def create_model():
    """Build the model and look for saved model checkpoints in case we can
    resume from one.
    """
    logger = logging.getLogger(__name__)
    start_iter = 0
    checkpoints = {}
    output_dir = get_output_dir(cfg.TRAIN.DATASETS, training=True)
    weights_file = cfg.TRAIN.WEIGHTS
    if cfg.TRAIN.AUTO_RESUME:
        # Check for the final model (indicates training already finished)
        final_path = os.path.join(output_dir, 'model_final.pkl')
        if os.path.exists(final_path):
            logger.info('model_final.pkl exists; no need to train!')
            return None, None, None, {'final': final_path}, output_dir

        if cfg.TRAIN.COPY_WEIGHTS:
            copyfile(
                weights_file,
                os.path.join(output_dir, os.path.basename(weights_file)))
            logger.info('Copy {} to {}'.format(weights_file, output_dir))

        # Find the most recent checkpoint (highest iteration number)
        files = os.listdir(output_dir)
        for f in files:
            iter_string = re.findall(r'(?<=model_iter)\d+(?=\.pkl)', f)
            if len(iter_string) > 0:
                checkpoint_iter = int(iter_string[0])
                if checkpoint_iter > start_iter:
                    # Start one iteration immediately after the checkpoint iter
                    start_iter = checkpoint_iter + 1
                    resume_weights_file = f

        if start_iter > 0:
            # Override the initialization weights with the found checkpoint
            weights_file = os.path.join(output_dir, resume_weights_file)
            logger.info(
                '========> Resuming from checkpoint {} at start iter {}'.
                format(weights_file, start_iter)
            )

    logger.info('Building model: {}'.format(cfg.MODEL.TYPE))
    model = model_builder.create(cfg.MODEL.TYPE, train=True)
    if cfg.MEMONGER:
        optimize_memory(model)
    # Performs random weight initialization as defined by the model
    workspace.RunNetOnce(model.param_init_net)
    return model, weights_file, start_iter, checkpoints, output_dir
Build the model and look for saved model checkpoints in case we can
    resume from one.
    
detectron/utils/train.py