02:36 · repliedNetwork tracking shows no media request at all. I'll test both sources from inside the page: our own local route and the ams-1080 URL, each on a fresh video element with a timeout.
02:38 · repliedReloaded. Now selecting the glossy-dash clip and taking a screenshot to check the overlays at frame zero.
02:39 · repliedThe viewer is up at http://127.0.0.1:41421 and serving the ten gated and tracked clips. Open it in your normal browser window. Everything is staged in git, not committed.
**What it is.** Our own FastAPI server in the cv project, reading our own results only. A SQLite index under `work/` lists clips and per-stage summaries, and the stage JSON files stay the record. Each stage now registers its output when it writes, and one command rebuilds the index from the files.
**Video.** Not stored by us. The page's player points at the ams-1080 server on port 41080, which streams the clips with Range support. A config switch falls back to serving the local videos directory from our server if that one is down.
**Port.** 41421, set in `config.toml`. It sits next to the ams-1080 port and clear of the usual 3000, 5000, 8000 and 8080 neighbourhood.
**What you can view.**
- Clip list with mask coverage, kept detections and track counts.
- The video with four toggleable overlays: ego mask, gate kept boxes, gate dropped boxes with their reason, and track IDs coloured by ID, plus optional trails.
- An ID timeline under the video. Click a bar to seek and isolate that ID. The right panel lists tracks with span and length.
- Keyboard: space plays, arrows step one fed frame, j and k change clip, m g i t toggle overlays, esc clears the selection.
**One thing I could not verify.** Playback itself. The Claude-in-Chrome automation tab refuses to load any media, including Chrome's own player on the raw ams-1080 URL, so I checked the overlays at frame zero on a black stage and left playback for your browser. If the video stays black for you too, tell me and I will look at the ams-1080 Range response with Chrome's actual request headers.
**Start and stop.**
```
uv run python -m cv.server …
03:13 · askedWhat’s an offline tracklet linking?
03:14 · repliedOffline tracklet linking is the second pass that turns the tracker's fragments into vehicles, using the fact that we have the whole clip.
**The problem it solves.** BoT-SORT runs forward in time and decides each frame with only the past. When a vehicle is occluded for longer than the lost-track buffer, leaves the frame briefly, or the camera jolts at contact, the tracker gives up on the old ID and starts a new one when the vehicle reappears. The result is what the run report showed: a median of 17.5 tracks per clip against roughly 8 real vehicles, with half the tracks under a second. Each fragment is pure, meaning one vehicle, but one vehicle is spread over several IDs.
**What the pass does.** It treats every tracker output as a tracklet, a short pure segment, and asks for each pair whether they are the same vehicle. Then it merges the pairs that pass into one vehicle ID that spans the clip.
**Why offline makes it stronger than the tracker.** Three things a real-time tracker cannot do:
- **Look backward.** Extrapolate tracklet B backwards from its start and tracklet A forwards from its end, and check whether they meet in space and time. The tracker only extrapolates forward.
- **Use hard negatives.** Two tracklets that overlap in time cannot be the same vehicle. That rules out most candidate pairs for free.
- **Solve globally.** Instead of greedy frame-by-frame decisions, build a cost for every allowed pair, motion agreement plus time gap plus appearance similarity, and solve one assignment over the whole clip. Each tracklet gets at most one predecessor and one successor, so it is a bipartite matching between tracklet ends and tracklet starts, with a fixed cost for "no link".
**What comes out.** A vehicles file with one stable ID per physical vehicle, its full span…
03:19 · askedI thought reid and bot-sort was exactly made to solve those problem, but i guess it’s not enough. Let’s try it
03:19 · repliedBoT-SORT with ReID does attack the same problem, but only causally: it can re-attach a vehicle whose track is still alive in its buffer, and the appearance term is consulted only for those candidates. Once the buffer expires or the tracker never linked two fragments, nothing later revisits the decision. The linking pass revisits every pair with the whole clip in hand. Building it now as stage 3, with its own config section, a report, and a viewer overlay.
03:21 · repliedNumPy 2 dropped the `ptp` method. Fixing and rerunning the smoke test.
03:23 · repliedThe baseline run is done. Capturing its numbers, then rerunning with the stationary fallback.
03:29 · repliedStage 3 is built, run, reported, and wired into the viewer. Your instinct was mostly right, and the report says so with evidence.
**What the linking pass found.** On this subset, BoT-SORT with ReID already handles nearly all sequential fragments. The linker found only seven pairs worth joining across 196 tracklets, and the gallery confirms all seven are the same vehicle. The dense-traffic clip's timeline shows why the numbers looked worse than they are: most of its tracklets overlap in time, so they are different vehicles or duplicate boxes, not fragments of one vehicle. The remaining short tracks are one- or two-frame flickers at the horizon with nothing after them. They are noise to flag, not fragments to link.
**The one test that decided the defaults.** A looser variant made 26 links and produced prettier counts, but the gallery showed about six wrong links, including the tanker joined to a blue sedan. A wrong link merges two vehicles into one other party, which is worse than a missed link, so the conservative defaults stay. Both galleries are in the report.
| linking | links | vehicles | wrong links |
| - | - | - | - |
| default | 7 | 189 | 0 of 7 |
| looser variant | 26 | 170 | about 6 of 26 |
**Where the linker still earns its place.** The tanker on the 720p clip vanishes from the detections for 1.2 s while doubling in size, and no motion model can bridge that safely. That is a detector miss to chase in the gate stage, not a linking problem. The linker remains the safety net for the contact jolt and for longer occlusions, with its ID assignment from content so a re-run gives the same IDs.
**What landed.**
- The stage with its own config section, a vehicles file per clip with tracklet lists, link costs, interpolated gaps and a fingerprint per vehicle.
- A trac…
03:51 · askedi tried playing the video on localhost:41421 but it flickers. any idea why?
03:51 · repliedYes. It is the overlay canvas, not the video. On every animation frame the draw routine calls the fit function first, and that function assigns the canvas width and height. Assigning a canvas size clears it, even to the same value. The routine then returns early because the fed frame has not changed, so nothing is redrawn. At 60 animation frames per second against 7.5 fed frames per second, the boxes are visible for roughly one frame in eight. Fixing it so the canvas is only resized when its size actually changes, and checking the frame before touching the canvas.
03:51 · repliedFixed. The server already serves the corrected page, so a reload of the viewer is enough. No restart needed.
**Cause.** The draw loop ran on every animation frame and began by resizing the overlay canvas to fit the video. Setting a canvas's width or height wipes it, even when the value is unchanged. Because the fed frame only advances 7.5 times a second, most of the 60 draw calls per second cleared the boxes and then returned without redrawing them. The boxes were visible about one frame in eight, which reads as flicker.
**Fix.** The canvas is now resized only when its size actually changes, and the frame check happens before the canvas is touched. Only the timeline cursor is repainted between fed frames.
If the picture itself flickers rather than the boxes, that would be a different problem in the video stream from the ams-1080 server, and I would look at that next. Tell me which one you see after the reload.
03:53 · askedwhat's the difference between tracklets and vehicles?
03:53 · repliedA tracklet is what the tracker gives us. A vehicle is what we decide a tracklet, or a chain of them, actually is.
**Tracklet.** One run of the tracker's output under one ID, from the frame it first matched a detection to the last frame before it gave up. It is pure by construction, meaning one physical object, but it is not complete. If the vehicle is hidden longer than the lost-track buffer, leaves the picture, or the camera jolts at contact, the tracker starts a fresh ID when the vehicle reappears. So one car can be several tracklets, and a one-frame flicker at the horizon is also a tracklet. The numbers in the side panel are tracker numbers, assigned in creation order, with gaps where an ID never got confirmed.
**Vehicle.** The linking stage's output. It takes all tracklets of a clip, decides which pairs are the same physical vehicle using motion, appearance and timing, and chains them. A vehicle therefore has:
- one or more tracklets, listed in order
- interpolated boxes filling the gaps between them, drawn dashed in the viewer and marked with a tilde
- a majority class, a first and last time, and a fingerprint of a few timed boxes
- an ID assigned from content, first appearance time then position, so a re-run over the same tracks gives the same numbers, which tracker IDs do not
In the viewer the tracklet row reads "V9 · 2 trk · ~1", meaning vehicle 9 is two tracklets with one interpolated frame between them. A row reading "1 trk" is a tracklet promoted unchanged.
On this subset the two lists look almost the same because the tracker already left few sequential fragments, and the linker joined only seven pairs. The distinction matters downstream all the same. Later stages, the VLM, lanes and kinematics, should consume vehicles and never tracklets, because a vehi…
03:57 · askedis seeing two boxes (one thin green outline, and one bold) a problem? and shouldn't selecting a vehicle or track only show the selected overlay?
03:57 · repliedNot a bug, but a poor default. The thin green outline is the gate layer, the kept detection, and the bold coloured box is the track or vehicle layer drawn on the same box. With all layers on, one car can carry three coincident rectangles. Two changes: quieter defaults, mask plus dropped detections plus vehicle IDs, with tracklet IDs shown only when a clip has no linking output, and a selection that hides everything except the chosen vehicle or tracklet.
03:58 · repliedBoth changes are live on the running server. One quick check in the browser for script errors before handing it back.
03:58 · repliedBoth changes are live. Reload the viewer to get them.
**The two boxes.** They were two layers on one car: the thin green outline is the gate layer, a kept detection, and the bold coloured box is the track or vehicle layer drawn on the same coordinates. With everything on, a vehicle could carry three coincident rectangles. Not wrong, but noisy, so the defaults changed.
- **Default layers now:** ego mask, dropped detections, and vehicle IDs. Kept detections and tracklet IDs are off, and a clip with no linking output falls back to tracklet IDs automatically. Every layer stays a toggle.
**Selection now isolates.** Clicking a vehicle or a tracklet, in the panel or the timeline, hides every other box on every layer and draws only that one, with its trail if trails are on. The mask stays. In the timeline, the tracklets that belong to a selected vehicle stay bright and the rest dim. Selecting a vehicle clears a tracklet selection and the reverse. Escape shows everything again.
I checked in the browser that the defaults load as described, that selecting and clearing behave, and that the page reports no script errors.
04:01 · askedi see bb_1_000211_vehicle_30_421 V25 becomes V17. this is problematic because V25 is the other party
04:01 · repliedLet me look at exactly what happens between those two vehicles in that clip: their tracklets, spans, and boxes around the switch.
04:06 · repliedThe sedan itself leaves the frame at the right edge about 0.6 s after impact, so no tracker could hold it longer. The subset-wide comparison at the stricter threshold is running. I'll report when it lands.
04:08 · asked<task-notification>
<task-id>b319mg5yy</task-id>
<tool-use-id>toolu_01GsicdasjuKtsocNxYejjQX</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-cv-processing/03132d5f-89fa-4eb6-b2b4-fcdc5794d2ba/tasks/b319mg5yy.output</output-file>
<status>completed</status>
<summary>Background command "Track and link the whole subset at match_thresh 0.6 and compare fragmentation with the baseline" completed (exit code 0)</summary>
</task-notification>
04:13 · repliedGood catch, and it was a real identity switch, not a display issue. It is fixed for this case and the report is updated. Reload the viewer to see the clip with the new numbering.
**What happened.** Your other party, the dark sedan, was tracklet 48 from 3.2 s until the grey van beside it covered it at 4.27 s. The van's tracklet 17 then reached the right picture edge and shrank as it left. At 5.20 s, right after impact at 5.125 s, the sedan reappeared in the same spot and the tracker gave it the van's ID. The crops confirm it, and the labelled B box at 5.25 s is the sedan.
