Python 使用方法
Welcome to the YOLO11 Python Usage documentation! This guide is designed to help you seamlessly integrate YOLO11 into your Python projects for object detection, segmentation, and classification. Here, you'll learn how to load and use pretrained models, train new models, and perform predictions on images. The easy-to-use Python interface is a valuable resource for anyone looking to incorporate YOLO11 into their Python projects, allowing you to quickly implement advanced object detection capabilities. Let's get started!
观看： Mastering Ultralytics YOLO11: Python
例如，用户只需几行代码就能加载模型、对其进行训练、评估其在验证集上的性能，甚至将其导出为ONNX 格式。
Python
from ultralytics import YOLO
# Create a new YOLO model from scratch
model = YOLO("yolo11n.yaml")
# Load a pretrained YOLO model (recommended for training)
model = YOLO("yolo11n.pt")
# Train the model using the 'coco8.yaml' dataset for 3 epochs
results = model.train(data="coco8.yaml", epochs=3)
# Evaluate the model's performance on the validation set
results = model.val()
# Perform object detection on an image using the model
results = model("https://ultralytics.com/images/bus.jpg")
# Export the model to ONNX format
success = model.export(format="onnx")
火车
Train mode is used for training a YOLO11 model on a custom dataset. In this mode, the model is trained using the specified dataset and hyperparameters. The training process involves optimizing the model's parameters so that it can accurately predict the classes and locations of objects in an image.
火车
瓦尔
Val mode is used for validating a YOLO11 model after it has been trained. In this mode, the model is evaluated on a validation set to measure its accuracy and generalization performance. This mode can be used to tune the hyperparameters of the model to improve its performance.
瓦尔
预测
Predict mode is used for making predictions using a trained YOLO11 model on new images or videos. In this mode, the model is loaded from a checkpoint file, and the user can provide images or videos to perform inference. The model predicts the classes and locations of objects in the input images or videos.
预测
import cv2
from PIL import Image
from ultralytics import YOLO
model = YOLO("model.pt")
# accepts all formats - image/dir/Path/URL/video/PIL/ndarray. 0 for webcam
results = model.predict(source="0")
results = model.predict(source="folder", show=True) # Display preds. Accepts all YOLO predict arguments
# from PIL
im1 = Image.open("bus.jpg")
results = model.predict(source=im1, save=True) # save plotted images
# from ndarray
im2 = cv2.imread("bus.jpg")
results = model.predict(source=im2, save=True, save_txt=True) # save predictions as labels
# from list of PIL/ndarray
results = model.predict(source=[im1, im2])
# results would be a list of Results object including all the predictions by default
# but be careful as it could occupy a lot memory when there're many images,
# especially the task is segmentation.
# 1. return as a list
results = model.predict(source="folder")
# results would be a generator which is more friendly to memory by setting stream=True
# 2. return as a generator
results = model.predict(source=0, stream=True)
for result in results:
# Detection
result.boxes.xyxy # box with xyxy format, (N, 4)
result.boxes.xywh # box with xywh format, (N, 4)
result.boxes.xyxyn # box with xyxy format but normalized, (N, 4)
result.boxes.xywhn # box with xywh format but normalized, (N, 4)
result.boxes.conf # confidence score, (N, 1)
result.boxes.cls # cls, (N, 1)
# Segmentation
result.masks.data # masks, (N, H, W)
result.masks.xy # x,y segments (pixels), List[segment] * N
result.masks.xyn # x,y segments (normalized), List[segment] * N
# Classification
result.probs # cls prob, (num_class, )
# Each result is composed of torch.Tensor by default,
# in which you can easily use following functionality:
result = result.cuda()
result = result.cpu()
result = result.to("cpu")
result = result.numpy()
出口
Export mode is used for exporting a YOLO11 model to a format that can be used for deployment. In this mode, the model is converted to a format that can be used by other software applications or hardware devices. This mode is useful when deploying the model to production environments.
出口
Export an official YOLO11n model to ONNX with dynamic batch-size and image-size.
轨道
Track mode is used for tracking objects in real-time using a YOLO11 model. In this mode, the model is loaded from a checkpoint file, and the user can provide a live video stream to perform real-time object tracking. This mode is useful for applications such as surveillance systems or self-driving cars.
