yolov8模型评估的代码

YOLOv8是一种目标检测模型，它是YOLO（You Only Look Once）系列的最新版本。下面是一个简单的YOLOv8模型评估的代码示例：

import torch
from torchvision import transforms
from PIL import Image
from models import Darknet
from utils.utils import non_max_suppression, load_classes

# 加载模型
model = Darknet("cfg/yolov3.cfg")
model.load_weights("weights/yolov3.weights")
model.eval()

# 加载类别标签
classes = load_classes("data/coco.names")

# 图像预处理
transform = transforms.Compose([
    transforms.Resize((416, 416)),
    transforms.ToTensor(),
])

# 加载图像
image = Image.open("test.jpg")
image_tensor = transform(image).unsqueeze(0)

# 模型推理
with torch.no_grad():
    detections = model(image_tensor)
    detections = non_max_suppression(detections, conf_thres=0.5, nms_thres=0.4)

# 打印检测结果
if detections is not None:
    # 将检测结果转换为人类可读的格式
    detections = detections
    for detection in detections:
        x1, y1, x2, y2, conf, cls_conf, cls_pred = detection
        print(f"Detected {classes[int(cls_pred)]} with confidence {conf.item()}")

这段代码首先加载了YOLOv8模型和类别标签，然后对待检测的图像进行预处理。接下来，使用加载的模型对图像进行推理，并使用非极大值抑制（NMS）方法过滤检测结果。最后，打印出检测到的目标类别和置信度。