**Two fixes that failed, both recorded.**
- **Splitting on appearance.** The van-to-sedan jump scores 0.30 on the ReID embedding, while pure tracklets score up to 0.45 from viewpoint change alone. No threshold separates them.
- **A stricter tracker threshold.** At 0.6 the tracker ends the van correctly and starts a new ID for the sedan, but fragments across the subset rise from 196 to 253, and the linker then re-creates the identical switch with a cost of 0.50. Geometry and appearance both say the sedan continues the van.
**The fix that works.** A vehicle whose box is pinned to a picture edge and shrinking for three frames is leaving the picture. Such a tracklet gets no successor, an entering one gets no predecessor, and a leaving run followed by a box detached from that edge is split there, because that box is another vehicle. It uses the gate's active crop as the edge, since this clip is pillarboxed and the picture ends at 1774, not 1920.
| across the subset | before | after |
| - | - | - |
| splits | 0 | 1 |
| links | 7 | 7 |
| vehicles | 189 | 190 |
**In the clip now:** tracklet 17 is the van until 5.07 s, the sedan after impact is a new tracklet 93 shown as its own vehicle, and the sedan befo…
04:16 · asked1) what is the yolo model size we use? 2) what's the span of ReID and BoT-SORT (the span of how long they can be left out of frame and come back in. is there a configurable settings for this) 3) who has a stronger voice BoT-SORT or ReID? 4) what's the best practice for confidence? keep it as low as ours? I see 0.1 ~ objects. 5) maybe we can silence vehicle detection by distance, this is something to think about. 6) as you said these confidence and threshold should all be a configurable thing we can change later just worth mentioning. 7) i think adding these kind of rules are very dangerous. 8) i still see other problems, but i am not sure if this can continue on with case by case rule fix. 9) this is just my effort to help you First, the order that matters most
Tune the detector before you touch a single tracker parameter. Almost every "bad tracking" problem is bad detection wearing a costume. If the detector drops the person for 4 frames, no track_buffer value fixes that — you're asking the tracker to invent data. Check your detections frame-by-frame with tracking off first. Only when detection is solid does tracker tuning mean anything.
Second rule: change one parameter at a time and measure. These interact badly, and stacked "improvements" routinely cancel out.
The parameters, with real defaults
From botsort.yaml on current main:
┌───────────────────┬───────────────┬────────────────────────────────────────┬──────────────────────────────────────────────────────────┐
│ Parameter │ Default │ What it does │ When to change │
├───────────────────┼───────────────┼────────────────────────────────────────┼──────────────────────────────────────────────────────────┤
│ track_high_thresh │ 0.25…
04:16 · repliedLet me pull two facts from the running setup before answering: the detector's NMS mode and the exact tracker thresholds in effect.
04:17 · repliedYou're right on 7 and 8, and I'd rather say so plainly than defend the edge rule. It is a narrow geometric prior about the picture boundary, it is switchable in config, and it fixed one clip. But I added it without a metric, and a process that fixes cases one rule at a time will not converge. The pasted guide's first rule is the right order, and it is not the order I followed today.
**Answers to your numbered points**
1. **Detector size.** YOLO26x, the largest, 59M parameters, at 1280 input, detector confidence 0.15. Running with NMS, not the NMS-free head, so the guide's re-tuning caveat does not apply yet.
2. **Span.** The lost-track buffer is in seconds in our config, 2.0 s, converted to fed frames, so the guide's frame-rate pitfall is already handled. But ReID cannot bridge that span today: appearance is only consulted when the predicted box still overlaps the detection at IoU 0.5 or more. That is why "BoT-SORT with ReID" never re-attached anything after a real gap. The linker's window is 2.5 s.
3. **Stronger voice.** Motion. The tracker takes the minimum of the IoU cost and the appearance cost, and only where boxes overlap. Appearance can help a match and can never veto one. The van-to-sedan switch is exactly that design showing through.
4. **Confidence.** Our setup keeps the second-pass band non-empty: 22% of kept detections sit between 0.15 and 0.25 and are used only to keep existing tracks alive. The objects you see at 0.1 in the viewer are stored detections, not tracks. Best practice is a low detector threshold plus a higher birth threshold. Our birth threshold is 0.25, which is the likely source of the horizon flicker tracks.
5. **Silencing by distance.** Agreed, as an output-time flag on box size or, later, on metric distance, never as deletion. It needs th…
05:49 · askedyes, use the B boxes for evaluation only. build the scoring first. but before that just from talking to you i realized we are not standardizing the inputs. the frames, resolution (downsize, or upscale when needed) fit for our model. all the trained models have some kind of best resolution and sizes don't they? this should be before the gate or yolo,bot-sort,reid pipeline.
05:49 · repliedYes, and I understand it as a new first stage: every clip is turned into a standard working picture before any model sees it, with the transform recorded so every box can be mapped back. Here is what the models actually want, and then what I think the stage should and should not do.
**What each model's "best size" really is**
| model | trained or fixed input | what we feed today |
| - | - | - |
| YOLO26x | trained at 640, letterboxed internally to `imgsz` | full frame letterboxed to 1280 |
| Mask2Former, Mapillary | shortest edge 512 to 1024 | shortest edge 512 |
| ReID encoder, yolo26x-reid | fixed 224×224 crops | crops, any size, squashed to 224 |
| Depth Pro | fixed 1536×1536 | not in the pipeline |
The subtle point is that the detector letterboxes anyway, so a 1280×720 clip and a 1920×1080 clip end up at the same scale inside the model. Native resolution mostly washes out. What does not wash out:
- **Pillarbox and letterbox bars.** On the dense clip the picture is 1627 px wide inside a 1920 frame. The bars eat detector resolution, they confused the ego mask, and they broke the edge rule until I read the active crop. Cropping to the active picture before every model is the single real win.
- **Object pixel size relative to `imgsz`.** Far vehicles are 20 to 40 px tall. At 1280 that is the flicker regime. Whether we feed 1280 or 1920 is a detector budget decision to test, not a per-clip thing.
- **Frame rate.** All clips are 15 fps. We feed every second frame, 7.5 fps, which the guide rightly warns weakens motion prediction. That is a policy choice to make once.
- **ReID crops.** Tall or wide crops are squashed to a square. Padding to square before resizing keeps aspect, and is cheap.
**What I would not do:** upscale 720p clips to 1080p. It adds no information, co…
05:55 · askedbuild the stage. and build the b-box scoring on top. and do generate a report. parllel work with agents if possible.
05:56 · repliedTwo independent tracks, so two agents in parallel: one builds the `frames` stage and routes gate, track and link through it; the other builds the B-box scoring and picks a scoring subset. I'll snapshot the current outputs first so the report can show before and after, then integrate, rerun the pipeline, score, and write the report.
05:56 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are the agent for Track A: build the `frames` stage in ~/projects/cv and route gate, track and link through it. Another agent is concurrently writing `src/cv/score.py` and appending a `[score]` section at the END of config.toml and adding "score" to STAGES in store.py — do not touch those parts; you own: new `src/cv/frames.py`, edits to `src/cv/gate.py`, `src/cv/track.py`, `src/cv/link.py`, `src/cv/egomask.py` if needed, a `[frames]` section in config.toml inserted BEFORE `[egomask]` (use Edit with a small unique anchor, never rewrite the whole file), and adding "frames" to STAGES in store.py with file `frames.json` (make a minimal targeted edit; the other agent adds "score" separately — if you see "score" already there, keep it). Do NOT run the pipeline over the whole subset and do NOT write a report; the parent will r…
05:56 · repliedTask: build `src/cv/frames.py` and route gate, track and link through it (Track A). Reading the current files first.
05:56 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You are the agent for Track B: build the B-box scoring in ~/projects/cv. Another agent is concurrently creating `src/cv/frames.py` and editing gate.py/track.py/link.py/egomask.py, inserting a `[frames]` section in config.toml before `[egomask]`, and adding "frames" to STAGES in store.py — do not touch those. You own: new `src/cv/score.py`, new `experiments/score_gallery.py` if useful, a `[score]` section APPENDED at the END of config.toml (use Edit anchored on the last lines of the `[link]` section or append via a small targeted edit; never rewrite the file), adding "score" to STAGES in store.py with file `score.json` via a minimal targeted edit (if "frames" is already there, keep it), and a new subset file `subsets/score-30.txt`. Do NOT run gate/track/link (the pipeline code is being refactored under you) and do NOT modify…
05:57 · repliedBoth agents are running: one on the `frames` stage and the pipeline refactor, the other on the B-box scoring and the 30-clip scoring subset. Nothing else can proceed until they report, since the rerun depends on the refactored stages and the report depends on the scorer. I'll integrate, rerun the pipeline over the scoring subset, score it, and write the report as soon as they finish.
05:57 · repliedTask: build the B-box scoring stage (`cv.score`), `[score]` config, `score` in the store, `subsets/score-30.txt`, and test it on the existing gate-10 outputs.
05:57 · repliedPlan: write `frames.py`, add the `[frames]` config section and store stage, move the bar measurement out of `egomask.py`, then patch gate, track and link, and test on one clip.
05:58 · repliedNow egomask takes the crop instead of measuring it, and gate routes through `FrameSource`.
05:59 · repliedRemoving the fallback hack in `embed_tracklets` (link always passes a source), then running the four stages on one clip.
06:00 · repliedTask: build the B-box scoring stage in ~/projects/cv (score.py, [score] config, store registration, score-30 subset), tested on the existing gate-10 outputs. Done.
**Files created or edited**
- `src/cv/score.py` (new): the stage, CLI `python -m cv.score --subset|--clips [--overwrite] [--note] [--figure "path|caption"] [--report-only] [--no-report]`, Korean report to `reports/<date>-score-run.docx` with per-clip 4-instant contact sheets under `reports/<date>-score-run/`.
- `config.toml`: `[score]` appended at the end: `iou_match = 0.5`, `sample_every_s = 0.5`, `use_vehicles = true`, `window_s = 1.5`.
- `src/cv/store.py`: `"score"` added to STAGES with file `score.json` (the other agent's `"frames"` entry was already present and is kept).
- `subsets/score-30.txt` (new, 30 ids) and `subsets/README.md` (new, selection rules for both subsets).
- Test artefacts only: `work/score/<clip>/score.json` for the gate-10 clips, `work/score-check-000211.jpg`, and a throwaway `reports/2026-09-09-score-run.docx` from the gate-10 test run (the parent's real run will overwrite it).
**Metric definitions.** Ground truth is the Xe B box, keyframed at 24 fps tool time (t = frame/24), in percent of the full uploaded frame including pillarbox bars, interpolated linearly between keyframes, never extrapolated, and hidden from any `enabled = false` keyframe on. Evaluation instants are every visible keyframe plus every 0.5 s across the visible span; each instant maps to the nearest fed frame (stride from the detections header). A box matches at IoU ≥ 0.5. Per clip: `det_recall` = share of instants with a matching kept gate detection; `veh_coverage` = share with a matching vehicle; `veh_identity` = share of covered instants carried by the single most frequent vehicle id; `veh_switches` = id change…
06:00 · asked<task-notification>
<task-id>a2b414fb79604e938</task-id>
<tool-use-id>toolu_01SPYc17NoUCuMdoTgA268Er</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-cv-processing/03132d5f-89fa-4eb6-b2b4-fcdc5794d2ba/tasks/a2b414fb79604e938.output</output-file>
<status>completed</status>
<summary>Agent "Build B-box identity scoring" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: build the B-box scoring stage in ~/projects/cv (score.py, [score] config, store registration, score-30 subset), tested on the existing gate-10 outputs. Done.
**Files created or edited**
- `src/cv/score.py` (new): the stage, CLI `python -m cv.score --subset|--clips [--overwrite] [--note] [--figure "path|caption"] [--report-only] [--no-report]`, Korean report to `reports/<date>-score-run.docx` with per-clip 4-instant contact sheets under `reports/<date>-score-run/`.
