{"id":16392488,"url":"https://github.com/ankandrew/open-image-models","last_synced_at":"2025-06-10T13:04:56.410Z","repository":{"id":257804948,"uuid":"860651367","full_name":"ankandrew/open-image-models","owner":"ankandrew","description":"Pre-trained image models using ONNX for fast, out-of-the-box 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Open Image Models\n\n[![Actions status](https://github.com/ankandrew/open-image-models/actions/workflows/main.yaml/badge.svg)](https://github.com/ankandrew/open-image-models/actions)\n[![GitHub version](https://img.shields.io/github/v/release/ankandrew/open-image-models)](https://github.com/ankandrew/open-image-models/releases)\n[![image](https://img.shields.io/pypi/pyversions/open-image-models.svg)](https://pypi.python.org/pypi/open-image-models)\n[![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)\n[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg)](https://ankandrew.github.io/open-image-models/)\n[![Pylint](https://img.shields.io/badge/linting-pylint-yellowgreen)](https://github.com/pylint-dev/pylint)\n[![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)\n[![ONNX Model](https://img.shields.io/badge/model-ONNX-blue?logo=onnx\u0026logoColor=white)](https://onnx.ai/)\n[![Hugging Face Spaces](https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-orange)](https://huggingface.co/spaces/ankandrew/open-image-models)\n![License](https://img.shields.io/github/license/ankandrew/open-image-models)\n\n\u003c!-- TOC --\u003e\n* [Open Image Models](#open-image-models)\n  * [Introduction](#introduction)\n  * [Features](#features)\n  * [Installation](#installation)\n  * [Available Models](#available-models)\n    * [Object Detection](#object-detection)\n      * [Plate Detection](#plate-detection)\n  * [Contributing](#contributing)\n  * [Citation](#citation)\n\u003c!-- TOC --\u003e\n\n---\n\n## Introduction\n\n**Ready-to-use** models for a range of **computer vision** tasks like **detection**, **classification**, and\n**more**. With **ONNX** support, you get **fast** and **accurate** results right out of the box.\n\nEasily integrate these models into your apps for **real-time** processing—ideal for edge devices, cloud setups, or\nproduction environments. In **one line of code**, you can have **powerful** model **inference** running!\n\n```python\nfrom open_image_models import LicensePlateDetector\n\nlp_detector = LicensePlateDetector(detection_model=\"yolo-v9-t-256-license-plate-end2end\")\nlp_detector.predict(\"path/to/license_plate_image.jpg\")\n```\n\n✨ That's it! Powerful license plate detection with just a few lines of code.\n\n## Features\n\n- 🚀 Pre-trained: Models are **ready** for immediate use, no additional training required.\n- 🌟 ONNX: Cross-platform support for **fast inference** on both CPU and GPU environments.\n- ⚡ Performance: Optimized for both speed and accuracy, ensuring efficient **real-time** applications.\n- 💻 Simple API: Power up your applications with robust model inference in just one line of code.\n\n## Installation\n\nTo install open-image-models via pip, use the following command:\n\n```shell\npip install open-image-models\n```\n\n## Available Models\n\n### Object Detection\n\n#### Plate Detection\n\n![](https://raw.githubusercontent.com/ankandrew/LocalizadorPatentes/2e765012f69c4fbd8decf998e61ed136004ced24/extra/demo_localizador.gif)\n\n|                 Model                 | Image Size | Precision (P) | Recall (R) | mAP50 | mAP50-95 |\n|:-------------------------------------:|------------|---------------|------------|-------|----------|\n| `yolo-v9-s-608-license-plate-end2end` | 608        | 0.957         | 0.917      | 0.966 | 0.772    |\n| `yolo-v9-t-640-license-plate-end2end` | 640        | 0.966         | 0.896      | 0.958 | 0.758    |\n| `yolo-v9-t-512-license-plate-end2end` | 512        | 0.955         | 0.901      | 0.948 | 0.724    |\n| `yolo-v9-t-416-license-plate-end2end` | 416        | 0.94          | 0.894      | 0.94  | 0.702    |\n| `yolo-v9-t-384-license-plate-end2end` | 384        | 0.942         | 0.863      | 0.92  | 0.687    |\n| `yolo-v9-t-256-license-plate-end2end` | 256        | 0.937         | 0.797      | 0.858 | 0.606    |\n\n\u003cdetails\u003e\n  \u003csummary\u003eUsage\u003c/summary\u003e\n\n  ```python\nimport cv2\nfrom rich import print\n\nfrom open_image_models import LicensePlateDetector\n\n# Initialize the License Plate Detector with the pre-trained YOLOv9 model\nlp_detector = LicensePlateDetector(detection_model=\"yolo-v9-t-384-license-plate-end2end\")\n\n# Load an image\nimage_path = \"path/to/license_plate_image.jpg\"\nimage = cv2.imread(image_path)\n\n# Perform license plate detection\ndetections = lp_detector.predict(image)\nprint(detections)\n\n# Benchmark the model performance\nlp_detector.show_benchmark(num_runs=1000)\n\n# Display predictions on the image\nannotated_image = lp_detector.display_predictions(image)\n\n# Show the annotated image\ncv2.imshow(\"Annotated Image\", annotated_image)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n  ```\n\n\u003c/details\u003e\n\n\u003e [!TIP]\n\u003e Checkout the [docs](https://ankandrew.github.io/open-image-models)!\n\n## Contributing\n\nContributions to the repo are greatly appreciated. Whether it's bug fixes, feature enhancements, or new models,\nyour contributions are warmly welcomed.\n\nTo start contributing or to begin development, you can follow these steps:\n\n1. Clone repo\n    ```shell\n    git clone https://github.com/ankandrew/open-image-models.git\n    ```\n2. Install all dependencies using [Poetry](https://python-poetry.org/docs/#installation):\n    ```shell\n    poetry install --all-extras\n    ```\n3. To ensure your changes pass linting and tests before submitting a PR:\n    ```shell\n    make checks\n    ```\n\n## Citation\n\n```\n@article{wang2024yolov9,\n  title={{YOLOv9}: Learning What You Want to Learn Using Programmable Gradient Information},\n  author={Wang, Chien-Yao  and Liao, Hong-Yuan Mark},\n  booktitle={arXiv preprint arXiv:2402.13616},\n  year={2024}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fankandrew%2Fopen-image-models","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fankandrew%2Fopen-image-models","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fankandrew%2Fopen-image-models/lists"}