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https://github.com/keremberke/awesome-yolov8-models

Easy-to-use finetuned YOLOv8 models.
https://github.com/keremberke/awesome-yolov8-models
computer-vision csgo gradio huggingface image-classification instance-segmentation object-detection pcb plane pothole-detection pytorch ultralytics valorant xray yolov8
Last synced: 16 days ago
JSON representation
Easy-to-use finetuned YOLOv8 models.
Host: GitHub
URL: https://github.com/keremberke/awesome-yolov8-models
Owner: keremberke
License: mit
Created: 2023-01-31T19:34:11.000Z (almost 2 years ago)
Default Branch: main
Last Pushed: 2023-02-27T20:22:46.000Z (almost 2 years ago)
Last Synced: 2024-04-18T14:38:43.340Z (8 months ago)
Topics: computer-vision, csgo, gradio, huggingface, image-classification, instance-segmentation, object-detection, pcb, plane, pothole-detection, pytorch, ultralytics, valorant, xray, yolov8
Language: HTML
Homepage: https://yolov8.xyz
Size: 51.8 KB
Stars: 145
Watchers: 1
Forks: 22
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project

ultimate-awesome - awesome-yolov8-models - Easy-to-use finetuned YOLOv8 models. (Other Lists / Monkey C Lists)
README

        


Awesome YOLOv8 Models

Easy-to-use finetuned YOLOv8 models




    

        Demo

        •

        Github

    





    

    

        

            TABLE OF CONTENTS

        

    

    


        

            About the Project

        

        

            Installation

        

        Usage

        Classification Models

        Detection Models

        Segmentation Models

        Contributing

        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](https://github.com/ultralytics/ultralytics)

## Installation

To use listed models, install ultralyticsplus:

```bash

pip install ultralyticsplus