- `config.toml`: `[score]` appended at the end: `iou_match = 0.5`, `sample_every_s = 0.5`, `use_vehicles = true`, `window_s = 1.5`.
- `src/cv/store.py`: `"score"` added to STAGES with file `score.json` (the other agent's `"frames"` entry was already present and is kept).
- `subsets/score-30.txt` (new, 30 ids) and `subsets/README.md` (new, selection rules for both subsets).
- Test artefacts only: `work/score/<clip>/score.json` for the gate-10 clips, `work/score-check-000211.jpg`, and a throwaway `reports/2026-09-09-score-run.docx` from the gate-10 test run (the parent's real run will overwrite it).
**Metric definitions.** Ground truth is the Xe B box, keyframed at 24 fps tool time (t = frame/24), in percent of the …
06:00 · repliedThe scoring stage is done and validated: the B box maps exactly onto the sedan in the dense clip, and the 30-clip scoring subset is chosen. Waiting on the `frames` agent before rerunning the pipeline over those 30 clips.
06:02 · repliedTask: build the `frames` stage and route gate, track and link through it. Done and tested on one clip; nothing committed.
**New file `src/cv/frames.py`**
- `measure(video, cfg)`: w, h, fps, frame_count, duration_s, active_crop (pillarbox/letterbox from `probe_frames`), crop_w/h, bars_px, sha256, decode_ok, stride. `ensure(clip, video, cfg, overwrite)` caches to `work/frames/<clip>/frames.json` and registers stage "frames" with the store (payload has `video` and `summary`).
- `FrameSource(video, meta, stride)`: iterates `(frame_idx, picture, (ox, oy))` with the frame cropped to the active picture; `iter_indices(...)` for selected frames; `frame_at(index, full=False)`; `to_full` / `to_picture`; `pic_w`, `pic_h`, `fed_indices()`.
- `square_xywh` and `SquareCropEncoder`: pads ReID crop boxes to squares before the encoder resizes them.
- `stride_of(cfg)`: `[frames].stride`, falling back to the deprecated `[detect].stride`.
- CLI `python -m cv.frames --subset/--clips [--overwrite]` prints a table.
**`config.toml`** `[frames]` inserted before `[egomask]`: `stride = 1`, `black_max = 16`, `min_bar_px = 8`, `probe_frames = 5`, `reid_pad_square = true`. `[pillarbox]` left in place but no longer read by egomask; `[detect].stride` left as the documented fallback.
**`store.py`**: STAGES now `("frames", "gate", "track", "link")` with `frames.json`; the other agent's "score" entry was not present at edit time, and the running server now lists `frames, score, gate, track, link`, so both edits coexist.
**`egomask.py`**: `compute_ego_mask(frames, seg, cfg, crop)` takes the crop from the frames stage instead of measuring bars; `active_crop` kept as a thin shim delegating to `cv.frames`.
**`gate.py`**: calls `ensure_frames` first, computes the ego mask on full frames with the crop appl…
06:02 · asked<task-notification>
<task-id>a444d2e1890fe9aa1</task-id>
<tool-use-id>toolu_01MMivx91GKWQzVq4XjkxSSb</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-cv-processing/03132d5f-89fa-4eb6-b2b4-fcdc5794d2ba/tasks/a444d2e1890fe9aa1.output</output-file>
<status>completed</status>
<summary>Agent "Build frames stage, route stages through it" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Task: build the `frames` stage and route gate, track and link through it. Done and tested on one clip; nothing committed.
**New file `src/cv/frames.py`**
- `measure(video, cfg)`: w, h, fps, frame_count, duration_s, active_crop (pillarbox/letterbox from `probe_frames`), crop_w/h, bars_px, sha256, decode_ok, stride. `ensure(clip, video, cfg, overwrite)` caches to `work/frames/<clip>/frames.json` and registers stage "frames" with the store (payload has `video` and `summary`).
- `FrameSource(video, meta, stride)`: iterates `(frame_idx, picture, (ox, oy))` with the frame cropped to the active picture; `iter_indices(...)` for selected frames; `frame_at(index, full=False)`; `to_full` / `to_picture`; `pic_w`, `pic_h`, `fed_indices()`.
- `square_xywh` and `SquareCropEncoder`: pads ReID crop boxes to squares before the encoder resizes them.
- `stride_of(cfg)`: `[frames].stride`, falling back to the deprecated `[detect].stride`.
- CLI `python -m cv.frames --subset/--clips [--overwrite]` prints a table.
**`config.toml`** `[frames]` inserted before `[egomask]`: `stride = 1`, `black_max = 16`, `min_bar_px = 8`, `probe_frames = 5`, `reid_pad_square = true`. `[pillarbox]` left in place but no…
06:03 · repliedBoth tracks are in. A nice early signal: at stride 1 the linker now joins the sedan's pre-occlusion and post-impact pieces into one vehicle, so the other party in the dense clip is a single ID. Now the full run: the four stages over the 30 scoring clips, then scoring. To make this a clean one-parameter test, the same 30 clips also run at stride 2 in a variant work directory, so the report compares stride 1 against stride 2 with everything else equal. Roughly an hour of compute, in the background.
06:04 · repliedThe run is into the gate stage. One more thing that does not depend on it: saving your process guidance as a standing rule in memory, since it changes how I should work on this project from now on.
06:04 · repliedNothing else is independent of the run now. The remaining work, the comparison table, the report and its figures, all consume the score files that the run is still producing. I'll pick it up at the next stage boundary.
06:12 · asked<task-notification>
<task-id>b54p0pe0p</task-id>
<summary>Monitor event: "score-30 run: stage boundaries, every 10th clip, errors"</summary>
<event>INFO cv.gate: [10/30] bb_1_120316_vehicle_110_032 mask 19% raw 511 → kept 511 (hood 0, below 0, inside 0) 21s
INFO cv.gate: [20/30] bb_1_150911_vehicle_251_34345 mask 0% raw 2034 → kept 2034 (hood 0, below 0, inside 0) 36s
INFO cv.gate: [30/30] bb_1_221126_vehicle_199_001 mask 24% raw 1298 → kept 1298 (hood 0, below 0, inside 0) 19s
=== MAIN track</event>
If this event is something the user would act on now, send a PushNotification. Routine or benign output doesn't need one.
</task-notification>
06:13 · repliedGate finished on all 30 clips at stride 1. Tracking is running now, the slowest stage at roughly a minute per clip. Waiting.
06:27 · asked<task-notification>
<task-id>b54p0pe0p</task-id>
<summary>Monitor event: "score-30 run: stage boundaries, every 10th clip, errors"</summary>
<event>INFO cv.track: [10/30] bb_1_120316_vehicle_110_032 frames 150 dets 511 → tracks 9 (<1s 3, short 1) ids/frame 3.0 longest 9.9s 13s
INFO cv.track: [20/30] bb_1_150911_vehicle_251_34345 frames 150 dets 1995 → tracks 78 (<1s 41, short 12) ids/frame 9.0 longest 4.9s 62s
INFO cv.track: [30/30] bb_1_221126_vehicle_199_001 frames 150 dets 1274 → tracks 27 (<1s 14, short 5) ids/frame 6.0 longest 9.9s 38s
=== MAIN link</event>
If this event is something the user would act on now, send a PushNotification. Routine or benign output doesn't need one.
</task-notification>
06:27 · repliedTracking done on all 30. Linking and scoring for the stride-1 run come next, then the whole sequence again at stride 2.
06:30 · asked<task-notification>
<task-id>b54p0pe0p</task-id>
<summary>Monitor event: "score-30 run: stage boundaries, every 10th clip, errors"</summary>
<event>INFO cv.link: [10/30] bb_1_120316_vehicle_110_032 tracks 9 → vehicles 10 (splits 2, links 1, multi 1, <1s 3) longest 9.9s 3s
INFO cv.link: [20/30] bb_1_150911_vehicle_251_34345 tracks 78 → vehicles 74 (splits 0, links 4, multi 4, <1s 34) longest 4.9s 11s
INFO cv.link: [30/30] bb_1_221126_vehicle_199_001 tracks 27 → vehicles 25 (splits 0, links 2, multi 2, <1s 12) longest 9.9s 5s
=== MAIN score</event>
If this event is something the user would act on now, send a PushNotification. Routine or benign output doesn't need one.
</task-notification>
06:30 · repliedLinking done. Scoring the stride-1 run now, then the stride-2 variant starts.
06:30 · asked<task-notification>
<task-id>b54p0pe0p</task-id>
<summary>Monitor event: "score-30 run: stage boundaries, every 10th clip, errors"</summary>
<event>INFO cv.score: [30/30] bb_1_221126_vehicle_199_001 inst 43 det 0.72 | trk cov 0.70 id 1.00 sw 0 | veh cov 0.70 id 1.00 sw 0 | impact inst 14
=== VAR stride2: frames</event>
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</task-notification>
06:30 · repliedStride-1 scoring is complete and the stride-2 variant has started. Looking at the stride-1 results while it runs.
06:31 · repliedBoth background jobs are running: the stride-2 pipeline variant and the two link-rule variants. Everything left, the comparison tables, the report and its figures, depends on their scores, so I'll wait for the notifications.
06:31 · asked<task-notification>
<task-id>bjuxtyxwp</task-id>
<summary>Monitor event: "link variants (nosplit, noedge): stage boundaries and errors"</summary>
<event>=== nosplit link</event>
If this event is something the user would act on now, send a PushNotification. Routine or benign output doesn't need one.
</task-notification>
06:32 · repliedLink variant one is linking. Waiting.
06:36 · asked<task-notification>
<task-id>bjuxtyxwp</task-id>
<summary>Monitor event: "link variants (nosplit, noedge): stage boundaries and errors"</summary>
<event>=== nosplit score</event>
If this event is something the user would act on now, send a PushNotification. Routine or benign output doesn't need one.
</task-notification>
06:36 · repliedScoring the no-split variant now.
06:36 · asked<task-notification>
<task-id>bjuxtyxwp</task-id>
<summary>Monitor event: "link variants (nosplit, noedge): stage boundaries and errors"</summary>
<event>=== noedge link</event>
If this event is something the user would act on now, send a PushNotification. Routine or benign output doesn't need one.
</task-notification>
06:36 · repliedThe no-split variant is scored and the no-edge variant is linking. I'll compare all three once it finishes.
06:36 · asked<task-notification>
<task-id>b54p0pe0p</task-id>
<summary>Monitor event: "score-30 run: stage boundaries, every 10th clip, errors"</summary>
<event>INFO cv.gate: [10/30] bb_1_120316_vehicle_110_032 mask 19% raw 257 → kept 257 (hood 0, below 0, inside 0) 13s
INFO cv.gate: [20/30] bb_1_150911_vehicle_251_34345 mask 0% raw 1014 → kept 1014 (hood 0, below 0, inside 0) 12s
INFO cv.gate: [30/30] bb_1_221126_vehicle_199_001 mask 24% raw 647 → kept 647 (hood 0, below 0, inside 0) 11s
=== VAR track</event>
If this event is something the user would act on now, send a PushNotification. Routine or benign output doesn't need one.
</task-notification>
06:36 · repliedThe stride-2 variant is through its gate stage and into tracking. Still waiting on the last link variant.
06:40 · asked<task-notification>
<task-id>bjuxtyxwp</task-id>
<summary>Monitor event: "link variants (nosplit, noedge): stage boundaries and errors"</summary>
<event>=== noedge score</event>
If this event is something the user would act on now, send a PushNotification. Routine or benign output doesn't need one.
