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The structural dynamics of a molecule are determined by the underlying potential energy landscape. Conical intersections are funnels connecting otherwise separate potential energy surfaces. Posited almost a century ago1, conical intersections remain the subject of intense scientific interest2–5. In biology, they have a pivotal role in vision, photosynthesis and DNA stability6. Accurate theoretical methods for examining conical intersections are at present limited to small molecules. Experimental investigations are challenged by the required time resolution and sensitivity. Current structure-dynamical understanding of conical intersections is thus limited to simple molecules with around ten atoms, on timescales of about 100 fs or longer7. Spectroscopy can achieve better time resolutions8, but provides indirect structural information. Here we present few-femtosecond, atomic-resolution videos of photoactive yellow protein, a 2,000-atom protein, passing through a conical intersection. These videos, extracted from experimental data by machine learning, reveal the dynamical trajectories of de-excitation via a conical intersection, yield the key parameters of the conical intersection controlling the de-excitation process and elucidate the topography of the electronic potential energy surfaces involved. Serial femtosecond crystallography (SFX) has provided significant understanding of time-resolved processes of various systems in biology, for example, rhodopsin, which underlies our vision. The approach involves femtosecond-length X-ray pulses directed at protein crystals and has been used to study various photoactive proteins. However, the function of proteins such as rhodopsin requires trans–cis isomerization of a chromophore, which involves crossing of a conical intersection—a funnel separating potential energy surfaces—at timescales faster than what can be achieved experimentally. Here, Ourmazd and colleagues report a machine learning analysis of SFX data of photoactive yellow protein, which resolves the protein passing through a conical intersection, providing information about the potential energy surfaces involved and achieving time resolution of less than 10 fs. This approach offers an opportunity to understand some of the fastest processes in biology by extracting even more information from SFX datasets.