feat: Added stuff

README.md

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-# Face Detection
+# Face Detection and Embedding
 
-Rust programs to do face detection and face embedding
+A high-performance Rust implementation for face detection and face embedding generation using neural networks.
+
+## Overview
+
+This project provides a complete face detection and recognition pipeline with the following capabilities:
+
+- **Face Detection**: Detect faces in images using RetinaFace model
+- **Face Embedding**: Generate face embeddings using FaceNet model
+- **Multiple Backends**: Support for both MNN and ONNX runtime execution
+- **Hardware Acceleration**: Metal, CoreML, and OpenCL support on compatible platforms
+- **Modular Design**: Workspace architecture with reusable components
+
+## Features
+
+- 🔍 **Accurate Face Detection** - Uses RetinaFace model for robust face detection
+- 🧠 **Face Embeddings** - Generate 512-dimensional face embeddings with FaceNet
+- ⚡ **High Performance** - Optimized with hardware acceleration (Metal, CoreML)
+- 🔧 **Flexible Configuration** - Adjustable detection thresholds and NMS parameters
+- 📦 **Modular Architecture** - Reusable components for image processing and bounding boxes
+- 🖼️ **Visual Output** - Draw bounding boxes on detected faces
+
+## Architecture
+
+The project is organized as a Rust workspace with the following components:
+
+- **`detector`** - Main face detection and embedding application
+- **`bounding-box`** - Geometric operations and drawing utilities for bounding boxes
+- **`ndarray-image`** - Conversion utilities between ndarray and image formats
+- **`ndarray-resize`** - Fast image resizing operations on ndarray data
+
+## Models
+
+The project includes pre-trained neural network models:
+
+- **RetinaFace** - Face detection model (`.mnn` and `.onnx` formats)
+- **FaceNet** - Face embedding model (`.mnn` and `.onnx` formats)
+
+## Usage
+
+### Basic Face Detection
+
+```bash
+# Detect faces using MNN backend (default)
+cargo run --release detect path/to/image.jpg
+
+# Detect faces using ONNX Runtime backend
+cargo run --release detect --executor onnx path/to/image.jpg
+
+# Save output with bounding boxes drawn
+cargo run --release detect --output detected.jpg path/to/image.jpg
+
+# Adjust detection sensitivity
+cargo run --release detect --threshold 0.9 --nms-threshold 0.4 path/to/image.jpg
+```
+
+### Backend Selection
+
+The project supports two inference backends:
+
+- **MNN Backend** (default): High-performance inference framework with Metal/CoreML support
+- **ONNX Runtime Backend**: Cross-platform ML inference with broad hardware support
+
+```bash
+# Use MNN backend with Metal acceleration (macOS)
+cargo run --release detect --executor mnn --forward-type metal path/to/image.jpg
+
+# Use ONNX Runtime backend
+cargo run --release detect --executor onnx path/to/image.jpg
+```
+
+### Command Line Options
+
+```bash
+# Face detection with custom parameters
+cargo run --release detect [OPTIONS] <IMAGE>
+
+Options:
+  -m, --model <MODEL>              Custom model path
+  -M, --model-type <MODEL_TYPE>    Model type [default: retina-face]
+  -o, --output <OUTPUT>            Output image path
+  -e, --executor <EXECUTOR>        Inference backend [mnn, onnx]
+  -f, --forward-type <FORWARD_TYPE> MNN execution backend [default: cpu]
+  -t, --threshold <THRESHOLD>      Detection threshold [default: 0.8]
+  -n, --nms-threshold <NMS_THRESHOLD> NMS threshold [default: 0.3]
+```
+
+### Quick Start
+
+```bash
+# Build the project
+cargo build --release
+
+# Run face detection on sample image
+just run
+# or
+cargo run --release detect ./1000066593.jpg
+```
+
+## Hardware Acceleration
+
+### MNN Backend
+
+The MNN backend supports various execution backends:
+
+- **CPU** - Default, works on all platforms
+- **Metal** - macOS GPU acceleration
+- **CoreML** - macOS/iOS neural engine acceleration  
+- **OpenCL** - Cross-platform GPU acceleration
+
+```bash
+# Use Metal acceleration on macOS
+cargo run --release detect --executor mnn --forward-type metal path/to/image.jpg
+
+# Use CoreML on macOS/iOS
+cargo run --release detect --executor mnn --forward-type coreml path/to/image.jpg
+```
+
+### ONNX Runtime Backend
+
+The ONNX Runtime backend automatically selects the best available execution provider based on your system configuration.
+
+## Development
+
+### Prerequisites
+
+- Rust 2024 edition
+- MNN runtime (automatically linked)
+- ONNX runtime (for ONNX backend)
+
+### Building
+
+```bash
+# Standard build
+cargo build
+
+# Release build with optimizations
+cargo build --release
+
+# Run tests
+cargo test
+```
+
+### Project Structure
+
+```
+├── src/
+│   ├── facedet/             # Face detection modules
+│   │   ├── mnn/            # MNN backend implementations
+│   │   ├── ort/            # ONNX Runtime backend implementations
+│   │   └── postprocess.rs  # Shared postprocessing logic
+│   ├── faceembed/          # Face embedding modules
+│   │   ├── mnn/            # MNN backend implementations
+│   │   └── ort/            # ONNX Runtime backend implementations
+│   ├── cli.rs              # Command line interface
+│   └── main.rs             # Application entry point
+├── models/                 # Neural network models (.mnn and .onnx)
+├── bounding-box/           # Bounding box utilities
+├── ndarray-image/          # Image conversion utilities
+└── ndarray-resize/         # Image resizing utilities
+```
+
+### Backend Architecture
+
+The codebase is organized to support multiple inference backends:
+
+- **Common interfaces**: `FaceDetector` and `FaceEmbedder` traits provide unified APIs
+- **Shared postprocessing**: Common logic for anchor generation, NMS, and coordinate decoding
+- **Backend-specific implementations**: Separate modules for MNN and ONNX Runtime
+- **Modular design**: Easy to add new backends by implementing the common traits
+
+## License
+
+MIT License
+
+## Dependencies
+
+Key dependencies include:
+
+- **MNN** - High-performance neural network inference framework (MNN backend)
+- **ONNX Runtime** - Cross-platform ML inference (ORT backend)  
+- **ndarray** - N-dimensional array processing
+- **image** - Image processing and I/O
+- **clap** - Command line argument parsing
+- **bounding-box** - Geometric operations for face detection
+- **error-stack** - Structured error handling
+
+### Backend Status
+
+- ✅ **MNN Backend**: Fully implemented with hardware acceleration support
+- 🚧 **ONNX Runtime Backend**: Framework implemented, inference logic to be completed
+
+*Note: The ORT backend currently provides the framework but requires completion of the inference implementation.*
+
+---
+
+*Built with Rust for maximum performance and safety in computer vision applications.*