This repository is a collection of C++ sample programs for deploying deep learning models on AXERA AI SoCs (NPU chips). It provides ready-to-run inference examples for a wide range of computer vision tasks including object detection (YOLOv5/6/7/8/9/10/11/26, YOLOX, YOLO-World, PPYOLOE+, RT-DETR, SCRFD), instance segmentation, oriented object detection (OBB), pose estimation, depth estimation, semantic segmentation, stereo matching, and more. Each example is a standalone command-line executable that loads a pre-compiled .axmodel, runs inference via the AX_ENGINE API, post-processes the outputs, and saves a result image. The code is organized per target chip (ax650, ax637, ax620e, ax615) with near-identical copies of examples across platforms, differing only in system initialization and minor chip-specific logic. The architecture is not a library or framework but a set of reference implementations demonstrating the integration of AXERA's NPU SDK. The primary data flow is: load image → preprocess (letterbox resize, color conversion) → push to engine → run synchronous inference → decode model outputs (e.g., anchor-based proposals, DFL, NMS) → draw bounding boxes/keypoints/masks → save output. External dependencies include the AX_ENGINE runtime, OpenCV for image processing, and standard C++ libraries. The build system uses CMake with per-chip toolchain files and GitHub Actions CI for cross-compilation verification. While the code is functional and covers a broad model zoo, it exhibits significant duplication across chip directories and hardcoded configuration (thresholds, image sizes, class names). The monolithic detection.hpp (~3500 lines) bundles many model-specific post-processing functions, increasing maintenance burden. There are no automated tests, and manual memory management is prevalent. Despite these quality issues, the repository serves its purpose as a practical reference for developers integrating AXERA hardware, and it stays current with recent model releases (YOLOv13, YOLO26, Depth Anything V2).
MetaSeg is a Python library that packages Facebook's Segment Anything Model (SAM) into an easy-to-use interface for automatic and manual image/video segmentation. It provides high-level predictors (SegAutoMaskPredictor, SegManualMaskPredictor) that handle model loading, inference, and output visualization. The library integrates with SAHI for sliced inference and FalAI for cloud GPU execution, and includes a Gradio web application. The codebase is primarily a thin orchestration layer over the official SAM implementation, with additional utility functions for data loading, video processing, and model download. It is designed for developers who want to quickly apply SAM without deep model internals.