MediaPipe is a cross-platform framework for building multimodal machine learning pipelines, primarily targeting on-device inference. It provides a graph-based execution model where modular 'calculators' process typed 'packets' through directed acyclic graphs. The framework includes a rich library of pre-built calculators for image processing, tensor manipulation, audio analysis, and ML inference, as well as high-level task APIs for vision (face/hand/pose landmarking, image classification, segmentation), text (classification, language detection), audio (classification), and generative AI (LLM inference). The architecture is layered: a core C++ framework (mediapipe/framework/) defines the graph runtime, stream handlers, and packet infrastructure; a calculator library (mediapipe/calculators/) provides reusable processing nodes; and task-specific APIs (mediapipe/tasks/) offer consistent interfaces across C++, Java, Python, iOS, Web, and C. The system relies on TensorFlow Lite (LiteRT) for model execution, OpenCV for image operations, protobuf for configuration, and Abseil for utilities. Key entry points include the Python package (mediapipe/__init__.py), the C++ task APIs (mediapipe/tasks/cc/), and the web TypeScript modules (mediapipe/web/). The codebase is large and actively developed, with ongoing migration from older API patterns to newer ones (api2/api3), and some deprecated components (legacy Python solutions, older calculator interfaces).
ADetailer is a script extension for AUTOMATIC1111's Stable Diffusion Web UI that performs automatic object detection and inpainting. After an initial image is generated, it runs detection models (YOLOv8, YOLO World, MediaPipe face detection/mesh) to identify objects such as faces, hands, or persons. It creates masks from the detections and performs targeted inpainting on those regions using separate prompts, denoising strength, and other configurable parameters. The extension supports multiple detection models per run, ControlNet integration for guided inpainting, and various mask preprocessing options (dilation/erosion, offset, merge, invert, filtering by area/confidence). It also provides a REST API for querying version, schema, and available models. Architecturally, ADetailer is a plugin that hooks into the webui's script system. It consists of several internal modules: `adetailer/` contains the core detection logic (ultralytics and mediapipe wrappers), mask processing functions, argument validation using pydantic, and utility functions for model management. The `aaaaaa/` module provides webui-specific helpers for compatibility, UI generation, and rich traceback formatting. The `controlnet_ext/` module handles ControlNet integration for both standard and Forge versions. The main script `scripts/!adetailer.py` orchestrates the entire pipeline: argument parsing, detection, mask preprocessing, and inpainting for each detected object. Key features include: support for multiple detection models simultaneously, configurable per-tab settings, dynamic denoising strength based on bounding box size, optimal crop size calculation for inpainting (SDXL and free modes), XYZ grid support for batch experimentation, and a comprehensive settings panel in the webui. The codebase demonstrates good engineering practices with type hints, pydantic validation, comprehensive tests, and clean separation of concerns.
This repository is a large model zoo (500+ models) focused on converting and quantizing pre-trained computer vision models for edge deployment across multiple frameworks (TFLite, ONNX, OpenVINO, CoreML, TensorRT). It provides hundreds of download scripts for pre-converted model files, conversion pipelines (PyTorch/ TensorFlow to ONNX/TFLite), and a modest set of inference demo scripts for real-time video processing. The repo is primarily a research/engineering resource for edge AI practitioners, not a software application. It exhibits heavy duplication across model directories, relies on an external S3 bucket for model distribution, and uses older framework versions (TensorFlow 1.x/2.x, ONNX opset 11-13). Despite these maintenance concerns, the systematic conversion workflow and post-processing ONNX subgraph generators (e.g., NMS, box decoding) represent reusable patterns for anyone building end-to-end inference pipelines. The repo is hybrid in nature: while classified as data_research due to its focus on pre-trained model artifacts and lack of original training code, it also contains application-like demo code and CLI entry points.