def METHOD_NAME(dets, thresh):
    """Apply classic DPM-style greedy NMS."""
    if dets.shape[0] == 0:
        return dets[[], :]
    scores = dets[:, 1]
    x1 = dets[:, 2]
    y1 = dets[:, 3]
    x2 = dets[:, 4]
    y2 = dets[:, 5]
    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
    order = scores.argsort()[::-1]
    ndets = dets.shape[0]
    suppressed = np.zeros((ndets), dtype=np.int)
    # nominal indices
    # _i, _j
    # sorted indices
    # i, j
    # temp variables for box i's (the box currently under consideration)
    # ix1, iy1, ix2, iy2, iarea
    # variables for computing overlap with box j (lower scoring box)
    # xx1, yy1, xx2, yy2
    # w, h
    # inter, ovr
    for _i in range(ndets):
        i = order[_i]
        if suppressed[i] == 1:
            continue
        ix1 = x1[i]
        iy1 = y1[i]
        ix2 = x2[i]
        iy2 = y2[i]
        iarea = areas[i]
        for _j in range(_i + 1, ndets):
            j = order[_j]
            if suppressed[j] == 1:
                continue
            xx1 = max(ix1, x1[j])
            yy1 = max(iy1, y1[j])
            xx2 = min(ix2, x2[j])
            yy2 = min(iy2, y2[j])
            w = max(0.0, xx2 - xx1 + 1)
            h = max(0.0, yy2 - yy1 + 1)
            inter = w * h
            ovr = inter / (iarea + areas[j] - inter)
            if ovr >= thresh:
                suppressed[j] = 1
    keep = np.where(suppressed == 0)[0]
    dets = dets[keep, :]
    return dets