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- CHANGELOG.md (Keep-a-Changelog, dated entries from git history) - README: production pipeline framing, config table, layout, flags, changelog link - configuration.md: legacy env overrides, zones geometry-only, archive paths - scripts.md: current flags, correct archive/ paths, tracked vs ignored weights - deployment.md: config.yaml in sync list, model_mode location, /api/model-modes - models.md + architecture.md: config.yaml pointers, deprecated src/config.py
75 lines
4.4 KiB
Markdown
75 lines
4.4 KiB
Markdown
# Models
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Class names below are read directly from each checkpoint (`YOLO(path).names`).
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Formats present: `.pt` (PyTorch) → `.onnx` (ONNX) → `.engine` (TensorRT FP16 for Jetson;
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see `export_model.py` / `export_v4.py`).
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## Inventory
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All weights live in `models/` — full per-mode detector/filter matrix in
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[`models/modelREADME.md`](../models/modelREADME.md).
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| File | Classes | Task | Used by |
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|---|---|---|---|
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| `models/truck-detector.{pt,engine}` | `{0: truck}` | bbox | Dedicated truck specialist; `src/config.py` default truck model |
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| `models/model_karung_truk.{pt,onnx,engine}` | `{0: sack, 1: truck}` | bbox | Legacy combined alias (auto-pick fallback in `predict.py`) |
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| `models/v4-best.{pt,onnx,engine}` (`v4-best (1).pt` = duplicate copy) | `{0: sack, 1: truck}` | bbox | Combined model (Modes A/B/C/D truck; Modes A/C sack) |
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| `models/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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| `models/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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| `models/best.{pt,onnx,engine}` | `{0: sack}` | seg | Sack-only specialist (Mode D sack; `src/config.py` default) |
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Model registry for the `rpo_iki` engine: `rpo_iki/configs/model_registry.json`
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(currently pins `karung-dimuat-seg-200e`, mAP50-mask 0.899, val MAE 0.67).
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## Support matrix
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### 1. Multiple specialized models — YES (current design)
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Two dedicated models run in parallel on the same frames:
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- **Truck model** (`truck-detector.pt`) → presence, ROI, counting-line placement,
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batch lifecycle. Runs every 15th frame in `src/main.py:130-145`.
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- **Sack model** (seg or combined) → per-frame track + count (`src/main.py:163-169`).
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- `predict_new.py:184-185` hardcodes the same duo (`TRUCK_MODEL_PATH`, `SACK_MODEL_PATH`);
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`rpo_iki/count.py:1136-1143` prefers a dedicated `truck-detector.pt` for dynamic
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truck calibration.
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### 2. Multiple classes within a single model — YES (filter-then-count)
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- Combined models expose 2 classes (`sack`+`truck`, `person`+`sack`, `sack`+`box`).
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- Consumers select what they need and ignore the rest:
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- `SackDetector._parse` (`src/detection.py:34`): keeps `name == "sack"` only.
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- `ByteTrackTracker._parse` (`src/tracking.py:62`): keeps `("sack", "truck")`.
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- `predict_new.py:379-389,571-579`: auto-detects karung/person class IDs by name
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substring, tracks `classes=[person, sack]`, draws persons in red and skips them
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in counting.
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- `rpo_iki/count.py` (`SACK_CLASS_ID = 1`): counts one class; falls back to the same
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model's `truck_class_id=2` when no dedicated truck model exists.
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- Rule of thumb: **only the sack/karung class is ever counted**; other classes are
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auxiliary (ROI, visualization, exclusion).
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### 3. Hybrid multi-model — YES (modes A/B/C/D, default C)
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- `predict.py --model-mode` (or `MODEL_MODE` env / dashboard `/api/batch/mode`):
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| Mode | Truck | Sack | Box |
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| A | v4 (sack+truck) | v4 | — |
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| B | v4 (truck-only) | yolo11n | yolo11n |
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| C (default) | v4 (sack+truck) | v4 | yolo11n (box-only) | | D | v4 (truck-only) | best (sack-only) | yolo11n (box-only) |
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- All weights load as `.engine` (verified coexist: 2 engines ~16 MB, 3 engines
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~24 MB peak of 7.6 GB). Dashboard switches persist to `batch_mode.json` and
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apply on next service restart (models load once at startup).
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- Counting: `MultiClassLineCounter` = dual `LineCrossCounter`s on one shared line;
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events tagged with `class_name`; sack and box track IDs live in separate spaces.
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## Choosing / swapping a model
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- `src/` pipeline: `MODEL_SACK_PATH` / `MODEL_TRUCK_PATH` env vars (defaults:
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`./models/best.engine` / `./models/truck-detector.engine`; see
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[`configuration.md`](configuration.md)) or `--source` for files.
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- `predict.py`: `config.yaml` `models.paths` is canonical (per-mode presets in
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`models.modes`, per-class conf/iou/min_bbox in `models.detection_params`).
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Legacy overrides still work: `MODEL_PATH` env or `--model` (single v4 file),
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`--box-model` (yolo11n weights), `--model-mode` / deprecated `MODEL_MODE` env
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for the preset, `--sack-conf` / `--truck-conf` / `--box-conf` for thresholds.
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- TensorRT: `python export_model.py models/<name>.pt` (FP16 `.engine`) on the Jetson;
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production loads `.engine` only (missing `.engine` falls back to the `.pt`
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sibling with a warning). `deploy_to_jetson.py` syncs `config.yaml` + the `.engine` files.
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