Segmentation

Instance segmentation goes a step further than object detection and involves identifying individual objects in an image and segmenting them from the rest of the image.

The output of an instance segmentation model is a set of masks or contours that outline each object in the image, along with class labels and confidence scores for each object. Instance segmentation is useful when you need to know not only where objects are in an image, but also what their exact shape is.

Tip

YOLOv8 segmentation models use the -seg suffix, i.e. yolov8n-seg.pt and are pretrained on COCO.

Models

Train

Train YOLOv8n-seg on the COCO128-seg dataset for 100 epochs at image size 640. For a full list of available arguments see the Configuration page.

PythonCLI

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n-seg.yaml")  # build a new model from scratch
model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)

# Train the model
model.train(data="coco128-seg.yaml", epochs=100, imgsz=640)

yolo segment train data=coco128-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640

Val

Validate trained YOLOv8n-seg model accuracy on the COCO128-seg dataset. No argument need to passed as the model retains it's training data and arguments as model attributes.

PythonCLI

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n-seg.pt")  # load an official model
model = YOLO("path/to/best.pt")  # load a custom model

# Validate the model
metrics = model.val()  # no arguments needed, dataset and settings remembered
metrics.box.map    # map50-95(B)
metrics.box.map50  # map50(B)
metrics.box.map75  # map75(B)
metrics.box.maps   # a list contains map50-95(B) of each category
metrics.seg.map    # map50-95(M)
metrics.seg.map50  # map50(M)
metrics.seg.map75  # map75(M)
metrics.seg.maps   # a list contains map50-95(M) of each category

yolo segment val model=yolov8n-seg.pt  # val official model
yolo segment val model=path/to/best.pt  # val custom model

Predict

Use a trained YOLOv8n-seg model to run predictions on images.

PythonCLI

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n-seg.pt")  # load an official model
model = YOLO("path/to/best.pt")  # load a custom model

# Predict with the model
results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image

yolo segment predict model=yolov8n-seg.pt source="https://ultralytics.com/images/bus.jpg"  # predict with official model
yolo segment predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg"  # predict with custom model

Read more details of predict in our Predict page.

Export

Export a YOLOv8n-seg model to a different format like ONNX, CoreML, etc.

PythonCLI

from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n-seg.pt")  # load an official model
model = YOLO("path/to/best.pt")  # load a custom trained

# Export the model
model.export(format="onnx")

yolo export model=yolov8n-seg.pt format=onnx  # export official model
yolo export model=path/to/best.pt format=onnx  # export custom trained model

Available YOLOv8-seg export formats include:

Format	`format=`	Model
PyTorch	-	`yolov8n-seg.pt`
TorchScript	`torchscript`	`yolov8n-seg.torchscript`
ONNX	`onnx`	`yolov8n-seg.onnx`
OpenVINO	`openvino`	`yolov8n-seg_openvino_model/`
TensorRT	`engine`	`yolov8n-seg.engine`
CoreML	`coreml`	`yolov8n-seg.mlmodel`
TensorFlow SavedModel	`saved_model`	`yolov8n-seg_saved_model/`
TensorFlow GraphDef	`pb`	`yolov8n-seg.pb`
TensorFlow Lite	`tflite`	`yolov8n-seg.tflite`
TensorFlow Edge TPU	`edgetpu`	`yolov8n-seg_edgetpu.tflite`
TensorFlow.js	`tfjs`	`yolov8n-seg_web_model/`
PaddlePaddle	`paddle`	`yolov8n-seg_paddle_model/`