YOLOv5 is a production-oriented PyTorch framework for object detection, instance segmentation, and image classification, with a hybrid research/educational flavour from its tutorial notebooks. The dominant character is application: the repository provides CLI tools (`train.py`, `val.py`, `detect.py`, `export.py`, plus `segment/` and `classify/` variants), a Flask REST API, Docker images, PyTorch Hub support, CI smoke tests, benchmarking scripts, and experiment-tracking integrations for W&B, ClearML, and Comet. It is not just an experiment repo; it is designed to be deployed and exported to edge and cloud inference targets. Core architecture is config-driven: model YAMLs are parsed by `models/yolo.py` into `DetectionModel`, `SegmentationModel`, or `ClassificationModel` geometries, while `models/common.py` holds reusable layers and the central `DetectMultiBackend` inference wrapper for PyTorch, TorchScript, ONNX Runtime, OpenVINO, TensorRT, CoreML, TensorFlow, TFLite/Edge TPU, Paddle, and Triton. The primary data flow is dataset YAML -> dataloaders with augmentation (`utils/augmentations.py`) -> model forward -> task-specific loss (`utils/loss.py`, `utils/segment/loss.py`) -> train/val loops. `export.py` converts trained checkpoints into 12+ deployment formats using a decorated `@try_export` pattern, and `data/` embeds dataset download/conversion scripts for COCO, ImageNet subsets, VOC, VisDrone, and others. Maintenance is active, but the codebase sits in a legacy/support position relative to the unified `ultralytics` package. Technical drift is medium: some loggers are deprecated, many `models/hub/` configs are experimental, and the code depends heavily on the external `ultralytics` package, which creates version-compatibility risk. The code is generally clean and modular, though it has task-level duplication across detection, segmentation, and classification paths and limited unit test coverage outside smoke tests.
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.