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yolov11输出预测结果

时间: 2025-01-14 17:49:34 浏览: 332

### YOLOv11 模型预测输出方法对于YOLOv11模型而言，获取其预测输出通常涉及加载预训练权重、准备输入图像以及处理网络输出。下面提供了一个Python代码示例来展示这一过程： ```python import torch from yolov11 import YOLOv11 # 假设这是YOLOv11的定义文件 def load_model(weights_path): model = YOLOv11() device = 'cuda' if torch.cuda.is_available() else 'cpu' state_dict = torch.load(weights_path, map_location=device) model.load_state_dict(state_dict) model.eval().to(device) return model def preprocess_image(image_path): image = Image.open(image_path).convert('RGB') transform = transforms.Compose([ transforms.Resize((640, 640)), # 调整到适合YOLOv11输入尺寸 transforms.ToTensor(), ]) tensor = transform(image).unsqueeze(0) # 添加批次维度 return tensor def postprocess_predictions(predictions, conf_threshold=0.5): boxes = [] scores = [] labels = [] for pred in predictions: box = pred[:4].detach().numpy() score = pred[4].item() label = pred[5:].argmax().item() if score >= conf_threshold: boxes.append(box) scores.append(score) labels.append(label) return boxes, scores, labels model = load_model('path/to/weights.pth') # 加载模型并设置为评估模式 image_tensor = preprocess_image('path/to/image.jpg') # 预处理图片 with torch.no_grad(): outputs = model(image_tensor) # 获取模型预测结果 boxes, scores, labels = postprocess_predictions(outputs) # 后处理得到最终检测框和其他信息 ``` 上述代码展示了如何通过PyTorch库加载YOLOv11模型及其权重，并对单张图片执行推理操作以获得边界框、置信度分数和类别标签等预测数据。

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