    # assert simulated_bsize == 64, 'must be 64'

    # Pascal 2007 + 2012 trainval has 16551 images
    # Pascal 2007 test has 4952 images
    # In the original YOLO, one batch is 64 images, therefore:
    #
    # ONE EPOCH is 16551 / 64 = 258.609375 = 259 iterations.
    #
    # From the original YOLO VOC v2 config
    # https://github.com/pjreddie/darknet/blob/master/cfg/yolov2-voc.cfg
    #     learning_rate=0.001
    #     burn_in=1000
    #     max_batches = 80200
    #     policy=steps
    #     steps=40000,60000
    #     scales=.1,.1
    #
    # However, the LIGHTNET values are
    #   LR_STEPS = [250, 25000, 35000]
    #
    # The DARNKET STEPS ARE:
    #   DN_STEPS = 1000, 40000, 60000, 80200
    #
    # Based in this, the iter to batch conversion is
    #
    # Key lightnet batch numbers
    # >>> np.array([250, 25000, 30000, 35000, 45000]) / (16512 / 64)
    # array([0.9689,  96.899, 116.2790, 135.658, 174.4186])
    # -> Round
    # array([  1.,  97., 135.])
    # >>> np.array([1000, 40000, 60000, 80200]) / 258
    # array([  3.86683584, 154.67343363, 232.01015044, 310.12023443])
    # -> Round
    # array(4, 157, 232, 310])
    # array([  3.87596899, 155.03875969, 232.55813953, 310.85271318])
