ModelsInstance Segmentation
YOLOv8 Instance Segmentation
Instance Segmentation
Real-Time Vision
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YOLOv8 Instance Segmentation

The state-of-the-art YOLOv8 model comes with support for instance segmentation tasks.
Ultralytics
•
GitHubGitHub
•
star
21.1k+
stars
•
AGPL-3.0
license
•
Developed by
Ultralytics
•
Released
Jan 10, 2023
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Arxiv logo markPaper

Try the Model

Use the widget below to experiment with YOLOv8 Instance Segmentation. You can detect COCO classes such as people, vehicles, animals, household items.

Overview

YOLOv8 is a state-of-the-art object detection and image segmentation model created by Ultralytics, the developers of YOLOv5. YOLOv8, launched on January 10, 2023, features:

  • A new backbone network;
  • A design that makes it easy to compare model performance with older models in the YOLO family;
  • A new loss function and;
  • A new anchor-free detection head.

there are many YOLOv8 segmentation models released. These include:
• YOLOv8n-seg (Nano): Approximately 3.2 million parameters
• YOLOv8s-seg (Small): Approximately 11.2 million parameters
• YOLOv8m-seg (Medium): Approximately 25.9 million parameters
• YOLOv8l-seg (Large): Approximately 43.7 million parameters
• YOLOv8x-seg (Extra Large): Approximately 68.2 million parameters

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YOLOv8 Instance Segmentation License

YOLOv8 Instance Segmentation

is licensed under a 

AGPL-3.0

license. 

Performance

Deploy a YOLOv8 Instance Segmentation API

You can use Roboflow Inference to deploy a

YOLOv8 Instance Segmentation

API on your hardware. You can deploy the model on CPU (i.e. Raspberry Pi, AI PCs) and GPU devices (i.e. NVIDIA Jetson, NVIDIA T4).

Below are instructions on how to deploy your own model API.

First, install Inference:‍


pip install inference

To try a demo with a model trained on the Microsoft COCO dataset, use:


import inference
model = inference.load_roboflow_model("yolov8n-seg-640")
results = model.infer(image="YOUR_IMAGE.jpg")

Above, replace:

  • YOUR_IMAGE.jpg with the path to your image.

You can also run fine-tuned models with Inference.

Retrieve your Roboflow API key and save it in an environment variable called ROBOFLOW_API_KEY:


export ROBOFLOW_API_KEY="your-api-key"

To use your model, run the following code:


import inference
model = inference.load_roboflow_model("model-name/version")
results = model.infer(image="YOUR_IMAGE.jpg")

Above, replace:

Label Data Automatically with YOLOv8 Instance Segmentation

You can automatically label a dataset using YOLOv8 Instance Segmentation with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use YOLOv8 Instance Segmentation to train a computer vision model.
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Use YOLOv8 Instance Segmentation to Build Computer Vision Pipelines and Applications
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Data

Augmentation GuideLabeling Guide
Flask

Training

Model OverviewTrain on ColabTrain on JupyterTrain on KaggleTrain on SageMakerSee Model AlternativesView Benchmarks
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Utilities

Plot PredictionsFilter PredictionsBlur PredictionsCreate A Confusion MatrixUse SAHI for Small Object Detection
Code Compare

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Convert Annotation Format

YOLOv8 uses the uses the YOLOv8 PyTorch TXT annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.
Convert Data Between Formats

Architecture

YOLO

Frameworks

PyTorch

Annotation Format

YOLOv8 PyTorch TXT

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YOLOv8 Instance Segmentation Integrations

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YOLOv8 Instance Segmentation to Roboflow Classification Model

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YOLOv8 Instance Segmentation to Roboflow Instance Segmentation Model

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Roboflow Object Detection Model to YOLOv8 Instance Segmentation

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YOLOv8 Instance Segmentation to YOLOv8 Classification

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Explore This Model on Roboflow

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