```

## Usage

```python

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](https://huggingface.co/keremberke/yolov8n-shoe-classification) | [dataset](https://huggingface.co/datasets/keremberke/shoe-classification) |

| 0.687 | 1.000 | yolov8s-cls | [keremberke/yolov8s-shoe-classification](https://huggingface.co/keremberke/yolov8s-shoe-classification) | [dataset](https://huggingface.co/datasets/keremberke/shoe-classification) |

| 0.795 | 1.000 | yolov8m-cls | [keremberke/yolov8m-shoe-classification](https://huggingface.co/keremberke/yolov8m-shoe-classification) | [dataset](https://huggingface.co/datasets/keremberke/shoe-classification) |



| top1 acc. | top5 acc. | model type | model id |  dataset page |

|---        |---        |---         |---       |---            |

| 0.943 | 1.000 | yolov8n-cls | [keremberke/yolov8n-chest-xray-classification](https://huggingface.co/keremberke/yolov8n-chest-xray-classification) | [dataset](https://huggingface.co/datasets/keremberke/chest-xray-classification) |

| 0.942 | 1.000 | yolov8s-cls | [keremberke/yolov8s-chest-xray-classification](https://huggingface.co/keremberke/yolov8s-chest-xray-classification) | [dataset](https://huggingface.co/datasets/keremberke/chest-xray-classification) |

| 0.955 | 1.000 | yolov8m-cls | [keremberke/yolov8m-chest-xray-classification](https://huggingface.co/keremberke/yolov8m-chest-xray-classification) | [dataset](https://huggingface.co/datasets/keremberke/chest-xray-classification) |

## Detection Models



| box [email protected] | model type | model id |  dataset page |

|---                 |---         |---       |---            |

| 0.937 | yolov8n | [keremberke/yolov8n-valorant-detection](https://huggingface.co/keremberke/yolov8n-valorant-detection) | [dataset](https://huggingface.co/datasets/keremberke/valorant-object-detection) |

| 0.971 | yolov8s | [keremberke/yolov8s-valorant-detection](https://huggingface.co/keremberke/yolov8s-valorant-detection) | [dataset](https://huggingface.co/datasets/keremberke/valorant-object-detection) |

| 0.965 | yolov8m | [keremberke/yolov8m-valorant-detection](https://huggingface.co/keremberke/yolov8m-valorant-detection) | [dataset](https://huggingface.co/datasets/keremberke/valorant-object-detection) |



| box [email protected] | model type | model id |  dataset page |

|---                 |---         |---       |---            |

| 0.838 | yolov8n | [keremberke/yolov8n-forklift-detection](https://huggingface.co/keremberke/yolov8n-forklift-detection) | [dataset](https://huggingface.co/datasets/keremberke/forklift-object-detection) |

| 0.851 | yolov8s | [keremberke/yolov8s-forklift-detection](https://huggingface.co/keremberke/yolov8s-forklift-detection) | [dataset](https://huggingface.co/datasets/keremberke/forklift-object-detection) |

| 0.846 | yolov8m | [keremberke/yolov8m-forklift-detection](https://huggingface.co/keremberke/yolov8m-forklift-detection) | [dataset](https://huggingface.co/datasets/keremberke/forklift-object-detection) |



| box [email protected] | model type | model id |  dataset page |

|---                 |---         |---       |---            |

| 0.844 | yolov8n | [keremberke/yolov8n-csgo-player-detection](https://huggingface.co/keremberke/yolov8n-csgo-player-detection) | [dataset](https://huggingface.co/datasets/keremberke/csgo-object-detection) |

| 0.886 | yolov8s | [keremberke/yolov8s-csgo-player-detection](https://huggingface.co/keremberke/yolov8s-csgo-player-detection) | [dataset](https://huggingface.co/datasets/keremberke/csgo-object-detection) |

| 0.892 | yolov8m | [keremberke/yolov8m-csgo-player-detection](https://huggingface.co/keremberke/yolov8m-csgo-player-detection) | [dataset](https://huggingface.co/datasets/keremberke/csgo-object-detection) |



| box [email protected] | model type | model id |  dataset page |

|---                 |---         |---       |---            |

| 0.893 | yolov8n | [keremberke/yolov8n-blood-cell-detection](https://huggingface.co/keremberke/yolov8n-blood-cell-detection) | [dataset](https://huggingface.co/datasets/keremberke/blood-cell-object-detection) |

| 0.917 | yolov8s | [keremberke/yolov8s-blood-cell-detection](https://huggingface.co/keremberke/yolov8s-blood-cell-detection) | [dataset](https://huggingface.co/datasets/keremberke/blood-cell-object-detection) |

| 0.927 | yolov8m | [keremberke/yolov8m-blood-cell-detection](https://huggingface.co/keremberke/yolov8m-blood-cell-detection) | [dataset](https://huggingface.co/datasets/keremberke/blood-cell-object-detection) |



| box [email protected] | model type | model id |  dataset page |

|---                 |---         |---       |---            |

| 0.995 | yolov8n | [keremberke/yolov8n-plane-detection](https://huggingface.co/keremberke/yolov8n-plane-detection) | [dataset](https://huggingface.co/datasets/keremberke/plane-detection) |

| 0.995 | yolov8s | [keremberke/yolov8s-plane-detection](https://huggingface.co/keremberke/yolov8s-plane-detection) | [dataset](https://huggingface.co/datasets/keremberke/plane-detection) |

| 0.995 | yolov8m | [keremberke/yolov8m-plane-detection](https://huggingface.co/keremberke/yolov8m-plane-detection) | [dataset](https://huggingface.co/datasets/keremberke/plane-detection) |



