yolo val segment data=coco-seg.yaml device=0
yolo val segment data=coco-seg.yaml batch=1 device=0|cpu
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.
例
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
YOLO セグメンテーション・データセットの形式は、データセット・ガイドに詳しく書かれている。既存のデータセットを他のフォーマット(COCOなど)からYOLO フォーマットに変換するには、Ultralytics のJSON2YOLOツールをご利用ください。
Validate trained YOLO11n-seg model accuracy on the COCO8-seg dataset. No arguments are needed as the model
トレーニング data
と引数をモデル属性として使用する。
例
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
Use a trained YOLO11n-seg model to run predictions on images.
例
詳細を見る predict
モードの詳細は 予測する ページを参照されたい。
Export a YOLO11n-seg model to a different format like ONNX, CoreML, etc.
例
Available YOLO11-seg export formats are in the table below. You can export to any format using the format
引数、すなわち format='onnx'
または format='engine'
.エクスポートされたモデルを直接予測または検証することができます。 yolo predict model=yolo11n-seg.onnx
.使用例は、エクスポート完了後に表示されます。
フォーマット | format 議論 | モデル | メタデータ | 論争 |
---|---|---|---|---|
PyTorch | - | yolo11n-seg.pt | ✅ | - |
TorchScript | torchscript | yolo11n-seg.torchscript | ✅ | imgsz , optimize , batch |
ONNX | onnx | yolo11n-seg.onnx | ✅ | imgsz , half , dynamic , simplify , opset , batch |
OpenVINO | openvino | yolo11n-seg_openvino_model/ | ✅ | imgsz , half , int8 , batch |
TensorRT | engine | yolo11n-seg.engine | ✅ | imgsz , half , dynamic , simplify , workspace , int8 , batch |
CoreML | coreml | yolo11n-seg.mlpackage | ✅ | imgsz , half , int8 , nms , batch |
TF SavedModel | saved_model | yolo11n-seg_saved_model/ | ✅ | imgsz , keras , int8 , batch |
TF GraphDef | pb | yolo11n-seg.pb | ❌ | imgsz , batch |
TF ライト | tflite | yolo11n-seg.tflite | ✅ | imgsz , half , int8 , batch |
TF エッジTPU | edgetpu | yolo11n-seg_edgetpu.tflite | ✅ | imgsz |
TF.js | tfjs | yolo11n-seg_web_model/ | ✅ | imgsz , half , int8 , batch |
PaddlePaddle | paddle | yolo11n-seg_paddle_model/ | ✅ | imgsz , batch |
MNN | mnn | yolo11n-seg.mnn | ✅ | imgsz , batch , int8 , half |
NCNN | ncnn | yolo11n-seg_ncnn_model/ | ✅ | imgsz , half , batch |
IMX500 | imx | yolo11n-seg_imx_model/ | ✅ | imgsz , int8 |
詳細を見る export
詳細は 輸出 ページを参照されたい。
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:
例
利用可能な引数については、コンフィギュレーション・ページを確認してください。
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.
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.
Loading and validating a pretrained YOLO segmentation model is straightforward. Here's how you can do it using both Python and CLI:
例
These steps will provide you with validation metrics like Mean Average Precision (mAP), crucial for assessing model performance.
Exporting a YOLO segmentation model to ONNX format is simple and can be done using Python or CLI commands:
例
様々なフォーマットへのエクスポートの詳細については、エクスポートのページを参照してください。