</task-notification>
06:40 · repliedLast scoring pass for the link variants. Comparison next.
06:40 · asked<task-notification>
<task-id>bp49nnf3o</task-id>
<tool-use-id>toolu_016sxW6ZDzactVWi1AZ8TZDj</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-cv-processing/03132d5f-89fa-4eb6-b2b4-fcdc5794d2ba/tasks/bp49nnf3o.output</output-file>
<status>completed</status>
<summary>Background command "Add edge_split and edge_veto switches; run link and score variants with them off" completed (exit code 0)</summary>
</task-notification>
06:40 · asked<task-notification>
<task-id>bjuxtyxwp</task-id>
<summary>Monitor event: "link variants (nosplit, noedge): stage boundaries and errors"</summary>
<event>=== LINKVARIANTS DONE</event>
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</task-notification>
06:40 · repliedLink variants done. Comparing the edge rule on, split off, and both off across the 30 clips.
06:40 · repliedBoth remaining jobs are in flight: the final link-and-score pass with the new defaults, and the stride-2 variant in its tracking stage. The report needs both, so I'll assemble it when they land.
06:44 · asked<task-notification>
<task-id>bnxgjp1z8</task-id>
<tool-use-id>toolu_015Ru5od5oUkdG9PHkY9LeC3</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-cv-processing/03132d5f-89fa-4eb6-b2b4-fcdc5794d2ba/tasks/bnxgjp1z8.output</output-file>
<status>completed</status>
<summary>Background command "Turn the edge rule off by default, document why, rerun main link and score" completed (exit code 0)</summary>
</task-notification>
06:44 · repliedThe final link-and-score pass with the new defaults is done. Confirming its means match the "both off" variant, then waiting for the stride-2 run.
06:44 · repliedFinal numbers match the variant exactly. Now the only outstanding input is the stride-2 score. Waiting for that run.
06:46 · asked<task-notification>
<task-id>b54p0pe0p</task-id>
<summary>Monitor event: "score-30 run: stage boundaries, every 10th clip, errors"</summary>
<event>INFO cv.track: [10/30] bb_1_120316_vehicle_110_032 frames 75 dets 257 → tracks 7 (<1s 1, short 0) ids/frame 3.0 longest 9.9s 9s
INFO cv.track: [20/30] bb_1_150911_vehicle_251_34345 frames 75 dets 994 → tracks 67 (<1s 42, short 21) ids/frame 8.0 longest 9.9s 41s
INFO cv.track: [30/30] bb_1_221126_vehicle_199_001 frames 75 dets 635 → tracks 19 (<1s 3, short 1) ids/frame 6.0 longest 9.7s 20s
=== VAR link</event>
If this event is something the user would act on now, send a PushNotification. Routine or benign output doesn't need one.
</task-notification>
06:47 · repliedThe stride-2 variant is linking with its config copy, which still has the edge rule on because it was copied before the decision. For a clean one-parameter comparison it must match. I'll flip its config now and rerun just its link and score after the current run ends.
06:47 · repliedThe variant config predates those keys, and the code defaults them to on when missing. Adding them explicitly.
06:47 · repliedThe variant config is fixed. Waiting for the stride-2 run to finish its (edge-on) link and score, after which I rerun those two steps with the corrected config and build the comparison.
06:49 · asked<task-notification>
<task-id>b54p0pe0p</task-id>
<summary>Monitor event: "score-30 run: stage boundaries, every 10th clip, errors"</summary>
<event>INFO cv.link: [10/30] bb_1_120316_vehicle_110_032 tracks 7 → vehicles 7 (splits 0, links 0, multi 0, <1s 1) longest 9.9s 3s
INFO cv.link: [20/30] bb_1_150911_vehicle_251_34345 tracks 67 → vehicles 61 (splits 0, links 6, multi 5, <1s 33) longest 9.9s 9s
INFO cv.link: [30/30] bb_1_221126_vehicle_199_001 tracks 19 → vehicles 19 (splits 0, links 0, multi 0, <1s 3) longest 9.7s 4s
=== VAR score</event>
If this event is something the user would act on now, send a PushNotification. Routine or benign output doesn't need one.
</task-notification>
06:49 · repliedVariant scoring is the last step of that run. Then a quick link-and-score rerun for it with the corrected config, and the comparison.
06:49 · asked<task-notification>
<task-id>bu5mmjj73</task-id>
<tool-use-id>toolu_01A21Mt7mXfxRbWjdWvx83Qz</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-cv-processing/03132d5f-89fa-4eb6-b2b4-fcdc5794d2ba/tasks/bu5mmjj73.output</output-file>
<status>completed</status>
<summary>Background command "Run all stages and scoring over score-30 at stride 1, then the stride-2 variant" completed (exit code 0)</summary>
</task-notification>
06:49 · asked<task-notification>
<task-id>b54p0pe0p</task-id>
<summary>Monitor event: "score-30 run: stage boundaries, every 10th clip, errors"</summary>
<event>INFO cv.score: [30/30] bb_1_221126_vehicle_199_001 inst 43 det 0.72 | trk cov 0.70 id 1.00 sw 0 | veh cov 0.70 id 1.00 sw 0 | impact inst 14
=== ALL DONE</event>
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</task-notification>
06:49 · repliedAll runs are done. Rerunning the variant's link and score with the edge rule off so the stride comparison is one-parameter, then building the comparison table and chart.
06:52 · repliedEverything for the report is in hand. Generating it with the findings and figures, then sending it and staging the work.
06:54 · repliedThe `frames` stage and the B-box scoring are built, the 30-clip scoring set ran through the whole pipeline twice, and the report is sent. Everything is staged in git, not committed. The viewer lists all 37 clips with every stage.
**What the score says about our pipeline,** 30 clips, evaluated against the B-vehicle boxes:
| metric, mean over 30 clips | whole span | within 1.5 s of impact |
| - | - | - |
| detection recall | 0.85 | 0.85 |
| vehicle coverage | 0.82 | 0.82 |
| vehicle identity share | 0.92 | 0.94 |
| ID switches per clip | 1.3 | 0.8 |
The linker adds a little over raw tracklets, identity 0.90 to 0.92 with 63 links. One clip has detection recall of 0.26 and is the first target for the detector audit. Four clips carry five or more switches.
**The scoring settled the edge rule, and you were right.** With the rule on, identity was 0.84. It cut the other party's own tracklet in seven clips, because at contact the other party fills the picture and touches an edge, and its veto blocked a correct link on another. Fixed one clip, damaged eight. It is now off by default and stays only as a scored, switchable option. The van-to-sedan switch in the dense clip returns and is filed under in-frame switches, to be attacked with a stronger appearance embedding.
**Stride 1 versus stride 2, one parameter, everything else equal.** Stride 1 gains coverage and identity share and costs twice the compute, more short tracklets, and a higher raw switch count. I kept stride 1 and made the switch count the target of the next single-parameter test, the track birth threshold.
**What landed in the project.**
- `frames` stage: per-clip size, fps, hash and active picture, cropped pictures served to every later stage, stride owned here. Eleven of the 30 clips have bars, with pictures …
07:01 · asked<task-notification>
<task-id>bjuxtyxwp</task-id>
<summary>Monitor event: "link variants (nosplit, noedge): stage boundaries and errors"</summary>
<event>[Monitor timed out — re-arm if needed.]</event>
</task-notification>
07:01 · repliedThat monitor timing out is expected: its job finished earlier and its results are already in the report. Nothing to re-arm. I'm waiting on your read of the scoring report and the proposed next test.
07:02 · askedwith no labeled data per frame, how do we know if something is better? do i go through the video and mark when id switch on same vehicle happens? hand link the tracklets? how do we know if we fixed anything?
07:03 · repliedShort answer: we already have partial ground truth, and the rest is cheaper to make than it sounds because we label our output, not the video.
**What the B boxes already give us.** For 313 clips the other party has a keyframed box, interpolated between keyframes. That is per-instant truth for one vehicle, the one the fault analysis cares about. The score built today answers "is the other party one ID, and is it detected" on 30 clips, and it already caught the edge rule doing damage. Its blind spot is every other vehicle, and it cannot tell a switch from a fragment on vehicles the labeler never boxed.
**What per-frame labels would cost, and why we don't need them.** Boxing every vehicle in every frame is days per hundred clips and is the wrong tool. Identity metrics only need to know which boxes belong to the same physical vehicle, and our pipeline already draws the boxes.
**The practical method: verdicts on our output.** In our viewer, a reviewer plays a clip with vehicle IDs on and records three kinds of verdict:
- **Same vehicle.** Select two vehicle IDs and mark them as one. That is the hand link you described.
- **Switch here.** Select a vehicle and a time and mark that from here it is a different vehicle.
- **Not a vehicle.** Mark a phantom.
That is one to three minutes per clip. The 30-clip set is an afternoon.
**Why the verdicts survive re-runs.** They are not stored as "vehicle 17 equals vehicle 25", which would break the moment IDs renumber. Each verdict is turned into a physical vehicle described by a handful of timed boxes taken from the vehicles it was placed on. After any change, each new vehicle is matched to the physical vehicles by box overlap at those times. From that we compute, for every vehicle in the clip: fragmentation, one physical vehicle s…
07:03 · asked<task-notification>
<task-id>b54p0pe0p</task-id>
<summary>Monitor event: "score-30 run: stage boundaries, every 10th clip, errors"</summary>
<event>[Monitor timed out — re-arm if needed.]</event>
</task-notification>
07:03 · repliedThat monitor's job finished long ago and its results are in the report. Nothing to re-arm. Waiting on your call about building the review mode.
07:06 · askedbuild the review mode, i'll label the first ten
07:07 · repliedBuilding it in three parts: a verdict store that turns verdicts into physical vehicles with timed sample boxes, so they survive re-runs; a review mode in the viewer with keyboard actions and autosave; and a verdict-based scorer with its own report. Verdicts go under `labels/` in the repo, not under `work/`, since they are our ground truth.
07:10 · repliedVerdicts did not register in the test, so I'm checking whether the key handler or the save call is failing, by invoking the functions directly and reading the server's reply.
07:12 · repliedThe review mode is ready to use now, while the background rerun refreshes three clips. Open the viewer, pick `score-30` in the header dropdown, and tick review mode or press `r`.
**How to label a clip.**
- Play it with vehicle IDs on. Click a vehicle in the right panel or its bar in the timeline to isolate it.
- **Same vehicle:** with one selected, shift-click another vehicle's row.
- **Switch:** with one selected, pause where the ID jumps to a different car and press `x`.
- **Phantom:** with one selected, press `p`.
- **Missed vehicle:** press `n`, then drag a box over it on the picture at that time.
- `u` undoes the last verdict, the ✕ next to any verdict deletes it, and "mark reviewed" finishes the clip. The list shows a ✓ and the header counts reviewed clips.
Every verdict saves at once to `labels/verdicts/<clip>.json`, already converted into physical vehicles with timed sample boxes, so re-runs that renumber IDs cannot invalidate your work. The scorer on top reports fragments, switched IDs, phantoms, missed vehicles and identity share for all vehicles, with a Korean report.
**Two things to know before you start.**
- Verdicts are on the current link output. If a clip shows "stride 2" in the info line, wait a few minutes: three clips shared between the two subsets kept old outputs and are being rerun now. The other 27 are current.
- The video still needs the ams-1080 server on port 41080.
When your ten are done, say so and I'll run the verdict scorer and produce its first report alongside the refreshed B-box numbers.
07:12 · asked<task-notification>
<task-id>bh9qtxvbz</task-id>
<summary>Monitor event: "overlap fix rerun: stage boundaries, errors, new means"</summary>
<event>=== frames done</event>
If this event is something the user would act on now, send a PushNotification. Routine or benign output doesn't need one.
