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Awesome YOLOv8 Models

Easy-to-use finetuned YOLOv8 models

DemoGithub

Satellighte

TABLE OF CONTENTS
  1. About the Project
  2. Installation
  3. Usage
  4. Classification Models
  5. Detection Models
  6. Segmentation Models
  7. Contributing
  8. License

About the Project

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

Installation

To use listed models, install ultralyticsplus:

pip install ultralyticsplus

Usage

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()

Classification Models

top1 acc. top5 acc. model type model id dataset page
0.678 1.000 yolov8n-cls keremberke/yolov8n-shoe-classification dataset
0.687 1.000 yolov8s-cls keremberke/yolov8s-shoe-classification dataset
0.795 1.000 yolov8m-cls keremberke/yolov8m-shoe-classification dataset

top1 acc. top5 acc. model type model id dataset page
0.943 1.000 yolov8n-cls keremberke/yolov8n-chest-xray-classification dataset
0.942 1.000 yolov8s-cls keremberke/yolov8s-chest-xray-classification dataset
0.955 1.000 yolov8m-cls keremberke/yolov8m-chest-xray-classification dataset

Detection Models

box mAP@0.5 model type model id dataset page
0.937 yolov8n keremberke/yolov8n-valorant-detection dataset
0.971 yolov8s keremberke/yolov8s-valorant-detection dataset
0.965 yolov8m keremberke/yolov8m-valorant-detection dataset

box mAP@0.5 model type model id dataset page
0.838 yolov8n keremberke/yolov8n-forklift-detection dataset
0.851 yolov8s keremberke/yolov8s-forklift-detection dataset
0.846 yolov8m keremberke/yolov8m-forklift-detection dataset

box mAP@0.5 model type model id dataset page
0.844 yolov8n keremberke/yolov8n-csgo-player-detection dataset
0.886 yolov8s keremberke/yolov8s-csgo-player-detection dataset
0.892 yolov8m keremberke/yolov8m-csgo-player-detection dataset

box mAP@0.5 model type model id dataset page
0.893 yolov8n keremberke/yolov8n-blood-cell-detection dataset
0.917 yolov8s keremberke/yolov8s-blood-cell-detection dataset
0.927 yolov8m keremberke/yolov8m-blood-cell-detection dataset

box mAP@0.5 model type model id dataset page
0.995 yolov8n keremberke/yolov8n-plane-detection dataset
0.995 yolov8s keremberke/yolov8s-plane-detection dataset
0.995 yolov8m keremberke/yolov8m-plane-detection dataset

box mAP@0.5 model type model id dataset page
0.209 yolov8n keremberke/yolov8n-nlf-head-detection dataset
0.279 yolov8s keremberke/yolov8s-nlf-head-detection dataset
0.287 yolov8m keremberke/yolov8m-nlf-head-detection dataset

box mAP@0.5 model type model id dataset page
0.836 yolov8n keremberke/yolov8n-hard-hat-detection dataset
0.834 yolov8s keremberke/yolov8s-hard-hat-detection dataset
0.811 yolov8m keremberke/yolov8m-hard-hat-detection dataset

box mAP@0.5 model type model id dataset page
0.967 yolov8n keremberke/yolov8n-table-extraction dataset
0.984 yolov8s keremberke/yolov8s-table-extraction dataset
0.952 yolov8m keremberke/yolov8m-table-extraction dataset

Segmentation Models

mask mAP@0.5 model type model id dataset page
0.491 yolov8n-seg keremberke/yolov8n-pcb-defect-segmentation dataset
0.517 yolov8s-seg keremberke/yolov8s-pcb-defect-segmentation dataset
0.557 yolov8m-seg keremberke/yolov8m-pcb-defect-segmentation dataset

mask mAP@0.5 model type model id dataset page
0.628 yolov8n-seg keremberke/yolov8n-building-segmentation dataset
0.651 yolov8s-seg keremberke/yolov8s-building-segmentation dataset
0.613 yolov8m-seg keremberke/yolov8m-building-segmentation dataset

mask mAP@0.5 model type model id dataset page
0.995 yolov8n-seg keremberke/yolov8n-pothole-segmentation dataset
0.928 yolov8s-seg keremberke/yolov8s-pothole-segmentation dataset
0.895 yolov8m-seg keremberke/yolov8m-pothole-segmentation dataset

Contributing

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.

License

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

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