Train your own image classifier, object detection, or segmentation model with your custom dataset using the YOLOv8 model.

Model | Trainable | Inference | Pre-trained

Published by DEEP-Hybrid-DataCloud Consortium
Created: - Updated:

Model Description

Build Status

Ultralytics YOLOv8 represents the forefront of object detection (segmentation/classification) models, incorporating advancements from prior YOLO iterations while introducing novel features to enhance performance and versatility. YOLOv8 prioritizes speed, precision, and user-friendliness, positioning itself as an exceptional solution across diverse tasks such as object detection, tracking, instance segmentation, and image classification. Its refined architecture and innovations make it an ideal choice for cutting-edge applications in the field of computer vision.


[1] Jocher, G., Chaurasia, A., & Qiu, J. (2023). YOLO by Ultralytics (Version 8.0.0) [Computer software]. https://github.com/ultralytics/ultralytics

[2] https://docs.ultralytics.com/

[3] Redmon, J., et al., You Only Look Once: Unified, Real-Time Object Detection, 2015, https://arxiv.org/abs/1506.02640 [cs.CV]

Test this module

You can test and execute this module in various ways.

Excecute locally on your computer using Docker

You can run this module directly on your computer, assuming that you have Docker installed, by following these steps:

$ docker pull deephdc/deep-oc-yolov8_api
$ docker run -ti -p 5000:5000 deephdc/deep-oc-yolov8_api

Execute on your computer using udocker

If you do not have Docker available or you do not want to install it, you can use udocker within a Python virtualenv:

$ virtualenv udocker
$ source udocker/bin/activate
(udocker) $ pip install udocker
(udocker) $ udocker pull deephdc/deep-oc-yolov8_api
(udocker) $ udocker create deephdc/deep-oc-yolov8_api
(udocker) $ udocker run -p 5000:5000  deephdc/deep-oc-yolov8_api

In either case, once the module is running, point your browser to and you will see the API documentation, where you can test the module functionality, as well as perform other actions (such as training).

For more information, refer to the user documentation.

Train this module

You can train this model using the DEEP framework. In order to execute this module in our pilot e-Infrastructure you would need to be registered in the DEEP IAM.

Once you are registedered, you can go to our training dashboard to configure and train it.

For more information, refer to the user documentation.