YOLOv8-16bit/docs/tasks/segmentation.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 _segmentation_ models use the `-seg` suffix, i.e. `yolov8n-seg.pt` and are pretrained on COCO.

Models{.md-button .md-button--primary}

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 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)
    ```
=== "CLI"

    ```bash
    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.

!!! 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
    ```

Read more details of predict in our 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 include:

| Format                                                                     | `format=`     | Model                         |
|----------------------------------------------------------------------------|---------------|-------------------------------|
| [PyTorch](https://pytorch.org/)                                            | -             | `yolov8n-seg.pt`              |
| [TorchScript](https://pytorch.org/docs/stable/jit.html)                    | `torchscript` | `yolov8n-seg.torchscript`     |
| [ONNX](https://onnx.ai/)                                                   | `onnx`        | `yolov8n-seg.onnx`            |
| [OpenVINO](https://docs.openvino.ai/latest/index.html)                     | `openvino`    | `yolov8n-seg_openvino_model/` |
| [TensorRT](https://developer.nvidia.com/tensorrt)                          | `engine`      | `yolov8n-seg.engine`          |
| [CoreML](https://github.com/apple/coremltools)                             | `coreml`      | `yolov8n-seg.mlmodel`         |
| [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model` | `yolov8n-seg_saved_model/`    |
| [TensorFlow GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb`          | `yolov8n-seg.pb`              |
| [TensorFlow Lite](https://www.tensorflow.org/lite)                         | `tflite`      | `yolov8n-seg.tflite`          |
| [TensorFlow Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`     | `yolov8n-seg_edgetpu.tflite`  |
| [TensorFlow.js](https://www.tensorflow.org/js)                             | `tfjs`        | `yolov8n-seg_web_model/`      |
| [PaddlePaddle](https://github.com/PaddlePaddle)                            | `paddle`      | `yolov8n-seg_paddle_model/`   |