andrew
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- config.yaml canonical for stream/models/counting/batch/output/camera - models.modes hold engines+class filters only; conf/iou/min_bbox in detection_params - predict.py derives tracker roles structurally (no per-mode branching) - dashboard mode switch validates + persists atomically to config.yaml - .env keeps secrets/deployment only; zones.json geometry; tracker.yaml hyperparams - batch_mode.json keeps manual/auto batch mode; legacy model_mode ignored w/ warning - src/detection+tracking gain iou param (default 0.7 = no behavior change)
61 lines
3.4 KiB
Markdown
61 lines
3.4 KiB
Markdown
# Model Zoo
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All weights live here. Three formats coexist per model:
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- `.pt` — PyTorch checkpoint (training source, dev, re-export)
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- `.onnx` — ONNX intermediate (export pipeline artifact)
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- `.engine` — TensorRT FP16, **what production loads on the Jetson**
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Class names below are read directly from each checkpoint (`YOLO(path).names`).
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## Inventory
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| File | Classes | Task | Role |
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|---|---|---|---|
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| `truck-detector.{pt,engine}` | `{0: truck}` | bbox | Dedicated truck specialist (ROI, batch lifecycle) |
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| `v4-best.{pt,onnx,engine}` (`v4-best (1).pt` = duplicate copy) | `{0: sack, 1: truck}` | bbox | Combined sack+truck (Modes A/B/C/D truck; Modes A/C sack) |
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| `model_karung_truk.{pt,onnx,engine}` | `{0: sack, 1: truck}` | bbox | Legacy combined alias (auto-pick fallback) |
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| `yolo11n-bbox-100ep-sack+box-20260909-best.{pt,onnx,engine}` | `{0: sack, 1: box}` | bbox | Unified sack+box (Modes B/C/D) |
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| `best.{pt,onnx,engine}` | `{0: sack}` | seg | Sack-only specialist (Mode D sack) |
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| `karung-dimuat-detection-di-feedmill-yolo26n-seg-200e.{pt,onnx,engine}` | `{0: person, 1: sack}` | **seg** | Person-exclusion seg model (legacy `predict_new.py`) |
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## Model Modes (`predict.py --model-mode` / `config.yaml models.active_mode`, default **C**)
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Modes are DATA in `config.yaml` `models.modes` — each preset declares engines
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(path key + contributed classes) and class filters only. Per-class
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conf/iou/min_bbox live in `models.detection_params` (shared across modes).
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Add E/F/... in YAML with no code change; `predict.py` derives tracker roles
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structurally and the dashboard lists new modes automatically.
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Counting filters by **class name, not ID** (`BaseDetector(class_filter)` in
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`src/detection.py`; tracker allow-list in `src/tracking.py`; dual counters in
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`src/counting.py`). "Filtered out" = class exists in the checkpoint but is
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dropped before tracking/counting.
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| Mode | Truck detector (filter) | Sack detector (filter) | Box detector (filter) |
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|---|---|---|---|
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| **A** | `v4-best.engine` → keep `truck`, drop `sack` | `v4-best.engine` (shared) → keep `sack`, drop `truck` | — (no box counting) |
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| **B** | `v4-best.engine` → keep `truck`, drop `sack` | `yolo11n-….engine` → keep `sack`, drop `box` | `yolo11n-….engine` (shared with sack) → keep `box`, drop `sack` |
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| **C** (default) | `v4-best.engine` → keep `truck`, drop `sack` | `v4-best.engine` (shared) → keep `sack`, drop `truck` | `yolo11n-….engine` → keep `box`, drop `sack` |
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| **D** | `v4-best.engine` → keep `truck`, drop `sack` | `best.engine` → `sack` only (nothing to drop) | `yolo11n-….engine` → keep `box`, drop `sack` |
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Notes:
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- Mode B loads **one** yolo11n handle shared by the sack and box paths (single
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tracker, single track-ID space); modes C/D run a dedicated box tracker
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(separate ID space + own stabilizer, so sack/box IDs never collide).
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- All `.engine` files verified to coexist: 2 engines ~16 MB, 3 engines ~24 MB
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peak of 7.6 GB Jetson GPU. If `.pt` is ever used, load PyTorch models **before**
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TensorRT engines or CUDA init fails.
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- Mode switches via dashboard persist to `batch_mode.json` and apply on next
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`karung-counter` restart (models load once at startup).
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## Exporting
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```bash
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python export_model.py models/<name>.pt # → FP16 .engine next to the .pt
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```
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## Deploy
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`deploy_to_jetson.py` syncs the `.engine` files only (what production loads);
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`.pt`/`.onnx` stay local for dev/export.
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