Rejecting null based on the likelihood ratio test

Question

Let $X = (X_1, ..., X_n)$ have joint density (frequency) function $f \in \{f_{\theta_0}, f_{\theta_1}\}$ and suppose we wish to test $$H_0 : f = f_{\theta_0} \:\text{ vs }\: H_1 : f = f_{\theta_1}.$$
By the Neyman-Pearson lemma, a good choice of a test function is : $\delta(X)=\mathbb{1}_{\{ \Lambda(X)\geq k\}}$ for some $k$ depending on the significance level of the test and $\Lambda$ is the likelihood ratio: $$\Lambda(X)=\frac{f_{\theta_1}(X)}{f_{\theta_0}(X)}.$$ Now my lecture states:
"Basically we reject the null hypothesis if the likelihood of $\theta_0$ is $k$ times higher than the likelihood of $\theta_1$."
But my understanding is: Reject $ H_0$ <=> $\delta=1$ <=> $f_1(X)\geq k\cdot f_0(X)$ which is the opposite.
Are my slides wrong or am I missing something here ?