andrew/karung-counting-feedmill-semarang

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feat: unified config.yaml with extensible model presets (A-D data-driven, E/F ready)

- 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)

2026-09-17 11:44:45 +07:00

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Model Zoo

All weights live here. Three formats coexist per model:

.pt — PyTorch checkpoint (training source, dev, re-export)
.onnx — ONNX intermediate (export pipeline artifact)
.engine — TensorRT FP16, what production loads on the Jetson

Class names below are read directly from each checkpoint (YOLO(path).names).

Inventory

File	Classes	Task	Role
`truck-detector.{pt,engine}`	`{0: truck}`	bbox	Dedicated truck specialist (ROI, batch lifecycle)
`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)
`model_karung_truk.{pt,onnx,engine}`	`{0: sack, 1: truck}`	bbox	Legacy combined alias (auto-pick fallback)
`yolo11n-bbox-100ep-sack+box-20260909-best.{pt,onnx,engine}`	`{0: sack, 1: box}`	bbox	Unified sack+box (Modes B/C/D)
`best.{pt,onnx,engine}`	`{0: sack}`	seg	Sack-only specialist (Mode D sack)
`karung-dimuat-detection-di-feedmill-yolo26n-seg-200e.{pt,onnx,engine}`	`{0: person, 1: sack}`	seg	Person-exclusion seg model (legacy `predict_new.py`)

Model Modes (`predict.py --model-mode` / `config.yaml models.active_mode`, default C)

Modes are DATA in config.yaml models.modes — each preset declares engines (path key + contributed classes) and class filters only. Per-class conf/iou/min_bbox live in models.detection_params (shared across modes). Add E/F/... in YAML with no code change; predict.py derives tracker roles structurally and the dashboard lists new modes automatically.

Counting filters by class name, not ID (BaseDetector(class_filter) in src/detection.py; tracker allow-list in src/tracking.py; dual counters in src/counting.py). "Filtered out" = class exists in the checkpoint but is dropped before tracking/counting.

Mode	Truck detector (filter)	Sack detector (filter)	Box detector (filter)
A	`v4-best.engine` → keep `truck`, drop `sack`	`v4-best.engine` (shared) → keep `sack`, drop `truck`	— (no box counting)
B	`v4-best.engine` → keep `truck`, drop `sack`	`yolo11n-….engine` → keep `sack`, drop `box`	`yolo11n-….engine` (shared with sack) → keep `box`, drop `sack`
C (default)	`v4-best.engine` → keep `truck`, drop `sack`	`v4-best.engine` (shared) → keep `sack`, drop `truck`	`yolo11n-….engine` → keep `box`, drop `sack`
D	`v4-best.engine` → keep `truck`, drop `sack`	`best.engine` → `sack` only (nothing to drop)	`yolo11n-….engine` → keep `box`, drop `sack`

Notes:

Mode B loads one yolo11n handle shared by the sack and box paths (single tracker, single track-ID space); modes C/D run a dedicated box tracker (separate ID space + own stabilizer, so sack/box IDs never collide).
All .engine files verified to coexist: 2 engines ~16 MB, 3 engines ~24 MB peak of 7.6 GB Jetson GPU. If .pt is ever used, load PyTorch models before TensorRT engines or CUDA init fails.
Mode switches via dashboard persist to batch_mode.json and apply on next karung-counter restart (models load once at startup).

Exporting

python export_model.py models/<name>.pt   # → FP16 .engine next to the .pt

Deploy

deploy_to_jetson.py syncs the .engine files only (what production loads); .pt/.onnx stay local for dev/export.

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