轨道
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt") # load an official detection model
model = YOLO("yolo11n-seg.pt") # load an official segmentation model
model = YOLO("path/to/best.pt") # load a custom model
# Track with the model
results = model.track(source="https://youtu.be/LNwODJXcvt4", show=True)
results = model.track(source="https://youtu.be/LNwODJXcvt4", show=True, tracker="bytetrack.yaml")
基准
Benchmark mode is used to profile the speed and accuracy of various export formats for YOLO11. The benchmarks provide information on the size of the exported format, its mAP50-95 指标（用于物体检测和分割）或 accuracy_top5 度量（用于分类），以及不同导出格式（如ONNX,OpenVINO,TensorRT 等）下每幅图像的推理时间（以毫秒为单位）。这些信息可以帮助用户根据他们对速度和准确性的要求，为他们的特定使用案例选择最佳的导出格式。
基准
使用培训师
YOLO 模型类是训练器类的高级封装。每个YOLO 任务都有自己的训练器，训练器继承自 BaseTrainer.
检测训练器示例
from ultralytics.models.yolo import DetectionPredictor, DetectionTrainer, DetectionValidator
# trainer
trainer = DetectionTrainer(overrides={})
trainer.train()
trained_model = trainer.best
# Validator
val = DetectionValidator(args=...)
val(model=trained_model)
# predictor
pred = DetectionPredictor(overrides={})
pred(source=SOURCE, model=trained_model)
# resume from last weight
overrides["resume"] = trainer.last
trainer = detect.DetectionTrainer(overrides=overrides)
您可以轻松定制培训师，以支持自定义任务或探索研发思路。了解有关定制的更多信息 Trainers, Validators 和 Predictors 以满足您在定制部分的项目需求。
常见问题
How can I integrate YOLO11 into my Python project for object detection?
Integrating Ultralytics YOLO11 into your Python projects is simple. You can load a pre-trained model or train a new model from scratch. Here's how to get started:
from ultralytics import YOLO
# Load a pretrained YOLO model
model = YOLO("yolo11n.pt")
# Perform object detection on an image
results = model("https://ultralytics.com/images/bus.jpg")
# Visualize the results
for result in results:
result.show()
更多详细示例，请参阅我们的 "预测模式"部分。
What are the different modes available in YOLO11?
Ultralytics YOLO11 provides various modes to cater to different machine learning workflows. These include:
- 火车:使用自定义数据集训练模型。
- Val:在验证集上验证模型性能。
- 预测:对新图像或视频流进行预测。
- 出口:将模型导出为各种格式，如ONNX 、TensorRT 。
- 跟踪:视频流中的实时目标跟踪
- 基准:不同配置下的模型性能基准。
每种模式都旨在为模型开发和部署的不同阶段提供全面的功能。
How do I train a custom YOLO11 model using my dataset?
To train a custom YOLO11 model, you need to specify your dataset and other hyperparameters. Here's a quick example:
from ultralytics import YOLO
# Load the YOLO model
model = YOLO("yolo11n.yaml")
# Train the model with custom dataset
model.train(data="path/to/your/dataset.yaml", epochs=10)
有关培训的更多详情和使用示例的超链接，请访问我们的列车模式页面。
How do I export YOLO11 models for deployment?
Exporting YOLO11 models in a format suitable for deployment is straightforward with the export 功能。例如，您可以将模型导出为ONNX 格式：
from ultralytics import YOLO
# Load the YOLO model
model = YOLO("yolo11n.pt")
# Export the model to ONNX format
model.export(format="onnx")
有关各种导出选项，请参阅导出模式文档。
Can I validate my YOLO11 model on different datasets?
Yes, validating YOLO11 models on different datasets is possible. After training, you can use the validation mode to evaluate the performance:
from ultralytics import YOLO
# Load a YOLO11 model
model = YOLO("yolo11n.yaml")
# Train the model
model.train(data="coco8.yaml", epochs=5)
# Validate the model on a different dataset
model.val(data="path/to/separate/data.yaml")
有关详细示例和用法，请查看Val Mode页面。
