build 0.10.0 · Windows x64 · Nim · ONNX Runtime (CPU / OpenVINO / CUDA via settings.json) · line station

Fruit Inspector is conveyor inspection: the fruit moves on the belt; quality is graded only in the control zone, not anywhere in the frame.

EdgeInfer: https://github.com/olesha-ai/edgeinfer-eval

Test bench: Intel i5-11400, 32 GB RAM, no discrete GPU, USB camera 1920×1080.

ORT CPU EP: infer loop on the order of ~100–130 fps (see speed.jpg).

Camera preview ~30 fps — camera limit, not infer.

On the line you do not grade an apple “wherever it happens to be in the frame”. You grade it in the control zone — a fixed spot on the belt.

The image uses a 3×3 grid. The centre cell (C) is the control zone:

- detect, track, and stable IDs on the line (up to two apples for now);

- focus — the apple closest to cell C; after it leaves C — sticky focus on the next ID; a new detection does not steal focus;

- Cat (LightGBM) — only while the focus apple’s centre is inside C;

- pipeline: YOLOX (640) → ROI refine (256) → 44 features (fruit_features) → Cat → GOOD / BAD on screen.

- no full-frame chase along the belt — we work cell C and an ID queue;

- while the apple is moving toward C — detect and track only, Cat is not run;

- in C — feature collection and verdict;

- left C — sticky on the next ID, repeat.

On the desk, Cat starts as soon as the apple enters C. On a live belt that is not the final story: the fruit can still move, roll, bounce — the frame is already in the zone, but the object is not ready to grade. On the factory floor the moment is different: stop, lying still, ready to capture.

trig is groundwork for that. Not aim-by-coordinates, but a station signal: object ready — grade it. The inspector sends the verdict over the network (GOOD / BAD and ID); trig shows the result and will become a line command over time: stop the section → signal the model → get the grade. The archive already includes trig as a LAN client; the full chain “ready → command → Cat” is the next step.

- Nim, ImGui (GLFW)

- ONNX Runtime — provider in settings.json:cpu,openvino,cuda

- YOLOX-nano + LightGBM (Cat ONNX)

- fruit_features.dll+- features.jsonnext to Cat

- apple crop pack: model/apple/det.onnx,cat.onnx,features.json

- Fruit_Inspector.exe

- fruit_features.dll

- lib/cpu/,- lib/openvino/(and- lib/cuda/if included)

- model/apple/

- settings.json

- trig.exe— LAN client (see trig)

- licenses/

The file sits next to the exe. Values in the archive may be from my desk — use your own.

Engine: ort_provider, ort_intra_threads, ort_inter_threads, openvino_* (when using OpenVINO).

Models and camera: crop_pack, model, cat_model, camera_index, conf, iou, infer_main_tensor_size, infer_refine_tensor_size.

Output and logs: ip_addr, ip_port, memory_diag_enabled.

Edit the file by hand or use Settings → Apply.

- Unpack locally.

- Keep lib/,model/,fruit_features.dll, andsettings.jsonnext toFruit_Inspector.exe.

- Set camera_indexandort_provider.

- Run from cmd if you want [infer]fps lines on stdout.

- START / STOP in the UI; Esc asks before quit.

Testing, teaching, research, feedback — yes.

Commercial production under this license — no.

Full text: LICENSE.md, licenses/Fruit_Inspector_LICENSE.md.

Third-party components: licenses/THIRD_PARTY.md. Do not remove the licenses/ folder from the zip.

Commercial contact: olesha-ai.

Intel, OpenVINO, Microsoft, Megvii, and other names are trademarks of their respective owners.

olesha-ai