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186 lines
11 KiB
186 lines
11 KiB
---
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comments: true
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description: Learn how to use instance segmentation models with Ultralytics YOLO. Instructions on training, validation, image prediction, and model export.
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keywords: yolov8, instance segmentation, Ultralytics, COCO dataset, image segmentation, object detection, model training, model validation, image prediction, model export
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---
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Instance segmentation goes a step further than object detection and involves identifying individual objects in an image
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and segmenting them from the rest of the image.
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<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418644-7df320b8-098d-47f1-85c5-26604d761286.png">
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The output of an instance segmentation model is a set of masks or
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contours that outline each object in the image, along with class labels and confidence scores for each object. Instance
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segmentation is useful when you need to know not only where objects are in an image, but also what their exact shape is.
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!!! tip "Tip"
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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/cfg/datasets/coco.yaml).
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## [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models/v8)
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YOLOv8 pretrained Segment models are shown here. Detect, Segment and Pose models are pretrained on
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the [COCO](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml) dataset, while Classify
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models are pretrained on
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the [ImageNet](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/ImageNet.yaml) dataset.
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[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest
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Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
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| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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|----------------------------------------------------------------------------------------------|-----------------------|----------------------|-----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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| [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 |
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- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
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<br>Reproduce by `yolo val segment data=coco.yaml device=0`
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- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
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instance.
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<br>Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu`
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## Train
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Train YOLOv8n-seg on the COCO128-seg dataset for 100 epochs at image size 640. For a full list of available
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arguments see the [Configuration](../usage/cfg.md) page.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-seg.yaml') # build a new model from YAML
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model = YOLO('yolov8n-seg.pt') # load a pretrained model (recommended for training)
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model = YOLO('yolov8n-seg.yaml').load('yolov8n.pt') # build from YAML and transfer weights
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# Train the model
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model.train(data='coco128-seg.yaml', epochs=100, imgsz=640)
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```
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=== "CLI"
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```bash
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# Build a new model from YAML and start training from scratch
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yolo segment train data=coco128-seg.yaml model=yolov8n-seg.yaml epochs=100 imgsz=640
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# Start training from a pretrained *.pt model
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yolo segment train data=coco128-seg.yaml model=yolov8n-seg.pt epochs=100 imgsz=640
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# Build a new model from YAML, transfer pretrained weights to it and start training
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yolo segment train data=coco128-seg.yaml model=yolov8n-seg.yaml pretrained=yolov8n-seg.pt epochs=100 imgsz=640
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```
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### Dataset format
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YOLO segmentation dataset format can be found in detail in the [Dataset Guide](../datasets/segment/index.md). To convert your existing dataset from other formats( like COCO etc.) to YOLO format, please use [json2yolo tool](https://github.com/ultralytics/JSON2YOLO) by Ultralytics.
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## Val
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Validate trained YOLOv8n-seg model accuracy on the COCO128-seg dataset. No argument need to passed as the `model`
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retains it's training `data` and arguments as model attributes.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-seg.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom model
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# Validate the model
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metrics = model.val() # no arguments needed, dataset and settings remembered
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metrics.box.map # map50-95(B)
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metrics.box.map50 # map50(B)
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metrics.box.map75 # map75(B)
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metrics.box.maps # a list contains map50-95(B) of each category
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metrics.seg.map # map50-95(M)
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metrics.seg.map50 # map50(M)
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metrics.seg.map75 # map75(M)
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metrics.seg.maps # a list contains map50-95(M) of each category
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```
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=== "CLI"
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```bash
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yolo segment val model=yolov8n-seg.pt # val official model
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yolo segment val model=path/to/best.pt # val custom model
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```
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## Predict
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Use a trained YOLOv8n-seg model to run predictions on images.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-seg.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom model
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# Predict with the model
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results = model('https://ultralytics.com/images/bus.jpg') # predict on an image
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```
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=== "CLI"
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```bash
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yolo segment predict model=yolov8n-seg.pt source='https://ultralytics.com/images/bus.jpg' # predict with official model
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yolo segment predict model=path/to/best.pt source='https://ultralytics.com/images/bus.jpg' # predict with custom model
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```
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See full `predict` mode details in the [Predict](https://docs.ultralytics.com/modes/predict/) page.
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## Export
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Export a YOLOv8n-seg model to a different format like ONNX, CoreML, etc.
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!!! example ""
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=== "Python"
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```python
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-seg.pt') # load an official model
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model = YOLO('path/to/best.pt') # load a custom trained
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# Export the model
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model.export(format='onnx')
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```
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=== "CLI"
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```bash
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yolo export model=yolov8n-seg.pt format=onnx # export official model
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yolo export model=path/to/best.pt format=onnx # export custom trained model
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```
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Available YOLOv8-seg export formats are in the table below. You can predict or validate directly on exported models,
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i.e. `yolo predict model=yolov8n-seg.onnx`. Usage examples are shown for your model after export completes.
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| Format | `format` Argument | Model | Metadata | Arguments |
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|--------------------------------------------------------------------|-------------------|-------------------------------|----------|-----------------------------------------------------|
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| [PyTorch](https://pytorch.org/) | - | `yolov8n-seg.pt` | ✅ | - |
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| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-seg.torchscript` | ✅ | `imgsz`, `optimize` |
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| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-seg.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` |
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| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n-seg_openvino_model/` | ✅ | `imgsz`, `half` |
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| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-seg.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
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| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-seg.mlmodel` | ✅ | `imgsz`, `half`, `int8`, `nms` |
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| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-seg_saved_model/` | ✅ | `imgsz`, `keras` |
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| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-seg.pb` | ❌ | `imgsz` |
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| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-seg.tflite` | ✅ | `imgsz`, `half`, `int8` |
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| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-seg_edgetpu.tflite` | ✅ | `imgsz` |
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| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-seg_web_model/` | ✅ | `imgsz` |
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| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-seg_paddle_model/` | ✅ | `imgsz` |
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| [ncnn](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-seg_ncnn_model/` | ✅ | `imgsz`, `half` |
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See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. |