3. 以下推荐算法中,哪个是和树相关的? 15分 A.DNN模型 B.DR模型 C.TDM 模型 D.FM 模型
时间: 2024-01-24 19:19:20 浏览: 28
B.DR模型。DR模型(Decision Tree Regression)是一种基于树结构的回归模型,它可以对连续型变量进行预测,并且可以处理离散型特征。在DR模型中,样本数据会被分成多个子集,每个子集对应一颗决策树,最后将所有子集对应的决策树的结果进行加权求和,得到最终的预测结果。因此,DR模型是和树相关的推荐算法。
相关问题
OpenCvSharp.Dnn 调用onnx模型
OpenCvSharp.Dnn是一个基于OpenCV的深度学习模块,它提供了一种方便的方式来使用深度学习模型进行图像处理和计算机视觉任务。在使用OpenCvSharp.Dnn调用ONNX模型时,你可以按照以下步骤进行操作:
1. 导入必要的命名空间:
```csharp
using OpenCvSharp;
using OpenCvSharp.Dnn;
```
2. 加载ONNX模型:
```csharp
Net net = CvDnn.ReadNetFromONNX("path/to/your/onnx/model.onnx");
```
这里的"path/to/your/onnx/model.onnx"是你的ONNX模型文件的路径。
3. 进行推理:
```csharp
Mat inputBlob = CvDnn.BlobFromImage(image, scalefactor: 1.0, size: new Size(224, 224), mean: new Scalar(0, 0, 0), swapRB: true, crop: false);
net.SetInput(inputBlob);
Mat outputBlob = net.Forward();
```
这里的image是你要进行推理的输入图像,可以是OpenCvSharp中的Mat对象。
4. 处理输出结果:
```csharp
float[] result = outputBlob.GetData<float>();
```
这里的result是输出结果,你可以根据具体的模型和任务进行后续处理。
cv2.dnn.readNet如何加载yolov5模型
可以按照以下步骤使用`cv2.dnn.readNet`加载yolov5模型:
1. 下载yolov5模型权重文件和配置文件。
2. 加载权重文件和配置文件:
```python
model_weights = "yolov5s.pt"
model_config = "yolov5s.yaml"
net = cv2.dnn.readNet(model_weights, model_config)
```
3. 设置预测输入尺寸:
```python
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
net.setPreferableLayout(cv2.dnn.DNN_TARGET_CUDA_FP16)
net.setInputSize(640, 640)
```
4. 设置类别标签:
```python
class_labels = ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"]
```
5. 进行预测:
```python
image = cv2.imread("image.jpg")
blob = cv2.dnn.blobFromImage(image, 1/255.0, (640, 640), swapRB=True, crop=False)
net.setInput(blob)
outputs = net.forward(net.getUnconnectedOutLayersNames())
```
6. 处理预测结果:
```python
confidence_threshold = 0.5
nms_threshold = 0.4
for output in outputs:
for detection in output:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > confidence_threshold:
center_x = int(detection[0] * image.shape[1])
center_y = int(detection[1] * image.shape[0])
width = int(detection[2] * image.shape[1])
height = int(detection[3] * image.shape[0])
left = int(center_x - width / 2)
top = int(center_y - height / 2)
cv2.rectangle(image, (left, top), (left + width, top + height), (0, 255, 0), 2)
cv2.putText(image, class_labels[class_id], (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
```
以上代码仅为示例,具体实现可能需要根据自己的需求进行修改。
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