基于深度学习的高精度80类动物目标检测系统（PyTorch+Pyside6+YOLOv5模型）

**基于深度学习的高精度80类动物目标检测系统**

在本文中，我们将介绍如何使用 PyTorch、PySide6 和 YOLOv5 模型构建一个高精度的80 类动物目标检测系统。

### 系统架构我们的系统架构如下：

* **数据集**:我们将使用一个包含80 类动物图像的数据集。
* **模型**:我们将使用 YOLOv5 模型作为目标检测算法。
* **前端**:我们将使用 PySide6 构建一个简单的 GUI 前端。

### 数据集准备首先，我们需要准备我们的数据集。我们将使用一个包含80 类动物图像的数据集。

import osfrom torch.utils.data import Dataset, DataLoaderfrom PIL import Imageimport torchvision.transforms as transformsclass AnimalDataset(Dataset):
 def __init__(self, image_dir, transform=None):
 self.image_dir = image_dir self.transform = transform self.images = []
 for filename in os.listdir(image_dir):
 if filename.endswith(".jpg") or filename.endswith(".png"):
 self.images.append(os.path.join(image_dir, filename))

 def __len__(self):
 return len(self.images)

 def __getitem__(self, index):
 image_path = self.images[index]
 image = Image.open(image_path)
 if self.transform:
 image = self.transform(image)
 return image

import torchfrom torchvision.models import yolov5smodel = yolov5s(pretrained=True)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

import sysfrom PySide6.QtWidgets import QApplication, QWidget, QVBoxLayout, QPushButton, QLabelfrom PySide6.QtGui import QPixmapclass MainWindow(QWidget):
 def __init__(self):
 super().__init__()
 self.initUI()

 def initUI(self):
 self.setGeometry(100,100,800,600)
 layout = QVBoxLayout()
 self.setLayout(layout)

 # Create a label to display the image self.image_label = QLabel()
 layout.addWidget(self.image_label)

 # Create a button to load an image load_button = QPushButton("Load Image")
 load_button.clicked.connect(self.load_image)
 layout.addWidget(load_button)

 # Show the window self.show()

 def load_image(self):
 # Load the image from file image_path = "path_to_your_image.jpg"
 image = QPixmap(image_path)

 # Display the image in the label self.image_label.setPixmap(image)

import sysfrom PySide6.QtWidgets import QApplication, QWidgetfrom torchvision.transforms import ToTensorfrom PIL import Imageimport torchfrom animal_dataset import AnimalDatasetfrom yolov5_model import YOLOv5Modelclass MainWindow(QWidget):
 def __init__(self):
 super().__init__()
 self.initUI()

 def initUI(self):
 self.setGeometry(100,100,800,600)
 layout = QVBoxLayout()
 self.setLayout(layout)

 # Create a label to display the image self.image_label = QLabel()
 layout.addWidget(self.image_label)

 # Create a button to load an image load_button = QPushButton("Load Image")
 load_button.clicked.connect(self.load_image)
 layout.addWidget(load_button)

 # Show the window self.show()

 def load_image(self):
 # Load the image from file image_path = "path_to_your_image.jpg"
 image = Image.open(image_path)
 tensor = ToTensor()(image)
 model = YOLOv5Model()
 output = model(tensor)
 print(output)

 # Display the image in the label self.image_label.setText("Image loaded")

if __name__ == "__main__":
 app = QApplication(sys.argv)
 window = MainWindow()
 sys.exit(app.exec())