yolov8_test/ultralytics/models

Models

Welcome to the Ultralytics Models directory! Here you will find a wide variety of pre-configured model configuration files (*.yamls) that can be used to create custom YOLO models. The models in this directory have been expertly crafted and fine-tuned by the Ultralytics team to provide the best performance for a wide range of object detection and image segmentation tasks.

These model configurations cover a wide range of scenarios, from simple object detection to more complex tasks like instance segmentation and object tracking. They are also designed to run efficiently on a variety of hardware platforms, from CPUs to GPUs. Whether you are a seasoned machine learning practitioner or just getting started with YOLO, this directory provides a great starting point for your custom model development needs.

To get started, simply browse through the models in this directory and find one that best suits your needs. Once you've selected a model, you can use the provided *.yaml file to train and deploy your custom YOLO model with ease. See full details at the Ultralytics Docs, and if you need help or have any questions, feel free to reach out to the Ultralytics team for support. So, don't wait, start creating your custom YOLO model now!

Usage

Model *.yaml files may be used directly in the Command Line Interface (CLI) with a yolo command:

yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100

They may also be used directly in a Python environment, and accepts the same arguments as in the CLI example above:

from ultralytics import YOLO

model = YOLO("yolov8n.yaml")  # build a YOLOv8n model from scratch

model.info()  # display model information
model.train(data="coco128.yaml", epochs=100)  # train the model