How to implement early stopping in TensorFlow Object Detection API

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1

I’m trying to add an early stopping mechanism to my training with the TensorFlow Object Detection API. I found a piece of code online that I used as a reference. Below is my version of the EarlyStoppingHook class that I adapted from the online example:

class EarlyStoppingHook(session_run_hook.SessionRunHook):
    """Hook that stops training based on a certain condition."""

    def __init__(self, metric='validation_loss', delta=0, patience=0,
                 mode='auto'):
        self.metric = metric
        self.patience = patience
        self.delta = delta
        self.wait_time = 0
        self.max_wait_time = 0
        self.current_step = 0

        if mode not in ['auto', 'min', 'max']:
            logging.warning('Unknown mode %s, using auto mode.', mode)
            mode = 'auto'

        if mode == 'min':
            self.monitor_op = np.less
        elif mode == 'max':
            self.monitor_op = np.greater
        else:
            if 'accuracy' in self.metric:
                self.monitor_op = np.greater
            else:
                self.monitor_op = np.less

        if self.monitor_op == np.greater:
            self.delta *= 1
        else:
            self.delta *= -1

        self.best_value = np.Inf if self.monitor_op == np.less else -np.Inf

    def begin(self):
        graph = tf.get_default_graph()
        self.metric = graph.as_graph_element(self.metric)
        if isinstance(self.metric, tf.Operation):
            self.metric = self.metric.outputs[0]

    def before_run(self, run_context):
        return session_run_hook.SessionRunArgs(self.metric)

    def after_run(self, run_context, run_values):
        self.current_step += 1
        current_value = run_values.results

        if self.current_step % 200 == 0:
            print(f"Current value: {current_value}, best: {self.best_value}, wait: {self.wait_time}, max wait: {self.max_wait_time}")

        if self.monitor_op(current_value - self.delta, self.best_value):
            self.best_value = current_value
            if self.max_wait_time < self.wait_time:
                self.max_wait_time = self.wait_time
            self.wait_time = 0
        else:
            self.wait_time += 1
            if self.wait_time >= self.patience:
                run_context.request_stop()

I’m using the class in this manner:

early_stopping_hook = EarlyStoppingHook(
    metric='total_loss', 
    patience=2000)

train_spec = tf.estimator.TrainSpec(
    input_fn=train_input_fn, max_steps=train_steps, hooks=[early_stopping_hook])

What confuses me is the term ‘total_loss’. Is that referring to validation loss or training loss? Also, where are the various losses like ‘total_loss’, ‘loss_1’, and ‘loss_2’ defined within the TensorFlow Object Detection API? I’m struggling to find clear information on what these loss names mean.

2

Had this exact problem last year with custom object detection models. Your hook looks good, but there’s a big issue - you’re watching training loss, which always drops during training. Makes early stopping pretty useless.

For the loss names: ‘total_loss’ is the combined training loss from all parts. The individual ones like ‘loss_1’ and ‘loss_2’ are usually classification and localization losses, but depends on your model setup. Check the meta_architecture files in the API to see what’s what.

Here’s what actually works: use train_and_evaluate instead of just TrainSpec. This lets you monitor real validation metrics like mAP or validation loss during training. Way better than relying on training loss for early stopping decisions.

3

The ‘total_loss’ you’re seeing is training loss, not validation loss. In TensorFlow Object Detection API, losses are defined in your pipeline config file’s loss configuration section. ‘Total_loss’ is just the sum of all loss components - classification loss, localization loss, and regularization loss if you’re using it.

Your hook implementation has a problem though. Training loss almost always decreases during training, so it’s not great for early stopping. You’d be better off with a validation evaluation hook instead. The Object Detection API doesn’t compute validation metrics automatically during training, so you’ll need to set up an EvalSpec with your TrainSpec to get useful validation metrics.

As for ‘loss_1’ and ‘loss_2’ - these are different parts of your multi-task loss function, depending on your model. Check the loss function definitions in the API’s models directory under whichever model you’re using.

4

pete’s right - total_loss is training loss. but skip the custom hooks for early stopping with object detection api. use tensorboard callback instead - way easier to implement and you can track both training and eval metrics. just set up eval_spec with your train_spec and it’ll log validation metrics every few hundred steps automatically. saves you from debugging custom hook nightmares.