yolov5部署在树莓派上
时间: 2025-02-01 20:40:42 浏览: 53
### 部署YOLOv5至树莓派
#### 准备工作
为了成功部署YOLOv5模型到树莓派,需确保拥有合适的硬件环境并安装必要的软件工具。对于树莓派4B而言,推荐利用OpenVINO工具套件来优化性能[^2]。
#### 安装依赖库
首先更新系统包管理器,并安装Python及其开发环境:
```bash
sudo apt-get update && sudo apt-get upgrade -y
sudo apt install python3-pip git cmake build-essential libatlas-base-dev
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
```
#### 获取YOLOv5源码
克隆官方GitHub仓库获取最新版本的YOLOv5代码:
```bash
git clone https://github.com/ultralytics/yolov5.git
cd yolov5
pip3 install -r requirements.txt
```
#### 转换模型格式
由于直接运行`.pt`文件效率低下,建议将其转化为更高效的中间表示形式(如ONNX),再进一步编译成适合嵌入式设备执行的IR格式。具体操作如下所示:
1. 将PyTorch模型导出为ONNX格式:
```python
import torch
from models.experimental import attempt_load
device = 'cpu'
model = attempt_load('yolov5s.pt', map_location=device) # 加载预训练权重
dummy_input = torch.randn(1, 3, 640, 640, device='cpu')
torch.onnx.export(model, dummy_input, "yolov5s.onnx", opset_version=11)
print("Exported model has been saved to yolov5s.onnx")
```
2. 使用Model Optimizer将ONNX文件转换为IR格式:
```bash
source /opt/intel/openvino/bin/setupvars.sh
mo --input_model yolov5s.onnx --output_dir ./ir_models/
```
#### 测试与验证
最后编写简单的推理脚本来加载IR模型并对图像数据集进行预测分析。注意调整输入尺寸以匹配原始训练配置。
```python
from openvino.runtime import Core
import cv2
import numpy as np
ie_core = Core()
net = ie_core.read_model(model="ir_models/yolov5s.xml")
compiled_model = ie_core.compile_model(net, "CPU")
image_path = "test.jpg"
img = cv2.imread(image_path)
resized_img = cv2.resize(img, (640, 640))
input_tensor = np.expand_dims(resized_img.transpose((2, 0, 1)), axis=0)
infer_request = compiled_model.create_infer_request()
result = infer_request.infer({0: input_tensor})
detections = result[list(result)[0]]
```
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