7 篇文章带有标签 “YOLO”

Ultralytics YOLOv8.0.92 🚀 Python-3.10.7 torch-2.0.0+cpu CPU
Model summary (fused): 168 layers, 3006038 parameters, 0 gradients, 8.1 GFLOPs

image 1/304 /usr/src/datasets/platen-switch/images/train/1.jpg: 480x640 22 closes, 14 opens, 51.7ms
image 2/304 /usr/src/datasets/platen-switch/images/train/2.jpg: 384x640 12 closes, 24 opens, 45.7ms
image 3/304 /usr/src/datasets/platen-switch/images/train/3.jpg: 576x640 12 closes, 33 opens, 62.3ms

Speed: 3.7ms preprocess, 45.0ms inference, 1.3ms postprocess per image at shape (1, 3, 640, 640)

.vscode/launch.json

{
    "version": "0.2.0",
    "configurations": [
        {
            "name": "Python: FastAPI",
            "type": "python",
            "request": "launch",
            "module": "uvicorn",
            "args": [
                "app.main:app",
                "--reload"
            ],
            "jinja": true,
            "justMyCode": true
        }
    ]
}

• --reload 修改后可以自动加载，适用于开发。
• Debug FastAPI in VS Code IDE
• Debug FastAPI application in VSCode

docker build docker buildx build 如何构建多架构 Docker 镜像？

• CPU

docker pull ultralytics/ultralytics:latest-cpu

• Apple Silicon

docker pull ultralytics/ultralytics:latest-arm64

• 配置

clearml-init

• 集成

from clearml import Task
task = Task.init(project_name="my project", task_name="my task")

对检测出来的结果裁剪分类保存

yolo predict data=project/data.yaml model=runs/detect/train/weights/best.pt source=project/images/val/ save_crop=true

• classes=0 或 classes=[0,2,5] 过滤指定的类别

导出的模型格式

训练结果存储在目录 runs/train/

浏览器中访问：https://wandb.ai/

在 Ubuntu20.04 系统上使用4张GPU卡基于容器训练模型。

• 运行容器

$ docker run --ipc=host --runtime=nvidia -it --name project_name-yolov5 \
    -v project_dir:/usr/src/app/project ultralytics/yolov5:latest

• 替换所有模型网络的类别

$ sed -i 's/nc: 80/nc: 2/g' project/models/yolov5?.yaml

验证替换结果 $ head -n 2 project/models/yolov5?.yaml ==> project/models/yolov5l.yaml <== # parameters nc: 2 # number of classes ==> project/models/yolov5m.yaml <== # parameters nc: 2 # number of classes ==> project/models/yolov5s.yaml <== # parameters nc: 2 # number of classes ==> project/models/yolov5x.