track models/*.pt + *.onnx (clone-ready); keep *.engine gitignored (rebuildable)
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@@ -26,19 +26,21 @@ output/
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*.tmp.*
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*.log
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# Media & large model binaries
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# Media (videos/images stay local)
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*.mp4
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*.avi
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*.mkv
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*.jpg
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*.jpeg
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*.png
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*.engine
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*.onnx
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*.pt
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*.zip
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*.tar.gz
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# TensorRT engines are Jetson build artifacts — rebuild via:
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# python export_model.py models/<name>.pt
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# .pt (PyTorch) and .onnx ARE tracked so `git clone` is self-sufficient.
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*.engine
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# Test & coverage artifacts
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.pytest_cache/
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.coverage
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@@ -34,7 +34,8 @@ Indonesian (`karung`=sack, `truk`=truck); YOLO class names are English
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tracker keeps `("sack", "truck", "box")` (`src/tracking.py`); counting uses
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`MultiClassLineCounter` = dual `LineCrossCounter`s on one shared line
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(`src/counting.py`). Same line geometry + 30px dedup for sacks and boxes.
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- All weights live in `models/` (untracked; per-mode detector/filter matrix in
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- All weights live in `models/` (`.pt`/`.onnx` tracked; `.engine` gitignored —
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rebuild via `export_model.py`; per-mode detector/filter matrix in
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`models/README.md`). Verified classes: `truck-detector`={truck},
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`model_karung_truk`/`v4-best`={sack,truck}, `karung-dimuat-*-seg-200e`=
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{person,sack} (seg; persons drawn, never counted), `best`={sack},
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@@ -49,9 +50,9 @@ Indonesian (`karung`=sack, `truk`=truck); YOLO class names are English
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Deps: `requirements.txt` (unpinned; **never** pip-install torch from PyPI on the
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Jetson — use the NVIDIA wheels already on device). For visual checks run on a video
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file (`--source`), not the live RTSP.
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- **Don't commit binaries/state**: `.gitignore` excludes `*.pt/*.onnx/*.engine`,
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`*.mp4/*.jpg/*.png`, `*.db`, `.env`, `batch_history_folder/`. Large weights and
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videos already sit untracked in the working tree — leave them alone.
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- **Don't commit state**: `.gitignore` excludes `*.engine` (rebuildable),
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`*.mp4/*.jpg/*.png`, `*.db`, `.env`, `batch_history_folder/`. Videos and local
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DBs already sit untracked in the working tree — leave them alone.
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## Deploy (Jetson `192.168.192.96`, user `jetson`)
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