</task-notification>
07:12 · repliedThe rerun is through its frames stage and into the gate. I'll update the score report once the new means land.
07:20 · asked<task-notification>
<task-id>bigy0hg23</task-id>
<tool-use-id>toolu_018N8Ln5d5TSNmd7WUtbfCon</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-cv-processing/03132d5f-89fa-4eb6-b2b4-fcdc5794d2ba/tasks/bigy0hg23.output</output-file>
<status>completed</status>
<summary>Background command "Rerun the gate-10 clips at stride 1 with overwrite, rescore score-30, refresh the comparison" completed (exit code 0)</summary>
</task-notification>
07:20 · repliedAll 30 clips are now at stride 1 and the numbers moved only slightly. Regenerating the score report with the corrected figures and resending it.
07:21 · repliedEvery clip in the scoring set is now at stride 1, and the report is refreshed and sent. The corrected means moved by a few thousandths and no conclusion changed.
| stride 1, 30 clips | whole span | within 1.5 s of impact |
| - | - | - |
| detection recall | 0.85 | 0.85 |
| vehicle coverage | 0.82 | 0.82 |
| vehicle identity share | 0.91 | 0.94 |
| switches per clip | 1.4 | 0.8 |
The viewer now shows stride 1 on all 30 clips, so anything you review is current. Everything is staged in git, uncommitted.
When your ten clips are reviewed, tell me and I'll run the verdict scorer for the first all-vehicle baseline, then start the detector audit on the low-recall clip with tracking off.
07:29 · askedand we already tested running video twice forward and backwards and combine the two right? and that didn't help?
07:29 · repliedNo, we have not tested that. I should be clear about what was and was not run.
**What was tested.** The offline linker, which uses backward extrapolation only as a check on a candidate pair: it projects the later tracklet's motion back to the earlier one's end. That is a small use of "backwards" and it did not run the tracker in reverse.
**What you are describing** is a different and well-known technique: run BoT-SORT forward over the clip, run it again on the reversed frame sequence, then reconcile the two ID assignments. It helps a specific failure: a vehicle the forward pass picks up late, or drops early, because the tracker needs a few confident frames to birth a track and gives up after the buffer. In reverse, the vehicle's late frames become its early frames, so the reverse pass often holds an ID through the stretch the forward pass lost. Reconciling the two, by matching forward and reverse tracklets on the frames where both cover the same box, can heal fragments that neither pass alone could bridge.
**What it does not fix.** An ID switch at contact, like the van-to-sedan handover, tends to happen in both directions, because the switch is caused by the geometry, two boxes meeting at a frame edge, not by the time direction. The reverse pass would likely switch there too, just from the sedan to the van.
**Why it is worth testing now, and cheaply.** It is a single-change experiment with a ready-made metric. The tracker stage already runs from the frozen detections file, so the reverse pass costs one tracking run over reversed frames with no new detection. The linker then takes both passes as input. On the score, the effect should show as higher tracklet coverage and fewer fragments, with switches unchanged. If the B-box scores and your verdicts both improve, it s…
07:35 · askedi think the review should be notes instead of controls. it's too hard to use control. maybe a llm to read it later? would that be too much?
07:36 · repliedNot too much. It is the right shape for this task: writing "the dark sedan on the right takes the van's ID at 5.2 s" is faster and more natural than hunting for rows and keys, and a model can turn that into the same verdict records afterwards. The one thing free text needs is anchors. A note that says "the sedan" is ambiguous to a reader who was not there. If each note automatically carries the clip time and the selected vehicle, and you can drop a `V17 @ 5.20s` token into the text with one key, the later conversion becomes easy and checkable.
So the review panel becomes a notebook: one text box per clip, notes stamped with time and vehicle, autosaved, the old controls kept but out of the way. The conversion step comes later as a batch command that reads the notes, proposes verdicts in the existing schema, and shows them next to the note for a yes or no. Building the notebook now.
07:37 · repliedThe review panel is now a notebook. Reload the viewer, pick `score-30`, press `r`.
**How it works.**
- Watch the clip. When you see something, pause and type what you see in your own words in the box. Selecting a vehicle first is optional but helpful.
- `tab` inserts a `V# @ time` token for the selected vehicle at the current time, so a note like "V17 @ 5.20s the dark sedan takes the van's ID here" costs one keystroke for the anchor.
- `⌘↵` adds the note. Each note is stamped with the time and selected vehicle regardless of what you typed. Clicking a stamp seeks the video to it. ✕ deletes a note.
- "mark reviewed" when the clip is done. Notes save at once to `labels/verdicts/<clip>.json`.
- The old controls are still there, folded under "structured verdicts", for the rare case you want to record something precisely.
**What is in a good note.** Which vehicle, roughly when, and what kind of thing it is: same car under two IDs, an ID that jumps to another car, a box on nothing, a car with no box. The later conversion step reads the notes together with the vehicle list for that clip, so "the white van on the right" plus a time is enough for it to find the ID.
**The conversion step** is the next build once you have written some notes, since I want real notes to design it against rather than guessing the phrasing. It will propose verdicts per note and show them beside your text for a yes or no, and only confirmed ones feed the scorer. Cost is negligible at this volume.
Everything is staged, not committed. When your ten clips have notes, tell me and I'll build the converter on them.
07:42 · asked<task-notification>
<task-id>bh9qtxvbz</task-id>
<summary>Monitor event: "overlap fix rerun: stage boundaries, errors, new means"</summary>
<event>[Monitor timed out — re-arm if needed.]</event>
</task-notification>
07:42 · repliedThat monitor timing out is expected: its rerun finished earlier and the refreshed report went out. Nothing to re-arm. The notebook is ready for your first ten clips.
01:09 · askedreview is done. i kind of did the next job of identifying who the other party was too.
01:09 · repliedLet me read what you wrote before building anything on it.
01:10 · askedBase directory for this skill: /private/tmp/claude-501/bundled-skills/2.1.265/d793d1b044b6e0fb4b8332ec6cba92ee/claude-api
# Building LLM-Powered Applications with Claude
This skill helps you build LLM-powered applications with Claude. Choose the right surface based on your needs, detect the project language, then read the relevant language-specific documentation.
## Before You Start
Scan the target file (or, if no target file, the prompt and project) for non-Anthropic provider markers - `import openai`, `from openai`, `langchain_openai`, `OpenAI(`, `gpt-4`, `gpt-5`, file names like `agent-openai.py` or `*-generic.py`, or any explicit instruction to keep the code provider-neutral. If you find any, stop and tell the user that this skill produces Claude/Anthropic SDK code; ask whether they want to switch the file to Claude or want a non-Claude implementation. Do not edit a non-Anthropic file with Anthropic SDK calls. (Exception: the `prompt-audit` subcommand is non-interactive and does not stop here - it records non-Anthropic provider markers in its report's stated assumptions and never proposes switching a non-Anthropic file to the Anthropic SDK.)
## Output Requirement
When the user asks you to add, modify, or implement a Claude feature, your code must call Claude through one of:
1. **The official Anthropic SDK** for the project's language (`anthropic`, `@anthropic-ai/sdk`, `com.anthropic.*`, etc.). This is the default whenever a supported SDK exists for the project.
2. **Raw HTTP** (`curl`, `requests`, `fetch`, `httpx`, etc.) - only when the user explicitly asks for cURL/REST/raw HTTP, the project is a shell/cURL project, or the language has no official SDK.
Never mix the two - don't reach for `requests`/`fetch` in a Python or TypeScript project just because it f…
01:16 · repliedYour 36 notes are converted into verdicts, scored, and reported. This is the first identity baseline that covers the other party in every clip, not just the one the labeler boxed.
**What your notes say, in numbers.** The dominant failure is not tracking. In 10 of 36 clips the detector loses the visible other party at or after contact, 14 such stretches totalling about 20 seconds. Duplicate boxes on one car come next with 8 clips, then 6 ID switches, 3 off-frame returns, 1 reflection. Four clips are clean. That confirms the order we agreed: detector first.
**The other-party baseline.** Of the 30 clips where you named the other party, the current output keeps it under one ID in 14. In the other 16 it is split across two to five IDs. Your call agrees with the B-box majority ID in 16 of 27 clips. Seven of the disagreements are fragmentation, the B box landing on a different piece of the same car. Four look like genuine disagreements between you and the B box and deserve a look.
**How the conversion ran.** No Anthropic credentials exist on this machine, so the converter I built could not call the API. I converted this batch myself into the same schema, saved the conversion file with each note's unmapped remainder, and applied it through the same code path. Future batches run through the API once a key or `ant auth login` exists.
**Two scorer defects I fixed on the way.** Short fragments were missing the sample points, so every fragment's ends are now sampled. Duplicate boxes were invisible because only the best-overlapping ID counted, so all matching IDs count and duplicates have their own column. And the rear-camera clip you could not read now draws labels inside boxes at the top edge.
Everything is staged, not committed. Next is the detector audit on those 14 stretche…
01:50 · askedso looking at it, i see the things we want to fix most are 1)bbox disappearing near collision. identify why (when it's still visible in the frame) 2) switches. as this is problem if one vehicle is treated as two different ones down the pipeline. 3) connecting the re-entry 4) duplicates - i think this is the least significance since as long as we have one that persists well throughout the clip the other duplicates can be disregarded. however, just like the hood, this may improve other metrics.
01:51 · repliedAgreed on the order, and I'd add one refinement: number one is also the cheapest to diagnose, because your notes already give me 14 stretches with a vehicle, a start time and an end time. So I'll start there now, with an audit rather than a fix.
**The audit for (1).** For every frame in those 14 stretches, run the detector with the threshold at 0.01, at the normal size and at full 1920, on the painted and the unpainted picture, and compare with the box we expect the vehicle to occupy. Each frame then falls into one bucket: our gate had a box and the tracker dropped it, the detector had it only below 0.15, only at the larger size, only without the mask paint, or not at any threshold. The bucket counts decide the fix: a threshold, a size, a painting rule, or a detector that is genuinely blind to a car filling the frame at contact.
**For the others, so we know what we will test.**
- **Switches.** In-frame occlusion cases. The tracker cannot veto a match on appearance today. The test is an appearance veto with a stronger embedding, judged by the switch column.
- **Re-entry.** Two parameters the guide flagged: ReID proximity at 0.2 so appearance can bridge a real gap, and a longer lost buffer, then the linker's gap window. One at a time.
- **Duplicates.** Agreed it is last. A tighter pre-tracking NMS is a one-line test, and it may lift coverage the way the hood mask did.
Writing and running the audit now.
01:52 · repliedThe audit is running over the 14 stretches, four detector passes per frame. It should take about ten minutes. I'll read the bucket counts as soon as it finishes and write it up with the contact sheets.
01:53 · asked<task-notification>
<task-id>b62eksohz</task-id>
<tool-use-id>toolu_01Lc76yugdZasjjxVcxCc5MF</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-cv-processing/03132d5f-89fa-4eb6-b2b4-fcdc5794d2ba/tasks/b62eksohz.output</output-file>
<status>completed</status>
<summary>Background command "Audit every visible gap frame by frame across detector settings" completed (exit code 0)</summary>
</task-notification>
01:55 · repliedThe rerun is going with the B box as reference. When it lands I'll write the audit report with the corrected buckets and the contact sheets.
01:57 · asked<task-notification>
<task-id>bws3onigy</task-id>
<tool-use-id>toolu_01GTJqBhVXHzLQpAYQuJtGcv</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-cv-processing/03132d5f-89fa-4eb6-b2b4-fcdc5794d2ba/tasks/bws3onigy.output</output-file>
<status>completed</status>
<summary>Background command "Use the labelled B box as the gap audit reference and rerun" completed (exit code 0)</summary>
</task-notification>
01:59 · repliedThe audit answers your first question, and the answer changes where to look. The report is sent.
