unet.ipynb

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    "# CoastSeg Image Segmentation Models\n",
    "**Notebook**: SDS_unet_classifier\n",
    "\n",
    "**Purpose**: This notebook contains an interactive dashboard that can be used to load satellite imagery and use any of the available image segmentation models to segment the satellite imagery\n",
    "\n",
    "**How it  Works**: Select the model you want to use, then select settings that will be used to run your model and then click either **Select Data Directory** or **Select Images**  to load satellite imagery for the model to run on."
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    "## Run Image Segmentation Models on the Downloaded Imagery\n",
    "---\n",
    "1. Select if you want to use a GPU or not. By default you won't your computer's GPU\n",
    " - Make sure no matte what model you select you always choose a directory with 'RGB' as the name.\n",
    "2. Select whether your imagery is type **RGB**,**NDWI**,**MNDWI** or **RGB+MNDWI+NDWI**(which is 5 band imagery).\n",
    " - If you choose a **NDWI**, **MNDWI** or **RGB+MNDWI+NDWI** the **RGB** will automatically be converted to these the selected type.\n",
    "3. Select the model you want to use.\n",
    "4. Select **Ensemble** or **Best** (Ensemble is recommended because it uses multiple models to create more accurate predictions).\n",
    "- **Best:** Uses only the best model available on the images.\n",
    " </br> - Using only the best model typically means each prediction takes less time because only a single model is used to make the prediction. However the resulting segmentation may not be as accurate as with the ensemble model.\n",
    "</br>\n",
    "</br>\n",
    "- **Ensemble:** Uses all the models available on the images.\n",
    " </br> - Ensemble modeling uses multiple models to create more accurate predictions, this means it takes longer to make a single predicition but the resulting segmentation will generally be more accurate.\n"
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   "source": [
    "from src.seg2map import log_maker\n",
    "from src.seg2map.models_UI import UI_Models\n",
    "models_ui = UI_Models()\n",
    "models_ui.create_dashboard()"
   ]
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