Models and Resources

This document describes the model and resource organization that can be confirmed in the current repository.

Resource Directories

Directory	Purpose
data/resource/aiboxresource_bm1688	Resources for the Sophon BM1688 release package.
data/resource/aiboxresource_cv186x	Resources for the Sophon CV186X release package.
data/resource/aiboxresource_x86	Resources for the x86 Docker / CPU backend.

The resource directory is selected through RESOURCE_DIR at build time.

Model Templates

Model templates are located at:

data/resource/*/model_template

The templates currently visible include:

• YOLO detection templates
• YOLO classification templates
• DINO
• SAM2
• feature
• keypoints
• Qwen3 / Qwen3VL
• OCR

License-Plate OCR Package

OCR recognizes plates already located by landmarks; it does not perform full-image text detection. Install the model package through device resources. This repository provides only the ocr.json template and never includes weights or private dictionaries.

Each external OCR package must contain:

• config.json with "model_type": "ocr";
• the current-platform model file (.nn for Sophon, .onnx for x86);
• the .txt character table named by its configuration.

models[0].params must explicitly bind the table and CTC mapping: character_table_file must name a root-level .txt file, ctc_blank_index identifies the blank class, ctc_prepend_tokens and ctc_append_tokens define additional leading and trailing model classes, and ctc_class_count must equal the model output's final dimension. The table plus leading and trailing tokens must match CTC indices exactly; a mismatch fails OCR initialization or inference instead of silently dropping characters. The Add Model wizard requires both the OCR model and its character table and generates these fields.

Legacy license-plate model 2000007 uses 6,625 classes with a 6,624-line table: entry zero is blank and the configuration appends one ASCII space for the final class. Decoding removes blank and consecutive duplicate classes.

A PP-OCR Chinese recognition model may use the standard 6,623-entry table without blank. For a 6,625-class output, the system explicitly prepends blank and appends an ASCII space.

Connect the scene nodes as “plate detection/association → four-point plate landmark → text recognition → event report”. OCR accepts only a strict four-point plate quadrilateral in top-left, bottom-left, bottom-right, top-right order; empty recognition results do not produce an alarm. In the event-report node, select the Vehicle Property alarm attribute to expose plateSrc, plate, and the rectified plate crop in the event.

Layout and Components

Resource layout files include:

data/resource/*/layout/modelComponents.json
data/resource/*/layout/actions.json
data/resource/*/layout/linkageStorages.json

These files affect the frontend configuration items, model component parameters, action nodes, and linkage strategies.

Algorithm Templates

Algorithm templates are located at:

data/resource/*/algorithm_template

Templates for vision-language models, DINO, YOLO, and related models are visible here. For an official release, it is recommended to generate a "template catalog table" from the current resource directory to avoid a hand-written list going stale.

x86 and Sophon Differences

x86 path:

data/resource/aiboxresource_x86

Sophon path:

data/resource/aiboxresource_bm1688
data/resource/aiboxresource_cv186x

The code shows the differences between handling x86 ONNX files and Sophon model packages. The full model porting workflow should be re-validated against the currently releasable model packages.

Resource Licensing Notes

The resource tree contains model, algorithm, and layout templates as well as selected public example weights and chip-converted artifacts. The repository's Apache-2.0 source-code license does not automatically cover model files; use the bundle manifest and directory-level license as the authority:

• Prebuilt components in the prebuild/ directory require separate distribution-license review.
• Ultralytics YOLOv8 chip artifacts under data/resource/aiboxresource_rknn/model-artifacts/ are AGPL-3.0 community examples, not commercial model deliverables.
• Commercial or proprietary models use independent source, weight, training, and license records and do not reuse the community example bundle identity.
• Whether the model encryption feature is included in the current build depends on the CMake option COSMO_MODEL_GUARD.
• If you introduce models from a third-party model ecosystem, follow the license requirements of the corresponding models.