The ML Menu for Model Selection: Hugging Face, Weights & Biases, and FiftyOne

The article discusses the integration of Weights & Biases (W&B) and FiftyOne for model and dataset tracking, visualization, and evaluation. It highlights how these tools can be used to co-develop high-quality datasets and models by iteratively improving them together. The author demonstrates this process using a food detection task with Hugging Face's DETR model. They cover the steps of curating a custom dataset with FiftyOne, integrating W&B and FiftyOne in the model training loop, visualizing model predictions in FiftyOne, sweeping over hyperparameters with W&B, and finding the best model with both tools. The integration between W&B and FiftyOne allows users to view high-level model evaluation results in W&B and explore corresponding model predictions and evaluations on actual samples in FiftyOne.

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