好了，我发现了它是如何工作的！参数grad_checkpoint=true必须在指定给object_detector.create（）函数的spec对象中设置！在spec中，模型检查点目录也可以指定！这就是我如何让它工作的：

spec = object_detector.EfficientDetLite0Spec(
    model_name='efficientdet-lite0',
    model_dir='/home/alex/checkpoints_1/',
    hparams='grad_checkpoint=true,strategy=gpus',
    epochs=30, batch_size=8,
    steps_per_execution=1, moving_average_decay=0,
    var_freeze_expr='(efficientnet|fpn_cells|resample_p6)',
    tflite_max_detections=25
)

model = object_detector.create(train_data, model_spec=spec, batch_size=8, 
    train_whole_model=True, validation_data=validation_data)

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TensorFlow Lite Model Maker不提供在训练期间保存检查点的直接方法，但您可以使用tensorboard。尝试以下操作：

import tensorflow as tf

checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
    filepath='path/to/checkpoints/model_checkpoint.h5',
    save_weights_only=True,
    save_best_only=True,
    monitor='val_loss',  # Choose a metric to monitor
    mode='min',  # 'min' or 'max' depending on the metric
)

tensorboard_callback = tf.keras.callbacks.TensorBoard(
    log_dir='path/to/tensorboard/logs',
    write_graph=True,
    write_images=True
)

# Train the model with callbacks
model.fit(
    train_data,
    validation_data=validation_data,
    epochs=10,  # Set the number of epochs
    callbacks=[checkpoint_callback, tensorboard_callback]
)

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如果你需要更多关于tensorboard的信息，请告诉我。