| box [email protected] | model type | model id |  dataset page |

|---                 |---         |---       |---            |

| 0.209 | yolov8n | [keremberke/yolov8n-nlf-head-detection](https://huggingface.co/keremberke/yolov8n-nlf-head-detection) | [dataset](https://huggingface.co/datasets/keremberke/nfl-object-detection) |

| 0.279 | yolov8s | [keremberke/yolov8s-nlf-head-detection](https://huggingface.co/keremberke/yolov8s-nlf-head-detection) | [dataset](https://huggingface.co/datasets/keremberke/nfl-object-detection) |

| 0.287 | yolov8m | [keremberke/yolov8m-nlf-head-detection](https://huggingface.co/keremberke/yolov8m-nlf-head-detection) | [dataset](https://huggingface.co/datasets/keremberke/nfl-object-detection) |



| box [email protected] | model type | model id |  dataset page |

|---                 |---         |---       |---            |

| 0.836 | yolov8n | [keremberke/yolov8n-hard-hat-detection](https://huggingface.co/keremberke/yolov8n-hard-hat-detection) | [dataset](https://huggingface.co/datasets/keremberke/hard-hat-detection) |

| 0.834 | yolov8s | [keremberke/yolov8s-hard-hat-detection](https://huggingface.co/keremberke/yolov8s-hard-hat-detection) | [dataset](https://huggingface.co/datasets/keremberke/hard-hat-detection) |

| 0.811 | yolov8m | [keremberke/yolov8m-hard-hat-detection](https://huggingface.co/keremberke/yolov8m-hard-hat-detection) | [dataset](https://huggingface.co/datasets/keremberke/hard-hat-detection) |



| box [email protected] | model type | model id |  dataset page |

|---                 |---         |---       |---            |

| 0.967 | yolov8n | [keremberke/yolov8n-table-extraction](https://huggingface.co/keremberke/yolov8n-table-extraction) | [dataset](https://huggingface.co/datasets/keremberke/table-extraction) |

| 0.984 | yolov8s | [keremberke/yolov8s-table-extraction](https://huggingface.co/keremberke/yolov8s-table-extraction) | [dataset](https://huggingface.co/datasets/keremberke/table-extraction) |

| 0.952 | yolov8m | [keremberke/yolov8m-table-extraction](https://huggingface.co/keremberke/yolov8m-table-extraction) | [dataset](https://huggingface.co/datasets/keremberke/table-extraction) |

## Segmentation Models



| mask [email protected] | model type | model id |  dataset page |

|---            |---         |---         |---            |

| 0.491 | yolov8n-seg | [keremberke/yolov8n-pcb-defect-segmentation](https://huggingface.co/keremberke/yolov8n-pcb-defect-segmentation) | [dataset](https://huggingface.co/datasets/keremberke/pcb-defect-segmentation) |

| 0.517 | yolov8s-seg | [keremberke/yolov8s-pcb-defect-segmentation](https://huggingface.co/keremberke/yolov8s-pcb-defect-segmentation) | [dataset](https://huggingface.co/datasets/keremberke/pcb-defect-segmentation) |

| 0.557 | yolov8m-seg | [keremberke/yolov8m-pcb-defect-segmentation](https://huggingface.co/keremberke/yolov8m-pcb-defect-segmentation) | [dataset](https://huggingface.co/datasets/keremberke/pcb-defect-segmentation) |



| mask [email protected] | model type | model id |  dataset page |

|---            |---         |---         |---            |

| 0.628 | yolov8n-seg | [keremberke/yolov8n-building-segmentation](https://huggingface.co/keremberke/yolov8n-building-segmentation) | [dataset](https://huggingface.co/datasets/keremberke/satellite-building-segmentation) |

| 0.651 | yolov8s-seg | [keremberke/yolov8s-building-segmentation](https://huggingface.co/keremberke/yolov8s-building-segmentation) | [dataset](https://huggingface.co/datasets/keremberke/satellite-building-segmentation) |

| 0.613 | yolov8m-seg | [keremberke/yolov8m-building-segmentation](https://huggingface.co/keremberke/yolov8m-building-segmentation) | [dataset](https://huggingface.co/datasets/keremberke/satellite-building-segmentation) |



| mask [email protected] | model type | model id |  dataset page |

|---            |---         |---         |---            |

| 0.995 | yolov8n-seg | [keremberke/yolov8n-pothole-segmentation](https://huggingface.co/keremberke/yolov8n-pothole-segmentation) | [dataset](https://huggingface.co/datasets/keremberke/pothole-segmentation) |

| 0.928 | yolov8s-seg | [keremberke/yolov8s-pothole-segmentation](https://huggingface.co/keremberke/yolov8s-pothole-segmentation) | [dataset](https://huggingface.co/datasets/keremberke/pothole-segmentation) |

| 0.895 | yolov8m-seg | [keremberke/yolov8m-pothole-segmentation](https://huggingface.co/keremberke/yolov8m-pothole-segmentation) | [dataset](https://huggingface.co/datasets/keremberke/pothole-segmentation) |

## Contributing

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

1. Train a YOLOv8 model with ultralytics package | [tutorial](https://github.com/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb)

2. Push your model to hub with ultralyticsplus package | [package readme](https://github.com/fcakyon/ultralyticsplus#push-to--hub)

3. Open a PR or Discussion post in this repo with your hub id.

## License

This project is licensed under `MIT` license. See [`LICENSE`](LICENSE) for more information.

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