\n",
- " "
+ "Model Builder Diagram:"
]
},
{
@@ -975,20 +985,6 @@
"We can use the following step if we want to detect mussel farms in any given imagery containing only the water body, without running the model builder. In this step, we will generate a feature layer with detected mussel farms using the 'Detect Objects Using Deep Learning' tool available in both [ArcGIS Pro](https://pro.arcgis.com/en/pro-app/latest/tool-reference/image-analyst/detect-objects-using-deep-learning.htm) and [ArcGIS Enterprise](https://developers.arcgis.com/rest/services-reference/enterprise/detect-objects-using-deep-learning.htm)."
]
},
- {
- "cell_type": "markdown",
- "id": "a80d0c09",
- "metadata": {},
- "source": [
- " \n",
- " \n",
- " \n",
- "
\n",
- " Detect Objects Using Deep Learning \n",
- "
\n",
- ""
- ]
- },
{
"cell_type": "markdown",
"id": "0648e82e",
@@ -1138,9 +1134,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python [conda env:conda-dl] *",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
- "name": "conda-env-conda-dl-py"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -1152,7 +1148,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.8"
+ "version": "3.11.0"
}
},
"nbformat": 4,
diff --git a/samples/04_gis_analysts_data_scientists/information-extraction-from-madison-city-crime-incident-reports-using-deep-learning.ipynb b/samples/04_gis_analysts_data_scientists/information-extraction-from-madison-city-crime-incident-reports-using-deep-learning.ipynb
index 70d05db3d..428643450 100644
--- a/samples/04_gis_analysts_data_scientists/information-extraction-from-madison-city-crime-incident-reports-using-deep-learning.ipynb
+++ b/samples/04_gis_analysts_data_scientists/information-extraction-from-madison-city-crime-incident-reports-using-deep-learning.ipynb
@@ -39,7 +39,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Introduction\n",
+ "## Introduction\n",
"\n",
"Crime analysis is an essential part of efficient law enforcement for any city. It involves:\n",
"-\tCollecting data in a form that can be analyzed. \n",
@@ -62,7 +62,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Prerequisites\n",
+ "## Prerequisites\n",
"\n",
"- **Data preparation** and **model training workflows** using `arcgis.learn` is based on [spaCy](https://spacy.io/usage/linguistic-features#named-entities) & [Hugging Face Transformers](https://huggingface.co/transformers/v3.0.2/index.html) libraries. A user can choose an appropriate backbone / library to train his/her model. \n",
"- Refer to the section **\"Install deep learning dependencies of arcgis.learn module\"** [on this page](https://developers.arcgis.com/python/guide/install-and-set-up/#Install-deep-learning-dependencies) for detailed documentation on installation of the dependencies.\n",
@@ -76,7 +76,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Necessary Imports"
+ "## Necessary Imports"
]
},
{
@@ -111,7 +111,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Data preparation\n",
+ "## Data preparation\n",
"\n",
"Data preparation involves splitting the data into training and validation sets, creating the necessary data structures for loading data into the model and so on. The `prepare_data()` function can directly read the training samples in one of the above specified formats and automate the entire process."
]
@@ -396,7 +396,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# EntityRecognizer model\n",
+ "## EntityRecognizer model\n",
"\n",
"`EntityRecognizer` model in `arcgis.learn` can be used with spaCy's [EntityRecognizer](https://spacy.io/api/entityrecognizer) backbone or with [Hugging Face Transformers](https://huggingface.co/transformers/v3.0.2/index.html) backbones\n",
"\n",
@@ -1280,7 +1280,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Model Inference\n",
+ "## Model Inference\n",
"\n",
"Now we can use the trained model to extract entities from new text documents using `extract_entities()` method. This method expects the folder path of where new text document are located, or a list of text documents."
]
@@ -1478,7 +1478,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Publishing the results as a feature layer\n",
+ "## Publishing the results as a feature layer\n",
"\n",
"The code below geocodes the extracted address and publishes the results as a feature layer."
]
@@ -1662,7 +1662,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Create a hot spot map of crime densities\n",
+ "## Create a hot spot map of crime densities\n",
"\n",
"ArcGIS has a set of tools to help us identify, quantify and visualize spatial patterns in our data by identifying areas of statistically significant clusters.\n",
"\n",
@@ -1737,7 +1737,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Conclusion\n",
+ "## Conclusion\n",
"\n",
"This sample demonstrates how `EntityRecognizer()` from `arcgis.learn` can be used for information extraction from crime incident reports, which is an essential requirement for crime analysis. Then, we see how can this information be geocoded and visualized on a map for further analysis."
]
@@ -1746,7 +1746,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# References\n",
+ "## References\n",
"\n",
"[1]: [Police Incident Reports(City of Madison)](https://www.cityofmadison.com/police/newsroom/incidentreports/)\n",
"\n",
@@ -1764,9 +1764,9 @@
"notebookRuntimeVersion": ""
},
"kernelspec": {
- "display_name": "Python [conda env:conda-dl] *",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
- "name": "conda-env-conda-dl-py"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -1778,7 +1778,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.8"
+ "version": "3.11.0"
}
},
"nbformat": 4,
\n",
- "