wkentaro/yolo-world-onnx

ONNX models of YOLO-World (an open-vocabulary object detection).

32
/ 100
Emerging

This project helps computer vision engineers and AI developers by providing optimized ONNX models for open-vocabulary object detection. You input images and a list of object names you're looking for, and it outputs the locations and classifications of those objects in the image. This is ideal for those integrating advanced object detection into applications without needing extensive machine learning model training.

No commits in the last 6 months.

Use this if you need to deploy a flexible object detection model that can identify a wide range of objects based on text descriptions, without retraining the model for each new object.

Not ideal if you are a non-technical end-user looking for a ready-to-use application with a graphical interface.

object-detection computer-vision-deployment AI-model-optimization edge-AI real-time-analytics
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 10 / 25

How are scores calculated?

Stars

24

Forks

3

Language

Python

License

GPL-3.0

Last pushed

Jun 29, 2024

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/wkentaro/yolo-world-onnx"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.