loglens-nano — the benchmark models (128K–202K params)
Checkpoints from the LogLens study of fixed logarithmic memory schedules for multi-timescale change localization on fixed cameras.
Read this first: these are benchmark models — they prove what a from-scratch nano model can and cannot do. On the synthetic benchmarks they are strong (tables below). On real footage they hit a measured clutter wall (unlearnable below ~1% object-to-frame ratio against real backgrounds), and the deployable real-scene system is training-free: frozen CLIP + the same log schedule + a cosine-gap readout (0.796 on real gold, zero parameters). Code and findings in the repo.
Domain measured / deployment domain tested: measured on the TriClock / TriClock-HD synthetic benchmarks; deployment domain: no real long-form footage tested. (Fifth line of the card standard, added 2026-09-02: a number is only as good as the domain it was measured in.)
Files
| file | what | result |
|---|---|---|
log_K8_s0.pt | temporal LogLensNet, auto-base log schedule, K=8 (128K params) | 0.901 ± .017 TriClock (3 seeds) |
logtuned_K8_s0.pt | boundary-aligned schedule | 0.922 ± .009 |
log_K8_s0.onnx | ONNX export (543 KB) | 0.7 ms/query CPU-only on AGX Orin, ~3 KB memory state |
logzoom_K5_s0.pt | tri-axis HDNet, saliency-guided log-zoom (202K params) | 0.807 ± .012 TriClock-HD |
oraclebox_K5_s0.pt | tri-axis, oracle glimpses (ceiling reference) | 0.999 |
The two headline findings these models carry
- Schedule geometry beats capacity: same model, same budget — a log- spaced memory schedule scores 0.90 where a sliding window scores 0.48; optimal K is width-independent across a 16× parameter range.
- Coverage is information-sufficiency; guidance is learnability: full-coverage unguided tiling has every needed pixel (oracle ceiling 1.0) and learns nothing (0.25 floor); centered glimpses at the same resolution reach 0.991.
Demo: https://huggingface.co/spaces/resoajoe/loglens · Benchmark: https://huggingface.co/datasets/resoa/triclock
⚠ This card predates our model-card standard
It does not report: a baseline comparison, a measured failure rate, or what it refuses.
Every model we publish should state (1) the cheap baseline and the margin over it, (2) at least one named failure mode with a measured rate, (3) what the model refuses and why, and (4) label provenance. This card was written before that rule and has not been retrofitted, because the measurements needed no longer exist. Treat its numbers with more caution than our newer cards, and prefer a model that meets the standard where one exists.