【计算机视觉】YOLOv8如何使用？（含源代码）

一、python usage

欢迎来到 yolov8 python 使用文档！ 本指南旨在帮助您将 yolov8 无缝集成到您的 python 项目中，以进行对象检测、分割和分类。 在这里，您将学习如何加载和使用预训练模型、训练新模型以及对图像执行预测。 对于任何希望将 yolov8 纳入其 python 项目的人来说，易于使用的 python 界面都是宝贵的资源，可让您快速实现高级对象检测功能。

让我们开始吧！ 例如，用户可以加载模型、训练模型、评估其在验证集上的性能，甚至只需几行代码即可将其导出为 onnx 格式。

```python
from ultralytics import yolo

# create a new yolo model from scratch
model = yolo('yolov8n.yaml')

# load a pretrained yolo model (recommended for training)
model = yolo('yolov8n.pt')

# train the model using the 'coco128.yaml' dataset for 3 epochs
results = model.train(data='coco128.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')
```

二、train

训练模式用于在自定义数据集上训练 yolov8 模型。 在此模式下，使用指定的数据集和超参数来训练模型。 训练过程涉及优化模型的参数，以便它能够准确预测图像中对象的类别和位置。

=== "from pretrained(recommended)"
    ```python
    from ultralytics import yolo

    model = yolo('yolov8n.pt') # pass any model type
    results = model.train(epochs=5)
    ```

=== "from scratch"
    ```python
    from ultralytics import yolo

    model = yolo('yolov8n.yaml')
    results = model.train(data='coco128.yaml', epochs=5)
    ```

=== "resume"
    ```python
    model = yolo("last.pt")
    results = model.train(resume=true)
    ```

三、val

val 模式用于在训练后验证 yolov8 模型。 在此模式下，模型在验证集上进行评估，以衡量其准确性和泛化性能。 该模式可用于调整模型的超参数以提高其性能。

=== "val after training"
    ```python
      from ultralytics import yolo

      model = yolo('yolov8n.yaml')
      model.train(data='coco128.yaml', epochs=5)
      model.val()  # it'll automatically evaluate the data you trained.
    ```

=== "val independently"
    ```python
      from ultralytics import yolo

      model = yolo("model.pt")
      # it'll use the data yaml file in model.pt if you don't set data.
      model.val()
      # or you can set the data you want to val
      model.val(data='coco128.yaml')
    ```

四、predict

预测模式用于使用经过训练的 yolov8 模型对新图像或视频进行预测。 在此模式下，模型从检查点文件加载，用户可以提供图像或视频来执行推理。 该模型预测输入图像或视频中对象的类别和位置。

=== "from source"
    ```python
    from ultralytics import yolo
    from pil import image
    import cv2

    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 usage"
    ```python
    # 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()
    ```

五、export

导出模式用于将yolov8模型导出为可用于部署的格式。 在此模式下，模型将转换为可供其他软件应用程序或硬件设备使用的格式。 将模型部署到生产环境时，此模式非常有用。

=== "export to onnx"

    export an official yolov8n model to onnx with dynamic batch-size and image-size.
    ```python
      from ultralytics import yolo

      model = yolo('yolov8n.pt')
      model.export(format='onnx', dynamic=true)
    ```

=== "export to tensorrt"

    export an official yolov8n model to tensorrt on `device=0` for acceleration on cuda devices.
    ```python
      from ultralytics import yolo

      model = yolo('yolov8n.pt')
      model.export(format='onnx', device=0)
    ```

六、track

跟踪模式用于使用 yolov8 模型实时跟踪对象。 在此模式下，模型从检查点文件加载，用户可以提供实时视频流来执行实时对象跟踪。 此模式对于监控系统或自动驾驶汽车等应用非常有用。

=== "python"

    ```python
    from ultralytics import yolo

    # load a model
    model = yolo('yolov8n.pt')  # load an official detection model
    model = yolo('yolov8n-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")
    ```

七、benchmark

基准模式用于分析 yolov8 各种导出格式的速度和准确性。 基准测试提供了有关导出格式的大小、其 map50-95 指标（用于对象检测和分割）或 precision_top5 指标（用于分类）以及跨各种导出格式（例如 onnx、openvino、tensorrt）的每个图像的推理时间（以毫秒为单位）的信息 和别的。 此信息可以帮助用户根据其对速度和准确性的要求，选择适合其特定用例的最佳导出格式。

=== "python"

    benchmark an official yolov8n model across all export formats.
    ```python
    from ultralytics.utils.benchmarks import benchmark

    # benchmark
    benchmark(model='yolov8n.pt', data='coco8.yaml', imgsz=640, half=false, device=0)
    ```

八、using trainers

yolo 模型类是 trainer 类的高级包装。 每个 yolo 任务都有自己的训练器，该训练器继承自 basetrainer。

    ```python
    from ultralytics.models.yolo import detectiontrainer, detectionvalidator, detectionpredictor

    # 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)
    ```

您可以轻松自定义培训师以支持自定义任务或探索研发想法。 在定制部分了解有关定制训练器、验证器和预测器以满足您的项目需求的更多信息。