Segmentación de instancias

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.

La salida de un modelo de segmentación de instancias es un conjunto de máscaras o contornos que delinean cada objeto de la imagen, junto con etiquetas de clase y puntuaciones de confianza para cada objeto. La segmentación de instancias es útil cuando necesitas saber no sólo dónde están los objetos en una imagen, sino también cuál es su forma exacta.

Observa: Run Segmentation with Pre-Trained Ultralytics YOLO Model in Python.

Modelos

YOLO11 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.

Los modelos se descargan automáticamente de la últimaversión de Ultralytics la primera vez que se utilizan.

• ^mAPval son para un modelo de escala única en COCO val2017 conjunto de datos.
  Reproducir por yolo val segment data=coco-seg.yaml device=0
• Velocidad promediada sobre las imágenes COCO val utilizando un Amazon EC2 P4d instancia.
  Reproducir por yolo val segment data=coco-seg.yaml batch=1 device=0|cpu

Tren

Train YOLO11n-seg on the COCO8-seg dataset for 100 epochs at image size 640. For a full list of available arguments see the Configuration page.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-seg.yaml")  # build a new model from YAML
model = YOLO("yolo11n-seg.pt")  # load a pretrained model (recommended for training)
model = YOLO("yolo11n-seg.yaml").load("yolo11n.pt")  # build from YAML and transfer weights

# Train the model
results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)

# Build a new model from YAML and start training from scratch
yolo segment train data=coco8-seg.yaml model=yolo11n-seg.yaml epochs=100 imgsz=640

# Start training from a pretrained *.pt model
yolo segment train data=coco8-seg.yaml model=yolo11n-seg.pt epochs=100 imgsz=640

# Build a new model from YAML, transfer pretrained weights to it and start training
yolo segment train data=coco8-seg.yaml model=yolo11n-seg.yaml pretrained=yolo11n-seg.pt epochs=100 imgsz=640

Formato del conjunto de datos

YOLO El formato del conjunto de datos de segmentación se puede consultar en detalle en la Guía del conjunto de datos. Para convertir tu conjunto de datos existente de otros formatos (como COCO, etc.) al formato YOLO , utiliza la herramienta JSON2YOLO de Ultralytics.

Val

Validate trained YOLO11n-seg model accuracy on the COCO8-seg dataset. No arguments are needed as the model conserva su formación data y argumentos como atributos del modelo.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-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=yolo11n-seg.pt  # val official model
yolo segment val model=path/to/best.pt  # val custom model

Predecir

Use a trained YOLO11n-seg model to run predictions on images.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-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=yolo11n-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

Ver todo predict detalles del modo en el Predecir página.

Exportar

Export a YOLO11n-seg model to a different format like ONNX, CoreML, etc.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-seg.pt")  # load an official model
model = YOLO("path/to/best.pt")  # load a custom trained model

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

yolo export model=yolo11n-seg.pt format=onnx  # export official model
yolo export model=path/to/best.pt format=onnx  # export custom trained model

Available YOLO11-seg export formats are in the table below. You can export to any format using the format argumento, es decir format='onnx' o format='engine'. Puedes predecir o validar directamente los modelos exportados, es decir. yolo predict model=yolo11n-seg.onnx. Se muestran ejemplos de uso de tu modelo una vez finalizada la exportación.

Ver todo export detalles en el Exportar página.

PREGUNTAS FRECUENTES

How do I train a YOLO11 segmentation model on a custom dataset?

To train a YOLO11 segmentation model on a custom dataset, you first need to prepare your dataset in the YOLO segmentation format. You can use tools like JSON2YOLO to convert datasets from other formats. Once your dataset is ready, you can train the model using Python or CLI commands:

from ultralytics import YOLO

# Load a pretrained YOLO11 segment model
model = YOLO("yolo11n-seg.pt")

# Train the model
results = model.train(data="path/to/your_dataset.yaml", epochs=100, imgsz=640)

yolo segment train data=path/to/your_dataset.yaml model=yolo11n-seg.pt epochs=100 imgsz=640

Consulta la página Configuración para ver más argumentos disponibles.

What is the difference between object detection and instance segmentation in YOLO11?

Object detection identifies and localizes objects within an image by drawing bounding boxes around them, whereas instance segmentation not only identifies the bounding boxes but also delineates the exact shape of each object. YOLO11 instance segmentation models provide masks or contours that outline each detected object, which is particularly useful for tasks where knowing the precise shape of objects is important, such as medical imaging or autonomous driving.

Why use YOLO11 for instance segmentation?

Ultralytics YOLO11 is a state-of-the-art model recognized for its high accuracy and real-time performance, making it ideal for instance segmentation tasks. YOLO11 Segment models come pretrained on the COCO dataset, ensuring robust performance across a variety of objects. Additionally, YOLO supports training, validation, prediction, and export functionalities with seamless integration, making it highly versatile for both research and industry applications.

How do I load and validate a pretrained YOLO segmentation model?

Loading and validating a pretrained YOLO segmentation model is straightforward. Here's how you can do it using both Python and CLI:

from ultralytics import YOLO

# Load a pretrained model
model = YOLO("yolo11n-seg.pt")

# Validate the model
metrics = model.val()
print("Mean Average Precision for boxes:", metrics.box.map)
print("Mean Average Precision for masks:", metrics.seg.map)

yolo segment val model=yolo11n-seg.pt

These steps will provide you with validation metrics like Mean Average Precision (mAP), crucial for assessing model performance.

How can I export a YOLO segmentation model to ONNX format?

Exporting a YOLO segmentation model to ONNX format is simple and can be done using Python or CLI commands:

from ultralytics import YOLO

# Load a pretrained model
model = YOLO("yolo11n-seg.pt")

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

yolo export model=yolo11n-seg.pt format=onnx

Para más detalles sobre la exportación a varios formatos, consulta la página Exportar.

📅 Created 11 months ago ✏️ Updated 2 days ago

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