The　two-dimensional　irregular　packing　problem　is　an　NP-hard　challenge,　notorious　for　requiring　sophisticated　strategies　to　optimize　object　placement　for　maximizing　area　utilization　while　minimizing　waste.　Traditional　heuristic　algorithms　rely　on　complex　calculations　of　placeable　and　non-placeable　areas,　which　can　hamper　efficiency.　This　paper　introduces　an　exploratory　study　using　UNet-based　architectures,　traditionally　successful　in　medical　image　segmentation,　to　approximate　these　calculations　in　packing　scenarios.　We　present　two　adaptations,　NFP-UNet-R18　and　NFP-UNet-R34,　which　incorporate　residual　blocks　to　potentially　refine　the　learning　of　placeable　area　patterns.　Initial　results　demonstrate　that　these　models　can　learn　to　identify　placeable　areas　and　achieve　reasonable　utilization,　albeit　limited　by　the　amount　of　training　data　available.　These　findings　suggest　that　with　further　development　and　more　extensive　training,　UNet-based　methods　could　significantly　enhance　computational　efficiency　in　industrial　applications.