Selecting the right dataset benchmark for a computer vision project is crucial but challenging due to the lack of industry standards and increasing complexity of datasets. Good benchmarks should reflect real-world applications, while bad ones may be biased towards ideal conditions. Collaboration between organizations in creating diverse and high-standard benchmarks is essential for developing better computer vision machine learning models. Tools like Activeloop can facilitate this collaboration by providing centralized storage and version control for datasets.