Open Image Models

• Open Image Models
  • Introduction
  • Features
  • Installation
  • Available Models
    • Object Detection
      • Plate Detection
  • Contributing
  • Citation

Introduction

Ready-to-use models for a range of computer vision tasks like detection, classification, and more. With ONNX support, you get fast and accurate results right out of the box.

Easily integrate these models into your apps for real-time processing—ideal for edge devices, cloud setups, or production environments. In one line of code, you can have powerful model inference running!

from open_image_models import create_detector

detector = create_detector("yolo-v9-t-256-license-plate-end2end")
detector.predict("path/to/license_plate_image.jpg")

✨ That's it! Powerful license plate detection with just a few lines of code.

Features

• 🚀 Pre-trained: Models are ready for immediate use, no additional training required.
• 🌟 ONNX: Cross-platform support for fast inference on both CPU and GPU environments.
• ⚡ Performance: Optimized for both speed and accuracy, ensuring efficient real-time applications.
• 💻 Simple API: Power up your applications with robust model inference in just one line of code.

Installation

To install open-image-models via pip, use the following command:

pip install open-image-models[onnx]

Available Models

Object Detection

COCO Detection

CoreML is excluded from automatic RF-DETR provider selection.

Plate Detection

import cv2
from rich import print

from open_image_models import create_detector

# Create a detector from a pretrained model
detector = create_detector("yolo-v9-t-384-license-plate-end2end")

# Load an image
image_path = "path/to/license_plate_image.jpg"
image = cv2.imread(image_path)

# Perform license plate detection
detections = detector.predict(image)
print(detections)

# Display predictions on the image
annotated_image = detector.display_predictions(image)

# Show the annotated image
cv2.imshow("Annotated Image", annotated_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

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

   git clone https://github.com/ankandrew/open-image-models.git
   

2. Install all dependencies using Poetry:

   make install
   

3. To ensure your changes pass linting and tests before submitting a PR:

   make checks
   

Citation

@article{wang2024yolov9,
  title={{YOLOv9}: Learning What You Want to Learn Using Programmable Gradient Information},
  author={Wang, Chien-Yao  and Liao, Hong-Yuan Mark},
  booktitle={arXiv preprint arXiv:2402.13616},
  year={2024}
}