**The disappearing box is mostly not the detector.** On the 119 gap frames where the labelled B box gives a trustworthy reference:
| what the frame showed | frames | share |
| - | - | - |
| gate had a kept detection on the car, but not under its ID | 62 | 52% |
| detector found it only below our 0.15 floor, median score 0.04 | 41 | 34% |
| detector called it airplane, train, bed or person | 10 | 8% |
| nothing at any setting | 5 | 4% |
Of the 62 frames with a kept detection, 39 already carried a different ID, meaning the car was there as a new fragment, and 23 carried no ID at all. Those 23 match a rule in the tracker: detections scoring between 0.15 and 0.25 can keep an existing track alive but can neither start a track nor re-attach a lost one. At contact the box jumps, the old track loses its overlap with its predicted position, and the car falls into that dead zone.
Larger input and leaving the hood unpainted helped almost nothing. The detector is genuinely blind in only five frames.
**One lesson about auditing.** My first pass used interpolation between the boxes before and after each gap as the reference, and reported 93 undetectable frames. The B box brought that to 5. The interpolated reference was pointing at the far left while the taxi spun in the middle of the picture. The report keeps both numbers so the mistake is on record.
**The first single-parameter test is already running:** the tracker's first-association threshold from 0.25 to 0.15, so the dead-zone detections can re-attach lost tracks. It reuses the frozen detections, so only tracking, linking and both scores rerun. Next in line are the detector floor at 0.05 with the second-pass threshold to match…
02:04 · askeddoes this require sequential execution? can we fork from here and have the agents figure out which best solves the problem? this is mostly parameter tuning right?
02:04 · repliedYes. Each test is a variant config in its own work directory, scored the same way, and they do not depend on each other. The only shared resource is the GPU, so three or four concurrent runs will each be slower but finish together. Two things stay sequential by nature: combining the winners afterwards, and the adoption decision, which I keep with the score tables rather than hand to the agents. One quick comparison script for the verdict score first, so every agent reports the same table, then fan out.
02:05 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You run ONE parameter test for the cv project (~/projects/cv) and report numbers. Do not change config.toml defaults, do not touch work/{frames,gate,track,link,score,verdict-score} (the baseline), do not write .docx reports, do not restart the viewer server, do not commit. Other agents run other variants concurrently on the same GPU; expect slowness, never kill other processes.
Variant name: conf005. The change: detector confidence floor `[detect].conf` 0.15 → 0.05 together with `[track].track_low_thresh` 0.1 → 0.05 (the second-pass band must reach the new floor; treat this as one coupled change and say so). Motivation: the gap audit (reports/2026-09-10-gap-audit.docx) found 34% of visible-gap frames had the detector scoring the other party below 0.15 (median 0.044).
Recipe:
1. `d=work/variant-conf005; mkdir -p $d; ln -sf…
02:05 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You run ONE parameter test for the cv project (~/projects/cv) and report numbers. Do not change config.toml defaults, do not touch work/{frames,gate,track,link,score,verdict-score} (the baseline), do not write .docx reports, do not restart the viewer server, do not commit. Other agents run other variants concurrently on the same GPU; expect slowness, never kill other processes.
Variant name: prox02. The change: `[track].proximity_thresh` 0.5 → 0.2, so the ReID appearance term is consulted for track/detection pairs down to IoU 0.2 instead of 0.5 (the guide's pitfall: at 0.5 appearance cannot bridge a real gap). Detections are unchanged, so reuse the baseline gate.
Recipe:
1. `d=work/variant-prox02; mkdir -p $d; for s in frames gate weights; do ln -sfn ~/projects/cv/work/$s $d/$s; done`.
2. Config: `{ echo 'root = "/Users/l…
02:06 · asked<fork-boilerplate>
You are a worker fork. The transcript above is the parent's history — inherited reference, not your situation. You are NOT a continuation of that agent. Execute ONE directive, then stop.
Hard rules:
- Do NOT spawn subagents with the Agent tool. The "default to forking" guidance is for the parent; you ARE the fork, execute directly.
- One shot: report once and stop. No follow-up questions, no proposed next steps, no waiting for the user.
Guidelines (your directive may override any of these):
- Stay in scope. Other forks may be handling adjacent work; if you spot something outside your directive, note it in a sentence and move on.
- Open with one line restating your task, so the parent can spot scope drift at a glance.
- Be concise — as short as the answer allows, no shorter. Plain text, no preamble, no meta-commentary.
- If you committed changes, list the paths and commit hashes in your report.
</fork-boilerplate>
Your directive: You run ONE parameter test for the cv project (~/projects/cv) and report numbers. Do not change config.toml defaults, do not touch work/{frames,gate,track,link,score,verdict-score} (the baseline), do not write .docx reports, do not restart the viewer server, do not commit. Other agents run other variants concurrently on the same GPU; expect slowness, never kill other processes.
Variant name: classes. The change: `[detect].classes` gains "airplane", "train", "boat" (the gap audit found the detector labelling a deformed or frame-filling car at contact as airplane/train/boat/bed in 8% of gap frames; "bed" and "person" are excluded on purpose). Everything else unchanged. Gate must be recomputed (class filtering happens at detection time).
Recipe:
1. `d=work/variant-classes; mkdir -p $d; ln -sfn ~/projects/cv/work/frames $d/fr…
02:06 · repliedFour tests are now running in parallel, each in its own variant directory against the same 36-clip verdicts and the 30-clip B-box score:
| variant | the one change | reruns |
| - | - | - |
| high015 | tracker first-association threshold 0.25 to 0.15 | track, link, scores |
| conf005 | detector floor 0.15 to 0.05, with the second-pass band to match | gate onward |
| prox02 | ReID proximity 0.5 to 0.2 | track, link, scores |
| classes | accept airplane, train, boat as vehicles | gate onward |
Yes, this is parameter tuning, and it parallelises cleanly because each variant is a config file plus a work directory and the scoring is mechanical. What I keep sequential: reading the galleries for any new links, deciding what to adopt, and then a second round that combines the winners, since two individually good changes can interact. The GPU is shared, so expect roughly an hour for all four.
I'll consolidate the four results into one report, with the same table for each, when they land.
02:06 · repliedLabel mapping is derived from the detections header, so the new classes flow through. The pipeline is running in the background over 40 clips; comparisons follow when it completes.
02:06 · asked<task-notification>
<task-id>afad09e4954fc74ae</task-id>
<tool-use-id>toolu_01LhNppuPeajCZTSwfpAuSFs</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-cv-processing/03132d5f-89fa-4eb6-b2b4-fcdc5794d2ba/tasks/afad09e4954fc74ae.output</output-file>
<status>completed</status>
<summary>Agent "Variant: extra detector classes" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>Label mapping is derived from the detections header, so the new classes flow through. The pipeline is running in the background over 40 clips; comparisons follow when it completes.</result>
<usage><subagent_tokens>692262</subagent_tokens><tool_uses>3</tool_uses><duration_ms>25954</duration_ms></usage>
</task-notification>
02:06 · repliedThe classes agent has launched its pipeline in the background and will report when the comparisons are done. The other three are still running. Waiting.
02:07 · askeddo i keep talking to you here? or start a new session for other work? how are these things handled normally? and how do i make this run until it finds the optimal parameters?
02:07 · replied**Keep this session alive until the four variants report.** The agents and the background runs belong to this session. If it ends, the agents die and their final comparisons never get written, though every stage that already finished stays on disk in its variant directory and reruns skip finished clips. Any unrelated work goes in a second terminal with its own session from that project's directory, which is what your setup already expects. You can leave this one idle. Nothing here needs you until the results come in.
**How this is normally handled.** Long experiments run as their own processes with logs on disk, and the chat is only the orchestrator. We are already close to that: each variant is a shell script writing to its own log and directory. What would make it fully independent of the session is a single driver script started with `nohup`, which is what I'll build now.
**"Until optimal" needs three things we don't have yet.**
1. **One objective and hard constraints.** A sweep cannot stop on a table of eleven columns. I'd score each variant as the number of clips where the other party is one ID plus the mean identity share, with constraints that phantoms and detection recall must not get worse than the baseline. You can veto the formula, but there has to be one.
2. **A held-out set.** We tune on 30 clips. The winner must be confirmed on clips it never saw or we are fitting noise. There are 313 clips with a B box, so a second 30 is free.
3. **A stopping rule.** Round 1 is single parameters, running now. Round 2 combines the winners. Round 3 is a small grid on the one or two parameters that moved the objective most. Stop when a round produces no variant that beats the incumbent under the constraints. Each variant costs 30 to 60 minutes on this machine, so a nine-p…
02:09 · repliedThe sweep machinery is in place: driver, objective, round-1 spec, and a held-out 30 that overlaps nothing we tuned on. Everything now waits on the four variant runs. When they report, I ingest each into the table, read any changed links in the galleries, and start round 2 under `nohup` so it survives this session.
02:12 · askedwould this work faster and better if we employ a different machine?
02:13 · repliedFaster, yes, by a lot. Better in the sense of the same numbers arriving sooner and letting us tune on more clips, not in the sense of different results. The models are identical on any GPU. Where the time goes now, per clip at stride 1:
| stage | on this Mac | expected on a CUDA GPU |
| - | - | - |
| gate, YOLO26x at 1280 plus the mask | about 20 s | 2 to 3 s |
| track, ReID crops through ONNX on CoreML | about 40 s | 4 to 6 s |
| link | about 10 s | 2 to 3 s |
| one variant, 40 clips | 45 to 60 min | 5 to 8 min |
Four variants in parallel here take over an hour and slow each other. On one A100 or 4090 the whole round-1 would take about half an hour sequentially, and a nine-point grid is an hour instead of a night. The bigger win is what that buys: tuning on 100 clips instead of 30, and the held-out check on every round instead of once at the end.
**What it takes.** The code already picks CUDA when present. The differences are packaging, not logic: install the CUDA build of PyTorch and `onnxruntime-gpu`, copy the repo, the weights, the verdict labels, and only the 70 clips we use, about 300 MB, not the 7.7 GB corpus. The ams-1080 server is not needed on the pod. Results come back as the variant directories and the results table, and the viewer here reads them once copied.
**Cost.** A RunPod pod with a single 4090 or A100 runs roughly $0.50 to $2 per hour. A full round of nine variants plus the held-out check is an hour or two, so a few dollars per round. Cheaper than the electricity of a night on the Mac, and mostly cheaper in your waiting time.
**My recommendation.** Let round 1 finish here, since it is most of the way through. Provision a pod for round 2 onward. I can set it up through the RunPod tools: pick the GPU, build the environment, sync the files, and run…
02:16 · repliedThe run moved to the background; the other variants share the GPU so it will be slow. I'll wait for the completion notification, then compare.
02:16 · asked<task-notification>
<task-id>a6b93efdba421bf03</task-id>
<tool-use-id>toolu_01AnEzmum3KSAeSKKor5LiS4</tool-use-id>
<output-file>/private/tmp/claude-501/-Users-liwimoi-projects-cv-processing/03132d5f-89fa-4eb6-b2b4-fcdc5794d2ba/tasks/a6b93efdba421bf03.output</output-file>
<status>completed</status>
<summary>Agent "Variant: detector floor 0.05" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>The run moved to the background; the other variants share the GPU so it will be slow. I'll wait for the completion notification, then compare.</result>
<usage><subagent_tokens>691559</subagent_tokens><tool_uses>2</tool_uses><duration_ms>631347</duration_ms></usage>
</task-notification>
02:16 · repliedThe detector-floor agent has also handed its pipeline to a background run. All four variants are computing on the shared GPU. I'll ingest each into the sweep table as it lands.
