【计算机视觉】YOLOv8如何使用？（含源代码）
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Boost your Python projects with object detection, segmentation and classification using YOLOv8. Explore how to load, train, validate, predict, export, track and benchmark models with ease. |
YOLOv8, Ultralytics, Python, object detection, segmentation, classification, model training, validation, prediction, model export, benchmark, real-time tracking |
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。