ultralytics/docs/tasks/segment.md

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 "Tip"

YOLOv8 Segment models use the `-seg` suffix, i.e. `yolov8n-seg.pt` and are pretrained on [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml).

Models

YOLOv8 pretrained Segment models are shown here. Detect, Segment and Pose models are pretrained on the COCO dataset, while Classify models are pretrained on the ImageNet dataset.

Models download automatically from the latest Ultralytics release on first use.

Model	size (pixels)	mAPbox 50-95	mAPmask 50-95	Speed CPU ONNX (ms)	Speed A100 TensorRT (ms)	params (M)	FLOPs (B)
YOLOv8n-seg	640	36.7	30.5	96.1	1.21	3.4	12.6
YOLOv8s-seg	640	44.6	36.8	155.7	1.47	11.8	42.6
YOLOv8m-seg	640	49.9	40.8	317.0	2.18	27.3	110.2
YOLOv8l-seg	640	52.3	42.6	572.4	2.79	46.0	220.5
YOLOv8x-seg	640	53.4	43.4	712.1	4.02	71.8	344.1

• mAP^val values are for single-model single-scale on COCO val2017 dataset.
  Reproduce by yolo val segment data=coco.yaml device=0
• Speed averaged over COCO val images using an Amazon EC2 P4d instance.
  Reproduce by yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu

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.

!!! example ""

=== "Python"

    ```python
    from ultralytics import YOLO
    
    # Load a model
    model = YOLO('yolov8n-seg.yaml')  # build a new model from YAML
    model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
    model = YOLO('yolov8n-seg.yaml').load('yolov8n.pt')  # build from YAML and transfer weights
    
    # Train the model
    model.train(data='coco128-seg.yaml', epochs=100, imgsz=640)
    ```
=== "CLI"

    ```bash
    # Build a new model from YAML and start training from scratch
    yolo segment train data=coco128-seg.yaml model=yolov8n-seg.yaml epochs=100 imgsz=640

    # Start training from a pretrained *.pt model
    yolo segment train data=coco128-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640

    # Build a new model from YAML, transfer pretrained weights to it and start training
    yolo segment train data=coco128-seg.yaml model=yolov8n-seg.yaml pretrained=yolov8n-seg.pt epochs=100 imgsz=640
    ```

Dataset format

YOLO segmentation dataset label format extends detection format with segment points.

cls x1 y1 x2 y2 p1 p2 ... pn

To convert your existing dataset from other formats( like COCO, VOC etc.) to YOLO format, please use json2yolo tool by Ultralytics.

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.

!!! example ""

=== "Python"

    ```python
    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
    ```
=== "CLI"

    ```bash
    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.

!!! example ""

=== "Python"

    ```python
    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
    ```
=== "CLI"

    ```bash
    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
    ```

See full predict mode details in the Predict page.

Export

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

!!! example ""

=== "Python"

    ```python
    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')
    ```
=== "CLI"

    ```bash
    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 are in the table below. You can predict or validate directly on exported models, i.e. yolo predict model=yolov8n-seg.onnx. Usage examples are shown for your model after export completes.

Format	format Argument	Model	Metadata
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	✅
TF SavedModel	saved_model	yolov8n-seg_saved_model/	✅
TF GraphDef	pb	yolov8n-seg.pb	❌
TF Lite	tflite	yolov8n-seg.tflite	✅
TF Edge TPU	edgetpu	yolov8n-seg_edgetpu.tflite	✅
TF.js	tfjs	yolov8n-seg_web_model/	✅
PaddlePaddle	paddle	yolov8n-seg_paddle_model/	✅

See full export details in the Export page.