Keras has played a significant role in the development and standardization of new deep learning and machine learning benchmarks, as well as the evaluation of new model architectures and training techniques. Let me explain why.
First, Keras provides a user-friendly, high-level API for building and training neural networks. This makes it easy for researchers to experiment with new network architectures and training techniques, allowing them to quickly prototype and evaluate ideas.
Second, Keras has become a de facto industry standard for deep learning. The framework has been widely adopted by both researchers and practitioners, making it easier to compare and reproduce results across different studies and applications.
Third, Keras provides a wide range of pre-trained models and tools for transfer learning, which can be used to benchmark new model architectures and training techniques against existing state-of-the-art approaches.
Fourth, Keras has a growing community of developers and users, who are actively contributing new models, datasets, and evaluation metrics. This community-driven approach helps to ensure that benchmarks are up-to-date and relevant, and that the framework remains at the forefront of deep learning research and development.
Overall, Keras has played a pivotal role in both the development and standardization of new deep learning and machine learning benchmarks, as well as the evaluation of new model architectures and training techniques. Its ease-of-use, popularity, and community-driven approach have made it an invaluable tool for researchers and practitioners alike.