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](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/v8) YOLOv8 pretrained Segment models are shown here. Detect, Segment and Pose models are pretrained on the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/coco.yaml) dataset, while Classify models are pretrained on the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/datasets/ImageNet.yaml) dataset. [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) 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](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640 | 36.7 | 30.5 | 96.1 | 1.21 | 3.4 | 12.6 | | [YOLOv8s-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640 | 44.6 | 36.8 | 155.7 | 1.47 | 11.8 | 42.6 | | [YOLOv8m-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640 | 49.9 | 40.8 | 317.0 | 2.18 | 27.3 | 110.2 | | [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 | | [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 | - **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
Reproduce by `yolo val segment data=coco.yaml device=0` - **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 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](../usage/cfg.md) 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 ``` ## 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](https://docs.ultralytics.com/modes/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](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` | ✅ | | [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-seg_saved_model/` | ✅ | | [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-seg.pb` | ❌ | | [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-seg.tflite` | ✅ | | [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-seg_edgetpu.tflite` | ✅ | | [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-seg_web_model/` | ✅ | | [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-seg_paddle_model/` | ✅ | See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.