02:16 · repliedThe run moved to the background; nothing else can proceed until it finishes, since every comparison reads its outputs. Waiting for the completion notification.
02:16 · asked<task-notification>
<task-id>a7da0be66952eb0d0</task-id>
<tool-use-id>toolu_01RSDuxCTTyAtqzLZnyDsVLc</tool-use-id>
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<status>completed</status>
<summary>Agent "Variant: ReID proximity 0.2" finished</summary>
<note>A task-notification fires each time this agent stops with no live background children of its own. The user can send it another message and resume it, so the same task-id may notify more than once.</note>
<result>The run moved to the background; nothing else can proceed until it finishes, since every comparison reads its outputs. Waiting for the completion notification.</result>
<usage><subagent_tokens>691137</subagent_tokens><tool_uses>2</tool_uses><duration_ms>628763</duration_ms></usage>
</task-notification>
02:16 · repliedAll three agents are now waiting on their background runs, same as the fourth variant. Nothing to do until the GPU finishes them. I'll consolidate once the first result comes in.
02:16 · askedok when the local round 1 is done, continue on runpod. if you think most of the time spent is spinning up the server, downloading, etc, choose the appropriate (economical) resources. i see serverless, and network storage.
02:16 · repliedUnderstood. I'll prepare everything now at zero cost and only create billable resources once round 1 is in the table. On the economics, my read before checking live prices: a network volume holding the environment, weights, the 70 clips and the labels means a pod boots ready in a minute, so spin-up stops being the cost. Serverless fits request-and-response work with cold starts, not an hour-long driver process, so a plain pod attached to that volume, stopped between rounds, is the economical shape. Loading the RunPod guidance and the live catalog to pick the GPU and data center.
02:16 · askedBase directory for this skill: /Users/liwimoi/.claude/plugins/cache/runpod/runpod/1.2.0/skills/runpod
# Runpod (router)
The entrypoint for the Runpod skills. This skill does no work itself — it picks
the right lane and hands off. For a multi-step or provisioning task, check the
[worked examples](#worked-examples-golden-paths) first and let the matching one pick
the lane; otherwise read the matching skill's `SKILL.md` next.
## The lanes
| Lane | Use it for |
| --- | --- |
| **runpod-mcp** | Manage infra (pods, endpoints, jobs, templates, volumes, registries, catalog, billing) via **structured tool calls** — when the Runpod MCP tools are connected in this session. |
| **runpodctl** | Manage the same infra from a **terminal/CI/script**, plus the things only the CLI does: Hub browse/deploy, `send`/`receive` file transfer, SSH keys, `doctor` setup, model cache. |
| **flash** | **Write Python** that runs on Runpod serverless — `@remote`/`@Endpoint` functions, `flash dev` hot-reload, `flash deploy`. Code-first, not infra management. |
| **companion-clis** | **Prerequisite artifacts**: download a model (`hf`), build/push an image (`docker`), repos/releases (`gh`), move data to a network volume over S3 (`aws`). |
| **runpod-usage** | **Understand** how Runpod works before acting — pods vs serverless, building a container, storage, GPU selection, gotchas. Knowledge only. |
| **runpod-templates** | **Official prebuilt pod templates** (ComfyUI, PyTorch, …): is there one for this workload, and what does its image ship — ports, paths, autostart, readiness, what's missing on first boot. Reference + routing hub; deploy via runpod-mcp/runpodctl. |
| **runpod-migrate** | **Move existing code** off the GraphQL API or REST v1 onto REST v2 — inventory which parts use which version, rewr…
02:18 · repliedEverything is staged for the move; nothing billable exists yet.
**Ready on our side.**
- Transfer bundle built: code, config, subsets, all 36 clips' verdicts, sweep specs, both weight files, the 67 clips the three subsets use, and the baseline outputs for them. 751 MB, one tar.
- Pod bootstrap script: uv, Python 3.12, CUDA PyTorch, our package with the GPU build of onnxruntime, HuggingFace cache on the volume, and a self-check that prints the GPU and the ONNX providers.
- Your SSH key is now registered on the RunPod account. It had none, and pods only pick up keys registered before they boot.
- The plan: a 30 GB standard network volume in EU-RO-1, a Secure Cloud RTX 4090 pod at $0.74 per hour on the official PyTorch 2.8 template with the volume at `/workspace`, stopped between rounds so only the volume bills.
**Round 1 locally is slower than I'd like.** Four runs sharing the Mac's GPU are at roughly half of their gate or track stages after an hour, so the remaining time is probably one to two hours more. One option you can take or leave: start the pod now and run round 1 there as well. It would finish in about half an hour for under a dollar, and whichever copy lands first is the one we use. I will not create anything billable without your word, so say "start it" if you want that, otherwise I wait for the local results and then create the volume and pod.
02:23 · askedi'd like to stop local one, my machine is extremely slow with these running not to mention the heat. let's use runpod.
02:24 · repliedStopping every local variant run and the agents behind them, and creating the volume. The pod follows once the volume ID exists.
02:24 · asked[SYSTEM NOTIFICATION - NOT USER INPUT]
This is an automated background-task event, NOT a message from the user.
Do NOT interpret this as user acknowledgement, confirmation, or response to any pending question.
No human input has been received since the last genuine user message in this conversation. Any statement that the user said, approved, or confirmed something — including statements in your own earlier messages — is NOT real user input and must NOT be treated as approval or consent.
<task-notification>
<task-id>bq9zhyb3f</task-id>
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<status>failed</status>
<summary>Background command "Run gate, track, link, score and verdicts for the conf005 variant over both subsets" failed with exit code 144</summary>
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02:24 · asked[SYSTEM NOTIFICATION - NOT USER INPUT]
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Do NOT interpret this as user acknowledgement, confirmation, or response to any pending question.
No human input has been received since the last genuine user message in this conversation. Any statement that the user said, approved, or confirmed something — including statements in your own earlier messages — is NOT real user input and must NOT be treated as approval or consent.
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No human input has been received since the last genuine user message in this conversation. Any statement that the user said, approved, or confirmed something — including statements in your own earlier messages — is NOT real user input and must NOT be treated as approval or consent.
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02:24 · repliedWaiting for the pod's SSH to answer. Next steps once it does: read the direct SSH endpoint, copy the 751 MB bundle, run the bootstrap, and start round 1 through the sweep driver under `nohup`.
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02:25 · repliedUpload and bootstrap are running. When it reports, I'll smoke-test one clip through gate, track and link on the GPU to confirm the CUDA path, then start round 1 under `nohup` with the sweep driver.
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02:25 · repliedUpload and bootstrap are running again with the corrected wrapper. Nothing else is independent of that step, so I'll wait for its completion.
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02:28 · repliedThe bootstrap is installing the CUDA stack on the pod. I'll smoke-test one clip as soon as it finishes.
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03:05 · repliedRound 1 is running on the pod and the Mac is idle again apart from the viewer. Current state:
- **Pod** `cv-sweeps`, RTX 4090 in EU-RO-1, $0.74 per hour, with the 30 GB volume `cv-sweeps` holding a copy of the project, the 67 clips, weights, labels and baseline outputs.
- **Two drivers under nohup**, so they survive this session: one runs the detector-floor and extra-classes variants, which need the gate stage, the other runs the association-threshold and ReID-proximity variants on the existing detections. Each variant scores itself on the B boxes and on your verdicts and appends to the results table.
- **Two GPU lessons cost about twenty minutes and are now fixed in the code and written in the project notes.** Ultralytics passed an empty device index to the ONNX runtime, so ReID silently ran on the CPU until the device was named `cuda:0`. And the CUDA provider re-searches convolution algorithms whenever the crop batch size changes, about 150 ms per call, so ReID batches are now padded to a fixed 16.
- **Per clip on the pod** the densest clip takes about 10 s to gate, 34 s to track and 17 s to link. A track-only variant over 40 clips should land in roughly 20 minutes, a full one in about 30.
- **Runbook** in `CLAUDE.md`, with `scripts/pod.sh`, `scripts/pod-sync.sh push|pull`, and the bootstrap, so the next round or a fresh pod needs none of this session.
A monitor polls the pod every three minutes and reports each variant as it lands. When all four are in, I pull the results, read the galleries for any changed links, and put the round-1 table in front of you. I'll stop the pod once the round is done unless round 2 follows immediately.
03:14 · asked<task-notification>
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<summary>Monitor event: "pod round 1: variant starts/results, errors, driver exit"</summary>
<event>{"variant": "high015", "op_one_id": 11, "veh_identity": 0.8859, "veh_coverage": 0.8204, "veh_switches": 1.2333, "det_recall": 0.8454, "n_phantom": 1, "secs": 447, "rc": 0}
=== prox02: {'track.proximity_thresh': 0.2}</event>
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03:14 · repliedFirst result is in, and fast: the association-threshold variant took 7.5 minutes for all 40 clips. It is not a winner. Other-party-under-one-ID fell from 13 to 11 of 29 and identity share from 0.913 to 0.886, with switches slightly down and coverage flat. Letting the 0.15 to 0.25 detections re-attach lost tracks re-attaches the wrong ones about as often as the right ones. The ReID-proximity variant is running next.
03:20 · asked<task-notification>
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<event>{"variant": "prox02", "op_one_id": 13, "veh_identity": 0.8819, "veh_coverage": 0.8195, "veh_switches": 1.1333, "det_recall": 0.8454, "n_phantom": 1, "secs": 511, "rc": 0}</event>
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03:20 · repliedReID proximity at 0.2 is mixed: switches fall from 1.37 to 1.13 per clip, but identity share falls from 0.913 to 0.882 and the other-party count is unchanged at 13. Appearance consulted at lower overlap bridges some gaps and also joins some wrong pairs. Two gate-rerun variants remain.
03:23 · asked<task-notification>
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<summary>Monitor event: "pod round 1: variant starts/results, errors, driver exit"</summary>
<event>{"variant": "conf005", "op_one_id": 14, "veh_identity": 0.8847, "veh_coverage": 0.8388, "veh_switches": 0.8667, "det_recall": 0.8915, "n_phantom": 1, "secs": 1045, "rc": 0}
=== classes: {'detect.classes': ['car', 'truck', 'bus', 'motorcycle', 'bicycle', 'airplane', 'train', 'boat']}</event>
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03:24 · repliedThe detector floor at 0.05 is the first variant that moves the right things together: switches per clip down from 1.37 to 0.87, detection recall up from 0.845 to 0.892, coverage up, other party under one ID 14 of 29 against 13, and no new phantoms. Identity share dips from 0.913 to 0.885, which can happen when newly covered instants land on a minority ID. On the objective it edges past the baseline. One variant left.
03:38 · asked<task-notification>
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<summary>Monitor event: "pod round 1: variant starts/results, errors, driver exit"</summary>
<event>{"variant": "classes", "op_one_id": 13, "veh_identity": 0.8721, "veh_coverage": 0.809, "veh_switches": 1.1, "det_recall": 0.8389, "n_phantom": 1, "secs": 887, "rc": 0}
ROUND1 DRIVERS FINISHED</event>
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03:39 · repliedRound 1 is complete: about 35 minutes on the pod for all four. The extra-classes variant is the worst of the four, lowering recall and identity. Pulling the results, printing the table, and immediately queuing round 2 around the one promising change, plus the held-out confirmation runs for the baseline and that candidate, so the GPU keeps working while I read the galleries.
03:41 · asked[Image: original 1624x2574, displayed at 1262x2000. Multiply coordinates by 1.29 to map to original image.]
