Win10环境下yolov8快速配置与测试 - FeiYull
source link: https://www.cnblogs.com/feiyull/p/17078770.html
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win10下亲测有效！（如果想在tensorrt+cuda下部署yolov8，直接看第五5章）
yolov8 官方仓库: https://github.com/ultralytics/ultralytics
一、win10下创建yolov8环境
# 注：python其他版本在win10下，可能有坑，我已经替你踩坑了，这里python3.9亲测有效 conda create -n yolov8 python=3.9 -y conda activate yolov8 pip install ultralytics -i https://pypi.tuna.tsinghua.edu.cn/simple
二、推理图像
模型下载地址：
# download offical weights(".pt" file)
https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt
https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt
https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt
https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt
https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt
https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x6.pt
这里下载yolov8n为例子，原图图下图：
我们将图像和yolov8n.pt放到路径：d:/Data/，推理：
yolo predict model="d:/Data/yolov8n.pt" source="d:/Data/6406407.jpg"
效果如图：
3.1 快速训练coco128数据集
在win10下，创建路径：D:\CodePython\yolov8，将这个5Mb的数据集下载并解压在目录，coco128数据集快速下载：https://share.weiyun.com/C0noWh5W
新建train.py文件，代码如下：、
from ultralytics import YOLO
# Load a model
# yaml会自动下载
model = YOLO("yolov8n.yaml") # build a new model from scratch
model = YOLO("d:/Data/yolov8n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="coco128.yaml", epochs=100, imgsz=640)
训练指令：
python train.py
如下图训练状态：
3.2 预测
新建predict.py文件，代码如下：
from ultralytics import YOLO
# Load a model
model = YOLO("d:/Data/yolov8n.pt") # load an official model
# Predict with the model
results = model("d:/Data/6406407.jpg") # predict on an image
预测指令：
python predict.py
如下图预测窗口打印：
四、导出onnx
pip install onnx yolo mode=export model="d:/Data/yolov8n.pt" format=onnx dynamic=True
五、yolov8的tensorrt部署加速
《YOLOV8部署保姆教程》：https://www.cnblogs.com/feiyull/p/17066486.html
TensorRT-Alpha基于tensorrt+cuda c++实现模型end2end的gpu加速，支持win10、linux，在2023年已经更新模型：YOLOv8, YOLOv7, YOLOv6, YOLOv5, YOLOv4, YOLOv3, YOLOX, YOLOR,pphumanseg,u2net,EfficientDet。
Windows10教程正在制作，可以关注TensorRT-Alpha：https://github.com/FeiYull/TensorRT-Alpha
🚀快速看看yolov8n 在移动端RTX2070m(8G)的新能表现：
| model | video resolution | model input size | GPU Memory-Usage | GPU-Util |
|---|---|---|---|---|
| yolov8n | 1920x1080 | 8x3x640x640 | 1093MiB/7982MiB | 14% |
下图是yolov8n的运行时间开销，单位是ms：
更多TensorRT-Alpha测试录像在B站视频：
B站：YOLOv8n
B站：YOLOv8s
更多训练指引，请看官方文档。
- # 🔥 yolov8 官方仓库: https://github.com/ultralytics/ultralytics
- # 🔥 yolov8 官方中文教程：https://github.com/ultralytics/ultralytics/blob/main/README.zh-CN.md
- # 🔥 yolov8 官方训练指引: https://docs.ultralytics.com/reference/base_trainer/
- # 🔥 yolov8 官方快速教程: https://docs.ultralytics.com/quickstart/
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