Kaggle Models

Models is the newest section introduced on the platform; at the time of writing this book, it is less than one month old. Models started to be contributed quite often by users in several ways and for a few purposes. Most frequently, models were saved as output of Notebooks (Code) after being trained using custom code, often in the context of a competition. Subsequently, these models can be optionally included in a dataset or used directly in code. Also, sometimes, models built outside the platform were uploaded as datasets and then included in the pipeline of users to prepare a solution for a competition. Meantime, model repositories were available either through a public cloud, like Google Cloud, AWS, or Azure, or from a company specialized in such a service, like Hugging Face.

With the concept of downloadable models ready to be used or easy to fine-tune for a custom task, Kaggle chose to include Models in this platform. Currently, you can search in several categories: Text Classification, Image Feature Vector, Object Detection, and Image Segmentation. Alternatively, you can use the Model Finder feature to explore models specialized in a certain modality: Image, Text, Audio, Multimodal, or Video. When searching the Models library, you can apply filters on Task, Data Type, Framework, Language, License, and Size, as well as functional criteria, like Fine Tuneable.

There are no ranking points or performance tiers related to models yet. Models can be upvoted and there is a Code and Discussions section associated with each model. In the future, it is possible that we will see evolution here as well and have models with ranking points as well as performance tiers if they make it possible to contribute models and get recognition for this. Currently, models are contributed by Google only.

We might see the Models feature evolving immensely in the near future, providing the community with a flexible and powerful tool for the creation of modular and scalable solutions to train and add inference to machine learning pipelines on the Kaggle platform.