Init readme

README.md

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+<p>
+  <img src="./assets/open-image-models-logo.png" alt="Open Image Models Logo" width="400"/>
+</p>
+
+# Fast & Lightweight License Plate OCR
+
+[![Actions status](https://github.com/ankandrew/fast-plate-ocr/actions/workflows/main.yaml/badge.svg)](https://github.com/ankandrew/fast-plate-ocr/actions)
+[![Keras 3](https://img.shields.io/badge/Keras-3-red?logo=keras&logoColor=red&labelColor=white)](https://keras.io/keras_3/)
+[![image](https://img.shields.io/pypi/v/fast-plate-ocr.svg)](https://pypi.python.org/pypi/fast-plate-ocr)
+[![image](https://img.shields.io/pypi/pyversions/fast-plate-ocr.svg)](https://pypi.python.org/pypi/fast-plate-ocr)
+[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
+[![Pylint](https://img.shields.io/badge/linting-pylint-yellowgreen)](https://github.com/pylint-dev/pylint)
+[![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)
+[![image](https://img.shields.io/pypi/l/fast-plate-ocr.svg)](https://pypi.python.org/pypi/fast-plate-ocr)
+
+<!-- TOC -->
+* [Fast & Lightweight License Plate OCR](#fast--lightweight-license-plate-ocr)
+  * [Introduction](#introduction)
+  * [Features](#features)
+  * [Available Models](#available-models)
+    * [Object Detection](#object-detection)
+      * [Plate Detection](#plate-detection)
+    * [Contributing](#contributing)
+<!-- TOC -->
+
+---
+
+## Introduction
+
+We aim to provide a variety of **pre-trained** models for different **computer vision** tasks, such as object detection and
+image classification, that can be used **out-of-the-box** for **fast** inference using ONNX. These models are optimized for
+performance and accuracy across various image sizes and tasks.
+
+Models found here can easily be integrated into your applications for real-time processing, making them ideal for
+deployment in edge devices, cloud environments, or production systems.
+
+## Features
+
+- 🚀 Pre-trained Models: Models are **ready** for immediate use, no additional training required.
+- 🔄 ONNX Format: Cross-platform support for **fast inference** on both CPU and GPU environments.
+- ⚡ High Performance: Optimized for both speed and accuracy, ensuring efficient **real-time** applications.
+- 📏 Variety of Image Sizes: Models **available** with different input sizes, allowing flexibility based on the task's
+  performance and speed requirements.
+- 📊 Evaluation Metrics: Precision, Recall, mAP50, and mAP50-95 metrics are provided for each model, helping users select
+  the most appropriate model for their needs.
+
+## Available Models
+
+### Object Detection
+
+#### Plate Detection
+
+| Model    | Image Size | Precision (P) | Recall (R) | mAP50 | mAP50-95 | Speed (ms) |
+|----------|------------|---------------|------------|-------|----------|------------|
+| yolov9-t | 640        | 0.955         | 0.91       | 0.959 | 0.75     | XXX        |
+| yolov9-t | 512        | 0.948         | 0.901      | 0.95  | 0.718    | XXX        |
+| yolov9-t | 384        | 0.943         | 0.863      | 0.921 | 0.688    | XXX        |
+| yolov9-t | 256        | 0.937         | 0.797      | 0.858 | 0.606    | XXX        |
+
+_<sup>[1]</sup> Inference on Mac M1 chip using CPUExecutionProvider. Utilizing CoreMLExecutionProvider accelerates speed
+by 5x._
+
+### Contributing
+
+Contributions to the repo are greatly appreciated. Whether it's bug fixes, feature enhancements, or new models,
+your contributions are warmly welcomed.
+
+To start contributing or to begin development, you can follow these steps:
+
+1. Clone repo
+    ```shell
+    git clone https://github.com/ankandrew/fast-plate-ocr.git
+    ```
+2. Install all dependencies using [Poetry](https://python-poetry.org/docs/#installation):
+    ```shell
+    poetry install --all-extras
+    ```
+3. To ensure your changes pass linting and tests before submitting a PR:
+    ```shell
+    make checks
+    ```