Reinforcement learning (RL) has recently made several significant advances using video games as a testbed. While many of these games are relatively self-contained, there has been a recent push to develop agents capable of tackling massive, open-ended environments that are more reminiscent of the real world. One of the most popular of these platforms is Minecraft, but to attain human-level performance, agents must be able to learn, plan, and reason using high-dimensional image input. Commonly, an agent will attempt to extract lower-dimensional features that assist with downstream tasks. However, representation learning techniques have primarily been applied to real-world, natural image datasets, and it is unclear how these same methods might translate to an artificial world with non-natural images. We therefore present MiDaS, a novel large-scale Minecraft dataset featuring 36,000 labeled images across 60 classes. MiDaS contains information about both the blocks in the image, critical to solving the game, as well as auxiliary information such as time of day and biome. Further, we perform an evaluation of various models to benchmark performance on this new dataset. Since RL agents must be capable of learning features without labels, we include benchmarks of various self-supervised learning approaches on the dataset. Our results indicate that self-supervised methods perform best in the linear evaluation paradigm, particularly in low-label settings with a ResNet-based backbone, whereas ImageNet-pretraining assists more in the fine-tuning setting. The full dataset is available at https://github.com/MinecraftDataset/MiDaS.