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Demo • Github

This is a collection of YOLOv8 models finetuned for classification/detection/segmentation tasks on datasets from various domains as Medicine/Insurance/Sports/Gaming.

Ultralytics YOLOv8, developed by Ultralytics, is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection, image segmentation and image classification tasks.

_Source: github

To use listed models, install ultralyticsplus:

pip install ultralyticsplus

from ultralyticsplus import YOLO, render_result

# load model
model = YOLO(DESIRED_MODEL_ID)

# set image
image = 'image.png'
    
# perform inference
results = model(image)

# parse results
result = results[0]
boxes = result.boxes.xyxy # x1, y1, x2, y2
scores = result.boxes.conf
categories = result.boxes.cls
scores = result.probs # for classification models
masks = result.masks # for segmentation models
    
# show results on image
render = render_result(model=model, image=image, result=result)
render.show()

To contribute to Awesome-YOLOv8-Models, follow these steps:

1. Train a YOLOv8 model with ultralytics package | tutorial
2. Push your model to hub with ultralyticsplus package | package readme
3. Open a PR or Discussion post in this repo with your hub id.

This project is licensed under MIT license. See LICENSE for more information.

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