We determine the computational complexity of approximately counting and sampling independent sets of a given size in bounded-degree graphs. That is, we identify a critical density $α_c(Δ)$ and provide (i) for $α< α_c(Δ)$ randomized polynomial-time algorithms for approximately sampling and counting independent sets of given size at most $αn$ in $n$-vertex graphs of maximum degree $Δ$; and (ii) a proof that unless NP=RP, no such algorithms exist for $α>α_c(Δ)$. The critical density is the occupancy fraction of the hard core model on the complete graph $K_{Δ+1}$ at the uniqueness threshold on the infinite $Δ$-regular tree, giving $α_c(Δ)\sim\frac{e}{1+e}\frac{1}Δ$ as $Δ\to\infty$. Our methods apply more generally to anti-ferromagnetic 2-spin systems and motivate new questions in extremal combinatorics.