| --- |
| license: apache-2.0 |
| tags: |
| - benchmark |
| - video-understanding |
| - edge |
| - change-detection |
| - tiny-models |
| pretty_name: TriClock / LogLens |
| --- |
| |
| # LogLens · TriClock |
|
|
| *Live demo: https://huggingface.co/spaces/resoajoe/loglens · Code: |
| https://github.com/resoajoe/loglens* |
|
|
| **One question:** if a camera never moves, how little memory does a model need |
| to answer *"when did that change?"* at every timescale — a frame ago, a minute |
| ago, all day ago? |
|
|
| **One answer:** K = O(log T) frame embeddings at logarithmically-spaced ages. |
| A 128K-parameter model with **8 memory slots** localizes changes across a |
| 1,024-frame horizon at 0.90–0.92 mean accuracy (3 seeds), where a sliding |
| window scores 0.48 (blind past 8 frames) and uniform sampling scores 0.67 |
| (blind to the recent past). With **4 slots** it beats hand-crafted schedules |
| using 8 and matches them at 12. The schedule's base is a *deploy-time knob*: |
| retune it coarser on frozen weights and keep 99–101% of accuracy. And more |
| memory isn't better — K=12 loses to K=8; coverage, not capacity, is what to |
| buy. |
|
|
| ## TriClock |
|
|
| A procedural benchmark isolating the memory schedule as the only variable. |
| Three objects per scene each change at most once, at an age drawn log-uniform |
| over [1, 1024]; the task is classifying WHEN (none/fast/med/slow). The current |
| frame is provably uninformative — colors, positions, and object existence are |
| randomized so only *comparison against a correctly-aged memory* answers the |
| question. Frames render functionally from a seed, so the dataset is infinite, |
| weightless, and exactly reproducible. |
|
|
| ```python |
| from triclock.generate import Episode |
| ep = Episode(seed=42) |
| frame_now, frame_100_ago = ep.frame(0), ep.frame(100) |
| ep.labels # [light, agent, furniture] -> {0: none, 1: fast, 2: med, 3: slow} |
| ``` |
|
|
| ## Headline table (K=8 slots, identical model & training) |
|
|
| | schedule | fast | med | slow | mean ± std (3 seeds) | |
| |---|---|---|---|---| |
| | log (boundary-tuned) | .91 | .90 | .91 | **.92 ± .01** | |
| | log (auto base) | .86 | .90 | .86 | .90 ± .02 | |
| | log (wrong boundaries — control) | .85 | .83 | .74 | .85 ± .00 | |
| | hybrid window+stride | .65 | .82 | .61 | .76 ± .01 | |
| | uniform stride | .06 | .74 | .92 | .67 ± .01 | |
| | sliding window | .92 | .01 | .00 | .48 ± .00 | |
|
|
| Train any row yourself in ~2.5 minutes on an M-series MacBook: |
|
|
| ```bash |
| python3 -m triclock.train --policy log --steps 6000 |
| ``` |
|
|
| ## Try the demo (Space) |
|
|
| Move the budget slider and watch the sliding window go blind to the past and |
| the stride go blind to the present, while the log schedule keeps both — the |
| whole idea in one interaction. |
|
|
| ## Usage note: the sampling is load-bearing (learned the hard way) |
|
|
| TriClock draws change ages **log-uniformly** over [1, T]. This is not a |
| detail. When we ported the task to real footage and sampled query times |
| uniformly instead, the label distribution silently collapsed to 87-100% |
| "slow" and models fit that prior perfectly while learning nothing (train |
| loss 0.002, gold accuracy 0.25). If you adapt this benchmark to your own |
| footage: port the age-sampling discipline, not just the task. The repo's |
| `realdata/` pipeline has the corrected anchored sampler. |
|
|
| ## Status & roadmap |
|
|
| Temporal results: 3 seeds, 20K steps, synthetic scenes. Spatial axis: a fixed |
| log-*zoom* glimpse wrapper on SmolVLM-256M was inconclusive on V*Bench (both |
| arms at chance — the backbone, not the schedule, is the bottleneck at 256M); |
| spatial validation moves to TriClock-HD, a trained-from-scratch setting like |
| the temporal one. Next: real fixed-camera streams and Jetson Nano deployment |
| (target: full-day change localization in ~3 KB of memory state at ~5 W). |
| Draft writeup in `paper/DRAFT.md`. |
| |
