Python Kullanım

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!

İzle: Mastering Ultralytics YOLO11: Python

Örneğin, kullanıcılar bir modeli yükleyebilir, eğitebilir, bir doğrulama kümesi üzerindeki performansını değerlendirebilir ve hatta sadece birkaç satır kodla ONNX formatına aktarabilir.

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")

Tren

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.

from ultralytics import YOLO

model = YOLO("yolo11n.pt")  # pass any model type
results = model.train(epochs=5)

from ultralytics import YOLO

model = YOLO("yolo11n.yaml")
results = model.train(data="coco8.yaml", epochs=5)

model = YOLO("last.pt")
results = model.train(resume=True)

Tren Örnekleri

Val

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.

from ultralytics import YOLO

# Load a YOLO11 model
model = YOLO("yolo11n.yaml")

# Train the model
model.train(data="coco8.yaml", epochs=5)

# Validate on training data
model.val()

from ultralytics import YOLO

# Load a YOLO11 model
model = YOLO("yolo11n.yaml")

# Train the model
model.train(data="coco8.yaml", epochs=5)

# Validate on separate data
model.val(data="path/to/separate/data.yaml")

Val Örnekleri

Tahmin Et

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.

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()

Örnekleri Tahmin Et

İhracat

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.

from ultralytics import YOLO

model = YOLO("yolo11n.pt")
model.export(format="onnx", dynamic=True)

from ultralytics import YOLO

model = YOLO("yolo11n.pt")
model.export(format="onnx", device=0)

İhracat Örnekleri

Parça

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.

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")

Parça Örnekleri

Benchmark

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 metrikleri (nesne algılama ve segmentasyon için) veya accuracy_top5 metrikleri (sınıflandırma için) ve ONNX, OpenVINO, TensorRT ve diğerleri gibi çeşitli dışa aktarma biçimlerinde görüntü başına milisaniye cinsinden çıkarım süresi. Bu bilgiler, kullanıcıların hız ve doğruluk gereksinimlerine göre kendi özel kullanım durumları için en uygun dışa aktarma biçimini seçmelerine yardımcı olabilir.

from ultralytics.utils.benchmarks import benchmark

# Benchmark
benchmark(model="yolo11n.pt", data="coco8.yaml", imgsz=640, half=False, device=0)

Benchmark Örnekleri

Eğitmenleri Kullanma

YOLO model sınıfı, Trainer sınıfları üzerinde üst düzey bir sarmalayıcıdır. Her YOLO görevinin, şu sınıftan miras alan kendi eğitmeni vardır BaseTrainer.

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)

Özel görevleri desteklemek veya Ar-Ge fikirlerini keşfetmek için Eğitmenleri kolayca özelleştirebilirsiniz. Özelleştirme hakkında daha fazla bilgi edinin Trainers, Validators ve Predictors Özelleştirme Bölümünde proje ihtiyaçlarınıza uyacak şekilde ayarlayın.

Özelleştirme eğitimleri

SSS

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()

Tahmin Modu bölümümüzde daha ayrıntılı örnekler görebilirsiniz.

What are the different modes available in YOLO11?

Ultralytics YOLO11 provides various modes to cater to different machine learning workflows. These include:

• Tren: Özel veri kümeleri kullanarak bir modeli eğitin.
• Val: Model performansını bir doğrulama seti üzerinde doğrulayın.
• Tahmin Et: Yeni görüntüler veya video akışları hakkında tahminlerde bulunun.
• İhracat: Modelleri ONNX, TensorRT gibi çeşitli formatlara aktarın.
• Parça: Video akışlarında gerçek zamanlı nesne izleme.
• Benchmark: Farklı konfigürasyonlarda kıyaslama modeli performansı.

Her mod, model geliştirme ve dağıtımının farklı aşamaları için kapsamlı işlevler sağlamak üzere tasarlanmıştır.

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)

Eğitim hakkında daha fazla bilgi ve örnek kullanım bağlantıları için Tren Modu sayfamızı ziyaret edin.

How do I export YOLO11 models for deployment?

Exporting YOLO11 models in a format suitable for deployment is straightforward with the export işlevini kullanabilirsiniz. Örneğin, bir modeli ONNX formatında dışa aktarabilirsiniz:

from ultralytics import YOLO

# Load the YOLO model
model = YOLO("yolo11n.pt")

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

Çeşitli dışa aktarma seçenekleri için Dışa Aktarma Modu belgelerine bakın.

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")

Ayrıntılı örnekler ve kullanım için Val Modu sayfasını kontrol edin.

📅 Created 11 months ago ✏️ Updated 0 days ago

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