
| ================================================================= |
| clip_l14 : cos_to_fused mean=0.9970 std=0.0016 |
| dinov2_b14 : cos_to_fused mean=0.9957 std=0.0027 |
| siglip_b16 : cos_to_fused mean=0.9955 std=0.0023 |
| Without clip_l14 : cos_to_full=0.9992 (uniqueness=0.0008) |
| Without dinov2_b14 : cos_to_full=0.9989 (uniqueness=0.0011) |
| Without siglip_b16 : cos_to_full=0.9989 (uniqueness=0.0011) |
|
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| Per-image expert disagreement: |
| Agreement: mean=0.9882 std=0.0055 |
| Disagreement: mean=0.0041 std=0.0034 |
|
|
| Most agreed image (1449): agreement=0.9978 |
| labels: [22] |
| Most disagreed image (1435): agreement=0.9214 |
| labels: [28] |
|
|
| ================================================================= |
| ANALYSIS COMPLETE |
| ================================================================= |
| /tmp/ipykernel_10600/3734699858.py:410: UserWarning: std(): degrees of freedom is <= 0. Correction should be strictly less than the reduction factor (input numel divided by output numel). (Triggered internally at /pytorch/aten/src/ATen/native/ReduceOps.cpp:1857.) |
| f"std={anchor_class_count[anchor_class_count>0].std():.1f}") |
