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YOLOv11 模型优化与部署实战

2025年09月10日计算机视觉

YOLOv11 模型优化生产部署

# YOLOv11 模型优化与部署

## 1. 模型压缩技术

### 1.1 量化训练(QAT)

```python

from yolov11.quantization import QAT

qat = QAT(

model="yolov11s.pt",

calib_data="calib/",  # 校准数据集

quant_bits=8  # 8位量化

)

qat.quantize()

```

### 1.2 通道剪枝

```python

from yolov11.pruning import ChannelPruner

pruner = ChannelPruner(

model="yolov11s.pt",

strategy="l1-norm",  # 剪枝策略

amount=0.3  # 剪枝比例

)

pruner.prune()

```

## 2. 部署方案对比

### 2.1 方案选型矩阵

|------|---------|------------|--------|

| ONNX Runtime | 15.2 | 1024 | 通用服务器 |

| TensorRT | 6.8 | 780 | NVIDIA GPU |

| OpenVINO | 12.4 | 890 | Intel CPU |

| CoreML | 18.6 | 1100 | Apple设备 |

### 2.2 TensorRT部署

```python

from yolov11.export import TRTExporter

exporter = TRTExporter(

model="yolov11s.pt",

precision="FP16",  # 精度模式

workspace=4096  # 显存限制(MB)

)

exporter.export("yolov11s.engine")

```

## 3. 生产级API开发

### 3.1 FastAPI服务

```python

from fastapi import FastAPI, UploadFile

from yolov11.inference import Detector

app = FastAPI()

detector = Detector("yolov11s.engine")

@app.post("/detect")

async def detect(image: UploadFile):

results = detector.predict(await image.read())

return {"detections": results}

```

### 3.2 性能优化技巧

```python

# 启用异步批处理

detector.enable_async(batch_size=4)

# 内存池优化

detector.enable_memory_pool()

```

## 4. 边缘设备适配

### 4.1 Jetson部署

```bash

# 转换TensorRT引擎

./yolov11_export --weights yolov11s.pt --device jetson

```

### 4.2 Android集成

```java

// Android NDK调用示例

YOLOv11 detector = new YOLOv11("yolov11s.tflite");

Bitmap input = BitmapFactory.decodeFile("input.jpg");

List<Detection> results = detector.detect(input);

```

## 5. 监控与维护

### 5.1 Prometheus监控

```yaml

# metrics配置示例

metrics:

- name: inference_latency

type: histogram

buckets: [10, 25, 50, 100]

- name: gpu_utilization

type: gauge

```

### 5.2 模型热更新

```python

detector.reload_model("yolov11s_v2.pt")  # 不中断服务更新

```

## 6. 性能基准测试

### 6.1 测试脚本

```bash

# 全量性能测试

python benchmark.py \

--model yolov11s.engine \

--data test_images/ \

--batch 1 4 8  # 测试不同batchsize

```

### 6.2 优化checklist

- [ ] 量化验证通过

- [ ] 内存泄漏测试

- [ ] 压力测试报告

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