YOLOv8 Instance Segmentation

YOLOv8 Instance Segmentation

YOLOv8 is an open-source computer vision model by Ultralytics, the creators of YOLOv5 that supports object detection, classification, and instance segmentation. You can use autodistill to train a YOLOv8 object detection model on a dataset of labelled images generated by the base models that autodistill supports.

This document shows how to train an instance segmentation model using a base model supported by autodiistill and YOLOv8's instance segmentation functionality.

View our YOLOv8 page for information on how to train object detectino models.

Installation

To use the YOLOv8 target model, you will need to install the following dependency:

pip3 install autodistill-yolov8

Quickstart

from autodistill_yolov8 import YOLOv8
target_model = YOLOv8("yolov8n-seg.pt")

# train model using images in `context_images_labeled` folder for 200 epochs
target_model.train("./context_images_labeled/data.yaml", epochs=200)

# export weights for future use
saved_weights = target_model.export(format="onnx")

# show performance metrics for your model
metrics = target_model.val()

# run inference on the new model
pred = target_model.predict("./context_images_labeled/train/images/dog-7.jpg", conf=0.01)