A [[Python]] package for [[Machine Learning (ML) and AI]] built on top of [[pytorch]]. ## Training a Model ![[2024-11-26_fastai.png]] Each of the five parameters above are crucial. The details are here: www.docs.fast.ai. This creates `dataloaders`, which are iterators that contain trained and predicted data. The `dataloaders` can be used to train various models. There are built-in models as part of `fastai`, or you can integrate with models here: www.timm.fast.ai/model_architectures. The models are pre-trained, then adjusted based on the data you pass in. This is called fine-tuning. - `item_tfms` - transformations applied to the items. For images: - Squish - Crop - Pad - RandomResizedCrop Use your trained model to identify the data that requires cleaning by looking at images with the highest loss. ## Deploying a Model A model can be deployed using [[Hugging Face]] and [[Gradio]] or [[Streamlit]]. # References - www.docs.fast.ai - Additional available models: www.timm.fast.ai/model_architectures - [GitHub - fastai/fastbook: The fastai book, published as Jupyter Notebooks](https://github.com/fastai/fastbook) - Kaggle Notebooks: - [Is it a bird? Creating a model from your own data | Kaggle](https://www.kaggle.com/code/jhoward/is-it-a-bird-creating-a-model-from-your-own-data)