1.图像分类

from ultralytics import YOLO
# 0 参数配置
# 模型路径
model_path = r"./yolov8s-cls.pt"
# 数据集yaml文件路径
data_path = r"D:\YoloV8Manual\dataset\DogCat-cls"
# 训练轮数
epochs = 500
imgsz = 224
batch = 4
project = r"./AIModel"
name = "CatAndDog_Cls"
predict_ImgPath = r"dataset/DogCat-cls/test/cat"
save_predictImg_flag = True
exportType= "onnx"
exist_ok = True
# 1 加载模型
model = YOLO(model_path)
# 2 训练模型
model.train(data=data_path, epochs=epochs, imgsz=imgsz,batch=batch,workers=0,project=project,name=name,exist_ok=True)
# 3 验证模型
metrics = model.val(data=data_path)  # 在验证集上评估模型性能
# 4 模型预测
results = model.predict(source=predict_ImgPath,  imgsz=imgsz ,save=save_predictImg_flag,batch=batch)  # save plotted images
# 5 导出所需模式（以onnx为例）
model.export(format=exportType, imgsz=imgsz)

训练指标说明

验证指标说明

from ultralytics import YOLO
import os
# 0 参数配置
# 模型路径
model_path = r"D:\YoloV8Manual\AIModel\CatAndDog_Cls\weights\best.pt"
# 数据集yaml文件路径
data_path = r"D:\YoloV8Manual\dataset\DogCat-cls"
# 训练轮数
epochs = 1
imgsz = 224
batch = 4
project = r"./AIModel"
name = "CatAndDog_Cls"
predict_ImgPath = r"dataset/DogCat-cls/test/cat"
save_predictImg_flag = True
exportType= "onnx"
exist_ok = True
# 1 加载模型
model = YOLO(model_path)
# 2 训练模型
#model.train(data=data_path, epochs=epochs, imgsz=imgsz,batch=batch,workers=0,project=project,name=name,exist_ok=True)
# 3 验证模型
metrics = model.val(workers=0)  # 在验证集上评估模型性能
# 4 模型预测
#results = model.predict(source=predict_ImgPath,  imgsz=imgsz ,save=save_predictImg_flag,batch=batch)  # save plotted images
# 5 导出所需模式（以onnx为例）
#model.export(format=exportType, imgsz=imgsz)

模型预测结果

from ultralytics import YOLO
import os
# 0 参数配置
# 模型路径
model_path = r"D:\YoloV8Manual\AIModel\CatAndDog_Cls\weights\best.pt"
# 数据集yaml文件路径
data_path = r"D:\YoloV8Manual\dataset\DogCat-cls"
# 训练轮数
epochs = 1
imgsz = 224
batch = 4
project = r"./AIModel"
name = "CatAndDog_Cls"
predict_ImgPath = r"dataset/DogCat-cls/val/cat"
save_predictImg_flag = True
exportType= "onnx"
exist_ok = True
# 1 加载模型
model = YOLO(model_path)
# 2 训练模型
#model.train(data=data_path, epochs=epochs, imgsz=imgsz,batch=batch,workers=0,project=project,name=name,exist_ok=True)
# 3 验证模型
# metrics = model.val(workers=0)  # 在验证集上评估模型性能
# 4 模型预测
results = model.predict(source=predict_ImgPath,  imgsz=imgsz ,save=save_predictImg_flag,batch=batch)  # save plotted images
# 5 导出所需模式（以onnx为例）
#model.export(format=exportType, imgsz=imgsz)

模型导出

1.导出TensorRT

TensorRT环境配置

from ultralytics import YOLO
import os
# 0 参数配置
# 模型路径
model_path = r"D:\YoloV8Manual\AIModel\CatAndDog_Cls\weights\best.pt"
# 数据集yaml文件路径
data_path = r"D:\YoloV8Manual\dataset\DogCat-cls"
# 训练轮数
epochs = 1
imgsz = 224
batch = 4
project = r"./AIModel"
name = "CatAndDog_Cls"
predict_ImgPath = r"dataset/DogCat-cls/val/cat"
save_predictImg_flag = True
exportType= "engine"
exist_ok = True
# 1 加载模型
model = YOLO(model_path)
# 2 训练模型
# model.train(data=data_path, epochs=epochs, imgsz=imgsz,batch=batch,workers=0,project=project,name=name,exist_ok=True)
# 3 验证模型
# metrics = model.val(workers=0)  # 在验证集上评估模型性能
# 4 模型预测
# results = model.predict(source=predict_ImgPath,  imgsz=imgsz ,save=save_predictImg_flag,batch=batch)  # save plotted images
# 5 导出所需模式（以onnx为例）
model.export(format=exportType)

2.导出OpenVino

OpenVino环境配置

from ultralytics import YOLO
import os
# 0 参数配置
# 模型路径
model_path = r"D:\YoloV8Manual\AIModel\CatAndDog_Cls\weights\best.pt"
# 数据集yaml文件路径
data_path = r"D:\YoloV8Manual\dataset\DogCat-cls"
# 训练轮数
epochs = 1
imgsz = 224
batch = 4
project = r"./AIModel"
name = "CatAndDog_Cls"
predict_ImgPath = r"dataset/DogCat-cls/val/cat"
save_predictImg_flag = True
exportType= "openvino"
exist_ok = True
# 1 加载模型
model = YOLO(model_path)
# 2 训练模型
# model.train(data=data_path, epochs=epochs, imgsz=imgsz,batch=batch,workers=0,project=project,name=name,exist_ok=True)
# 3 验证模型
# metrics = model.val(workers=0)  # 在验证集上评估模型性能
# 4 模型预测
# results = model.predict(source=predict_ImgPath,  imgsz=imgsz ,save=save_predictImg_flag,batch=batch)  # save plotted images
# 5 导出所需模式（以onnx为例）
model.export(format=exportType)