When to fine-tune a modelĭeep learning models are constrained by the data used to train them. This process will take less data, compute resources, and training time than training a new model from scratch. In this blog post, we will cover how to fine-tune Esri’s existing pretrained deep learning models to fit to your local geography, imagery, or features of interest. Training a deep learning model from scratchįor a detailed guide on the first workflow, using the pretrained models, see Deep Learning with ArcGIS Pro Tips & Tricks Part 2.Inferencing with existing, pretrained deep learning packages (dlpks).There are three main workflows for using deep learning within ArcGIS: Esri continues to invest resources to make deep learning accessible to all users, expanding deep learning capabilities across the ArcGIS system. Interest in deep learning is growing across many sectors that run the gamut of agriculture, natural resources, defense, and more. If you are unfamiliar with the process of setting up a deep learning environment, sizing the relevant and right GPU for your workflow, and running inferencing with ArcGIS Pro, it is recommended that you catch up with the previous blog posts on deep learning before diving into this guide: Deep Learning with ArcGIS Pro Tips & Tricks: Part 1, Part 2, and Part 3. This fine-tuning process can increase accuracy in detecting objects or making classifications, by tailoring the model to fit your geography and your imagery source characteristics, including the resolution, bit depth, and number of bands. In this post, we will cover how to take Esri’s pretrained deep learning models (available through ArcGIS Living Atlas of the World) and refine them with your own training data. IOM: International Organization for Migration.GeoAI: Geospatial Artificial Intelligence.DLPK: ArcGIS Pro deep learning model package.CUDA: Compute Unified Device Architecture.
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