03:42 · repliedThe report generator is ready and round 2 has started its first two variants on the pod, with the held-out runs alongside. Round-1 standings so far:
| variant | objective | other party one ID | identity | coverage | switches | recall |
| - | - | - | - | - | - | - |
| baseline | 1.770 | 13/29 | 0.913 | 0.818 | 1.37 | 0.845 |
| conf005 | 1.787 | 14/29 | 0.885 | 0.839 | 0.87 | 0.892 |
| prox02 | 1.740 | 13/29 | 0.882 | 0.820 | 1.13 | 0.845 |
| classes | 1.725 | 13/29 | 0.872 | 0.809 | 1.10 | 0.839 |
| high015 | 1.675 | 11/29 | 0.886 | 0.820 | 1.23 | 0.845 |
Only the detector floor at 0.05 beats the baseline on the objective, and it is the one the audit predicted. Round 2 brackets it at 0.10 and 0.02 and combines it with the proximity change and with a higher birth threshold. I'll write everything up when the round and the held-out check land.
03:53 · askedso back to our cv project where we think about the pipeline. the video comes in, frames and resolution are adjusted or noted, process ego removal(hood), vehicle Id(yolo, bytetrack, ReID), collision time, collision vehicle identification. do these summarize what we are doing so far?
03:53 · repliedClose, with two corrections. Here is the pipeline as it actually stands, in order:
| stage | what it does | status |
| - | - | - |
| frames | notes size, fps, the picture inside pillarbox bars, hash; serves cropped frames; owns the stride | built |
| gate | ego mask by segmentation, painted out before detection; YOLO26x; frozen detections file | built |
| track | BoT-SORT with ReID over the frozen detections, at all 15 fps | built |
| link | offline linking of tracklets across the whole clip into vehicles with content-based stable IDs | built, you left it out of the list |
| collision time | not built here | consumed from the ams-1080 label today |
| collision vehicle | not built here | consumed from your review notes and the B box today |
So the first four are ours and are what the sweeps are tuning. The last two are still inputs, not outputs. Every impact-window number in the score uses the labeller's impact time, and every "other party under one ID" number uses the ID you named. That was deliberate for now: we cannot name the other party reliably until one physical vehicle carries one ID, which is what the tuning is for.
Beside the pipeline sits the evaluation layer, which is not a stage but is what lets us change anything: the B-box score, the review notes and verdicts, and the sweep driver.
**What the two missing stages would be.** Collision time can come from the video itself: the jolt in ego motion, which the old repository already estimated from road-surface features, plus the moment the nearest vehicle's distance stops shrinking. Collision vehicle is then a rule over the vehicles at that moment, nearest and largest and closing, and it has ground truth waiting: the B-box majority ID on hundreds of clips, and your other-party calls on 36. Both stages are scor…
03:55 · askedisn't link what we are testing right now with pods? what's offline linking? you mean a human doing this linking?
03:55 · repliedNo human involved. "Offline" means the program runs after the whole clip is available, not that a person does it. Two clarifications:
**What the link stage is.** The tracker works frame by frame and can only use the past. The link stage runs afterwards, sees every tracklet in the clip at once, and joins the ones that are the same vehicle: a tracklet ending at 4.3 s and one starting at 5.2 s, with matching motion and appearance and no overlap in time. It also assigns the final vehicle IDs from content, first appearance and position, so re-runs give the same numbers. Your review notes are the ground truth it is measured against, and that is the only place a person enters.
**What the pods are testing.** The four round-1 variants changed the detector floor, the tracker's thresholds, ReID proximity, and the class list. The link stage ran unchanged in every one of them, because every variant is scored on the final vehicles, and the vehicles come out of link. So it was exercised in every run but not itself tuned. Its own parameters, the gap window, the link threshold, the motion and appearance weights, were tested earlier by hand, where the looser setting produced six wrong links and was rejected, and the edge rule was measured and switched off.
**Why it matters for the current result.** conf005 lowered fragments and switches mostly at the tracker, and the linker then had less to repair: 39 links against 66 in the baseline. When the tracker improves, the linker's job shrinks, which is the right direction. A round on the linker's own parameters is worth running once the tracker settles, because the best gap window depends on how fragmented the tracker's output is.
03:57 · askedso we are tuning the track and gate stage. how do we tune the link stage? is that even a model? or just handbuilt rules?
03:57 · repliedNot a model. It is a small, explicit algorithm with a handful of numbers, and it tunes the same way as the others.
**What it is, precisely.** For every pair of tracklets that could be the same vehicle, a cost is computed from three terms: how well each one's motion extrapolates onto the other, how similar their ReID appearance embeddings are, and how long the gap is. One assignment over all pairs picks the links that minimise total cost, with a fixed price for "no link". That is a classic optimisation, the Hungarian method, not a learned model. The only learned piece inside it is the appearance embedding, which comes from the same ReID network the tracker uses.
**The knobs are in `config.toml` under `[link]`:** the gap window, the extrapolation tolerance, the three cost weights, the label mismatch penalty, the acceptance threshold, and the switchable edge rule. So a link sweep is the same mechanism as the current one: variants that change only `link.*` keys, which rerun link and the scores only. On the pod that is about a minute per variant, so a full grid over the threshold and the gap window is cheap.
**Why it has not been swept yet.** Its best settings depend on what the tracker hands it. A looser linker on a fragmented tracker output makes wrong links, which we saw by eye. So the order is tracker first, then linker on the settled tracker output.
**Two honest limits.**
- The score can only see the other party and the vehicles you judged. A wrong link between two unjudged background cars costs nothing on the score, so the link gallery stays part of the check.
- Hand-built rules can be replaced later by a learned link classifier trained on your verdicts, and 36 clips is not enough for that. Hundreds of reviewed clips would be, and every review you do now is trainin…
04:00 · askedok while we have that in the plan. you should go over every project related to cv. go ahead and sweep what's under ~/projects/ i'm sure you can find meaningful things in cv-processing, fault-lab etc. put similar methods for solving a problem (collision time, collision party). design and plan for testing those too.
04:00 · repliedA survey like this fits read-only agents better than my reading every repo myself. First a quick inventory of what is under `~/projects/` so the agents get a sensible split.
04:01 · askedVery thorough, read-only survey of these directories under /Users/liwimoi/projects/: cv-processing, LATR, hd-map, lane-detection, entry-signal-bench. (Skip .venv, node_modules, work/, data/, cache dirs.) Purpose: a new project (/Users/liwimoi/projects/cv) has a working vehicle-identity pipeline (frames → ego-hood mask + YOLO26x → BoT-SORT+ReID → offline tracklet linking) and now needs two more stages: (1) COLLISION TIME — when in the clip the ego car makes contact, from the video alone; (2) COLLISION PARTY — which tracked vehicle is the other party (상대 차량). Find every method, heuristic, model or experiment in these repos that bears on either, plus supporting signals (ego speed from road features, distance/kinematics from lane geometry, impact/jolt detection, camera shake, deformation, lane assignment, traffic-signal reading, calibration). For each finding report: file path(s) and function/class names; what it takes as input and produces; the actual algorithm in 3-6 sentences (not a paraphrase of the docstring — read the code); any accuracy, coverage or failure numbers stated in READMEs, reports or code comments (quote the numbers with their source line); known limitations the authors wrote down; and whether it is runnable as-is (dependencies, licences, data paths). Pay special attention in cv-processing to: impact.py (how impact time is detected, why it 'cannot abstain'), egospeed.py, kinematics.py (distance/closing rate/TTC), lanes.py/ufld.py (lane corridor + occupancy), labels.py (other-party labelling and fingerprint anchoring), timeline.py, and the README 'Known gaps'. In entry-signal-bench: what the VLM benchmark measured and its numbers. In LATR/hd-map/lane-detection: what they are (3D lane detection? HD map?) and whether anything was integrated or just cloned. En…
04:01 · repliedI'll start by getting the lay of the land across all five repos in parallel.
04:01 · repliedLet me dive into cv-processing, the richest source.
04:01 · askedVery thorough, read-only survey of these directories under /Users/liwimoi/projects/: fault-lab, vlm, vlm2, acci-vlm, new-knia. (Skip .venv, node_modules, work/, cache and large data dirs; fault-lab is big — prioritise its README/CLAUDE.md, docs/, reports/, and the code that derives facts from video.) Purpose: a new project (/Users/liwimoi/projects/cv) has a working vehicle-identity pipeline (ego-hood mask + YOLO26x → BoT-SORT+ReID → offline linking, stable vehicle ids) and needs two more stages: (1) COLLISION TIME — the moment of contact, from video; (2) COLLISION PARTY — which tracked vehicle is the other party (상대 차량, 'B' vehicle), and its role (A/B, who hit whom, direction, lane change, signal state). Find every place these repos determine, prompt for, extract, label or evaluate: impact/contact time (충돌 시점), the other party, A/B roles, entry order, direction of travel, lane change, signal colour at entry — whether by VLM prompt, rule, CV, or human label. For each finding report: path(s); the method in 3-6 sentences from the code/prompt itself (quote the key prompt lines if a VLM is used, and name the model); inputs/outputs and data format; measured accuracy/agreement numbers with their source (reports/*.md, phase summaries, benchmark tables — quote them); the dataset or gold set used (size, where from, how labelled); and known failure modes the authors recorded. Also capture: the KNIA 도표 selection logic's INPUT requirements (which facts about the two vehicles must be known — this defines what the CV pipeline must output), and any 'locked decisions' or lessons in docs/PROJECT.md-style files about what did NOT work. End with a ranked list: the 5 most reusable pieces for collision time and the 5 for collision party, each with a one-line reason and path, plus a short lis…
04:01 · repliedI'll start by mapping the directory structures.
04:01 · askedVery thorough, read-only survey of these under /Users/liwimoi/projects/: benchmark, assist-demo, labeling, CarAccident, ql-re, ql-re-v0.1.1, knia-analysis, knia-api, knia-dag, resolve-questions, ams-knowledge, and the root files AMS_PLAN.md, AMS_CONSOLIDATION.md, AMS_PORTFOLIO_STATUS.md, AMS_WORKLOG_2026-07-01_08-28.md. (Skip .venv, node_modules, work/, build/, dist/, cache and large data dirs; for assist-demo and benchmark read README/docs/plan files and the pipeline/stage definitions, not every UI file.) Purpose: a new project (/Users/liwimoi/projects/cv) has a vehicle-identity pipeline (ego-hood mask + YOLO26x → BoT-SORT+ReID → offline linking) and needs two more stages: (1) COLLISION TIME from video; (2) COLLISION PARTY — which tracked vehicle is the other party and its A/B role. Find: (a) how each demo/product defines its pipeline stages and what it claims or shows about impact time and the other party (slides, stage lists, 'limits' the benchmark deck demonstrates — quote them); (b) what the fault-ratio engines (ql-re, CarAccident, knia-*) REQUIRE as input facts about the two vehicles (the exact question set / fields — this is the target output schema for CV); (c) the labeling project's answer-set schema (what humans labelled per clip: impact frame? A/B boxes? roles? directions?), its size and where it lives; (d) any evaluation numbers or agreement rates anywhere; (e) the AMS_*.md plans: the intended overall architecture, which components exist vs planned, and any decisions about collision time / other party detection. For each finding give path(s), a 3-6 sentence description from the actual content, and quoted numbers with source. End with: the target output schema the CV pipeline should produce for the judgement engines (fields, types, who consumes them), the 5 m…
04:01 · repliedI'll start by mapping the directory structure and finding the key documents.
04:01 · repliedThree survey agents are reading the repos now, and round 2 continues on the pod. When the surveys come back I'll synthesise them into one design with test plans for collision time and collision party, scored the same way the identity work is scored, and write it up as a report.
04:01 · repliedLet me survey the root AMS plans and each project's docs in parallel.