model.train(data="coco128-seg.yaml", epochs=3) # train the model metrics = model.val() # evaluate model performance on the validation set results = model("https://ultralytics.com/images/bus.jpg") # predict on an image path = model.export(format="onnx") # export the model to ONNX format

对于您的第一个问题，这是一个使用CSDN开发的模型训练和评估的示例代码。在这个例子中，我们使用了一个名为"coco128-seg.yaml"的数据集进行训练，并进行了3个epochs的训练。然后，我们评估了模型在验证集上的性能，并将结果存储在metrics变量中。接下来，我们使用模型对一张名为"bus.jpg"的图片进行预测，并将结果存储在results变量中。最后，我们将模型导出为ONNX格式，并将导出文件的路径存储在path变量中。

相关问题

model = IDLE_UNET() model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) model.fit(train_vol, train_seg, batch_size=40, epochs=1, validation_data=(val_vol, val_seg))报错怎么改

你的代码中似乎缺少了一些关键的信息，无法确定具体出错的原因。但是，根据报错信息 `Unexpected result of train_function (Empty logs)`，你可以尝试添加 `run_eagerly=True` 参数到 `model.compile()` 中，以启用 TensorFlow 的 Eager Execution 模式，从而更容易地查找错误，即：

model = IDLE_UNET()
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'], run_eagerly=True)
model.fit(train_vol, train_seg, batch_size=40, epochs=1, validation_data=(val_vol, val_seg))

如果这样还是无法解决问题，你可以进一步检查代码是否存在语法错误、数据是否正确加载等方面的问题。如果问题依然存在，你可以将完整的代码和报错信息贴出来，以便更好地定位问题。

设置训练和验证集路径 train_vol_path = "data/train/trainvol" train_seg_path = "data/train/trainseg" val_vol_path = "data/val/valvol" val_seg_path = "data/val/valseg" # 定义数据生成器 train_datagen = ImageDataGenerator(rescale=1./255) val_datagen = ImageDataGenerator(rescale=1./255) # 加载训练和验证数据 train_vol = train_datagen.flow_from_directory(train_vol_path, target_size=(256, 256), batch_size=32, class_mode=None) train_seg = train_datagen.flow_from_directory(train_seg_path, target_size=(256, 256), batch_size=32, class_mode=None) val_vol = val_datagen.flow_from_directory(val_vol_path, target_size=(256, 256), batch_size=32, class_mode=None) val_seg = val_datagen.flow_from_directory(val_seg_path, target_size=(256, 256), batch_size=32, class_mode=None) # 合并训练和验证数据 train_generator = zip(train_vol, train_seg) val_generator = zip(val_vol, val_seg) # 定义UNet模型 model = unet() # 编译模型 model.compile(optimizer=Adam(learning_rate=1e-4), loss='binary_crossentropy', metrics=['accuracy'])报错'NoneType' object has no attribute 'compile'怎么改代码

可以尝试将 `class_mode` 参数设置为 `'binary'` 或者其他适合数据集的值，来解决这个问题。修改后的代码如下所示：

# 设置训练和验证集路径
train_vol_path = "data/train/trainvol"
train_seg_path = "data/train/trainseg"
val_vol_path = "data/val/valvol"
val_seg_path = "data/val/valseg"

# 定义数据生成器
train_datagen = ImageDataGenerator(rescale=1./255)
val_datagen = ImageDataGenerator(rescale=1./255)

# 加载训练和验证数据
train_vol = train_datagen.flow_from_directory(train_vol_path, target_size=(256, 256), batch_size=32, class_mode='binary')
train_seg = train_datagen.flow_from_directory(train_seg_path, target_size=(256, 256), batch_size=32, class_mode='binary')
val_vol = val_datagen.flow_from_directory(val_vol_path, target_size=(256, 256), batch_size=32, class_mode='binary')
val_seg = val_datagen.flow_from_directory(val_seg_path, target_size=(256, 256), batch_size=32, class_mode='binary')

# 合并训练和验证数据
train_generator = zip(train_vol, train_seg)
val_generator = zip(val_vol, val_seg)

# 定义UNet模型
model = unet()

# 编译模型
model.compile(optimizer=Adam(learning_rate=1e-4), loss='binary_crossentropy', metrics=['accuracy'])